Journal articles: 'Glaucoma Optic nerve Machine learning'

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Barella, Kleyton Arlindo, Vital Paulino Costa, Vanessa Gonçalves Vidotti, Fabrício Reis Silva, Marcelo Dias, and Edson Satoshi Gomi. "Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT." Journal of Ophthalmology 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/789129.

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Purpose. To investigate the diagnostic accuracy of machine learning classifiers (MLCs) using retinal nerve fiber layer (RNFL) and optic nerve (ON) parameters obtained with spectral domain optical coherence tomography (SD-OCT).Methods. Fifty-seven patients with early to moderate primary open angle glaucoma and 46 healthy patients were recruited. All 103 patients underwent a complete ophthalmological examination, achromatic standard automated perimetry, and imaging with SD-OCT. Receiver operating characteristic (ROC) curves were built for RNFL and ON parameters. Ten MLCs were tested. Areas under ROC curves (aROCs) obtained for each SD-OCT parameter and MLC were compared.Results. The mean age was56.5±8.9years for healthy individuals and59.9±9.0years for glaucoma patients (P=0.054). Mean deviation values were −1.4 dB for healthy individuals and −4.0 dB for glaucoma patients (P<0.001). SD-OCT parameters with the greatest aROCs were cup/disc area ratio (0.846) and average cup/disc (0.843). aROCs obtained with classifiers varied from 0.687 (CTREE) to 0.877 (RAN). The aROC obtained with RAN (0.877) was not significantly different from the aROC obtained with the best single SD-OCT parameter (0.846) (P=0.542).Conclusion. MLCs showed good accuracy but did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.

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Sułot, Dominika, David Alonso-Caneiro, Paweł Ksieniewicz, Patrycja Krzyzanowska-Berkowska, and D. Robert Iskander. "Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method." PLOS ONE 16, no. 6 (June 4, 2021): e0252339. http://dx.doi.org/10.1371/journal.pone.0252339.

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This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in a low data regime to differentiate glaucoma patients from healthy controls. The two groups of subjects were initially categorized based on a range of clinical tests including measurements of intraocular pressure, visual fields, oct derived retinal nerve fiber layer (rnfl) thickness and dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used. A new task-specific convolutional neural network architecture was developed for slo image-based classification. To benchmark the results of the proposed method, a range of classifiers were tested including five machine learning methods to classify glaucoma based on rnfl thickness—a well-known biomarker in glaucoma diagnostics, ensemble classifier based on inception v3 architecture, and classifiers based on features extracted from the image. The study shows that cross-validation dl ensemble based on slo images achieved a good discrimination performance with up to 0.962 of balanced accuracy, outperforming all of the other tested classifiers.

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An, Guangzhou, Kazuko Omodaka, Kazuki Hashimoto, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, Tsutomu Kikawa, Hideo Yokota, Masahiro Akiba, and Toru Nakazawa. "Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images." Journal of Healthcare Engineering 2019 (February 18, 2019): 1–9. http://dx.doi.org/10.1155/2019/4061313.

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This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

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Abidi, Syed S. R., Patrice C. Roy, Muhammad S. Shah, Jin Yu, and Sanjun Yan. "A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods." Journal of Healthcare Informatics Research 2, no. 4 (June 20, 2018): 370–401. http://dx.doi.org/10.1007/s41666-018-0028-7.

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Omar, Yasser, Mohamed Abd-ElSalam ElSheikh, and Rania Hodhod. "GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning." Health Informatics Journal 27, no. 2 (April 2021): 146045822110092. http://dx.doi.org/10.1177/14604582211009276.

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Glaucoma is a serious eye disease characterized by dysfunction and loss of retinal ganglion cells (RGCs) which can eventually lead to loss of vision. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods which, unfortunately, are expensive methods and hence, a novel automated glaucoma diagnosis system is needed. This paper proposes a new model for mass screening that aims to decrease the false negative rate (FNR). The model is based on applying nine different machine learning techniques in a majority voting model. The top five techniques that provide the highest accuracy will be used to build a consensus ensemble to make the final decision. The results from applying both models on a dataset with 499 records show a decrease in the accuracy rate from 90% to 83% and a decrease in false negative rate (FNR) from 8% to 0% for majority voting and consensus model, respectively. These results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening.

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An, Guangzhou, Kazuko Omodaka, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, Tsutomu Kikawa, Toru Nakazawa, Hideo Yokota, and Masahiro Akiba. "Comparison of Machine-Learning Classification Models for Glaucoma Management." Journal of Healthcare Engineering 2018 (June 19, 2018): 1–8. http://dx.doi.org/10.1155/2018/6874765.

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This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients’ background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.

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Diwakaran and S. Sheeba Jeya Sophia. "Survey on Automatic Detection of Glaucoma through Deep Learning Using Retinal Fundus Images." Journal of Biomedical Engineering and Medical Imaging 7, no. 4 (August 1, 2020): 11–15. http://dx.doi.org/10.14738/jbemi.74.8055.

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Glaucoma - a disease which causes damage to our eye's optic nerve and subsequently blinds the vision. This occurs due to increased intraocular pressure (IOP) which causes the damage of optic nerve axons at the back of the eye, with eventual deterioration of vision. Presently, many works have been done towards automatic glaucoma detection using Fundus Images (FI) by extracting structural features. Structural features can be extracted from optic nerve head (ONH) analysis, cup to disc ratio (CDR) and Inferior, Superior, Nasal, Temporal (ISNT) rule in Fundus Image for glaucoma assessment.This survey presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under non-invasive methods of detection. Here, a review and study were conducted for the different techniques of glaucoma detection using retinal fundus images. The objective of this survey is to obtain a technique which automatically analyze the retinal images of the eye with high efficiency and accuracy

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Sreng, Syna, Noppadol Maneerat, Kazuhiko Hamamoto, and Khin Yadanar Win. "Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images." Applied Sciences 10, no. 14 (July 17, 2020): 4916. http://dx.doi.org/10.3390/app10144916.

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Glaucoma is a major global cause of blindness. As the symptoms of glaucoma appear, when the disease reaches an advanced stage, proper screening of glaucoma in the early stages is challenging. Therefore, regular glaucoma screening is essential and recommended. However, eye screening is currently subjective, time-consuming and labor-intensive and there are insufficient eye specialists available. We present an automatic two-stage glaucoma screening system to reduce the workload of ophthalmologists. The system first segmented the optic disc region using a DeepLabv3+ architecture but substituted the encoder module with multiple deep convolutional neural networks. For the classification stage, we used pretrained deep convolutional neural networks for three proposals (1) transfer learning and (2) learning the feature descriptors using support vector machine and (3) building ensemble of methods in (1) and (2). We evaluated our methods on five available datasets containing 2787 retinal images and found that the best option for optic disc segmentation is a combination of DeepLabv3+ and MobileNet. For glaucoma classification, an ensemble of methods performed better than the conventional methods for RIM-ONE, ORIGA, DRISHTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90.00%, 86.84% and 99.53% and Area Under Curve (AUC) of 100%, 92.06%, 91.67% and 99.98%, respectively, and performed comparably with CUHKMED, the top team in REFUGE challenge, using REFUGE dataset with an accuracy of 95.59% and AUC of 95.10%.

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SHARMA, RAHUL, PRADIP SIRCAR, R. B. PACHORI, SULATHA V. BHANDARY, and U. RAJENDRA ACHARYA. "AUTOMATED GLAUCOMA DETECTION USING CENTER SLICE OF HIGHER ORDER STATISTICS." Journal of Mechanics in Medicine and Biology 19, no. 01 (February 2019): 1940011. http://dx.doi.org/10.1142/s0219519419400116.

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Glaucoma is one of the leading causes of blindness. The raised intraocular pressure is one of the important modifiable risk factor causing glaucomatous optic nerve damage. Glaucomatous optic nerve damage is seen as increase in the cupping of the optic disc and loss of neuroretinal rim. An automated detection system using nonlinear higher order statistics (HOS) based method is used to capture the detailed information present in the fundus image efficiently. The center slice of bispectrum and bicepstrum are applied on fundus images. Various features are extracted from the diagonal of these central slices. In order to reduce the number of features the locality sensitive discriminant analysis (LSDA) data reduction technique method is implemented. The ranked LSDA features are fed to support vector machine (SVM) classifier with various kernels for automated glaucoma detection. The simulation is performed on two databases. The proposed algorithm has yielded classification accuracy of 98.8% and 95% using entire private and public databases, respectively. The proposed technique achieved the highest classification accuracy, hence, confirm the diagnosis of ophthalmologists and can be employed in the community health care centers and hospitals.

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Xiao, Zhang, Geng, Zhang, Wu, and Liu. "Research on the Method of Color Fundus Image Optic Cup Segmentation Based on Deep Learning." Symmetry 11, no. 7 (July 17, 2019): 933. http://dx.doi.org/10.3390/sym11070933.

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The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as glaucoma or congenital myopia. The occurrence of glaucoma is generally accompanied by physical changes to the optic cup, optic disc, and optic nerve fiber layer. Therefore, accurate measurement of the optic cup is important for the detection of glaucoma. The accurate segmentation of the optic cup is essential for the measurement of the size of the optic cup relative to other structures in the eye. This paper proposes a new network architecture we call Segmentation-ResNet Seg-ResNet that takes a residual network structure as the main body, introduces a channel weighting structure that automatically adjusts the dependence of the feature channels, re-calibrates the feature channels, and introduces a set of low-level features that are combined with high-level features to improve network performance. Pre-fusion features and fused features are symmetrical. Hence, this work correlates with the concept of symmetry. Combined with the training strategy of migration learning, the segmentation accuracy is improved while speeding up network convergence. The robustness and effectiveness of the proposed method are demonstrated by testing data from the GlaucomaRepo and Drishti-GS fundus image databases.

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Escamez, Carlos Salvador Fernandez, Susana Perucho Martinez, and Nicolas Toledano Fernandez. "High interpretable machine learning classifier for early glaucoma diagnosis." International Journal of Ophthalmology 14, no. 3 (March 18, 2021): 393–98. http://dx.doi.org/10.18240/ijo.2021.03.10.

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AIM: To develop a classifier for differentiating between healthy and early stage glaucoma eyes based on peripapillary retinal nerve fiber layer (RNFL) thicknesses measured with optical coherence tomography (OCT), using machine learning algorithms with a high interpretability. METHODS: Ninety patients with early glaucoma and 85 healthy eyes were included. Early glaucoma eyes showed a visual field (VF) defect with mean deviation >-6.00 dB and characteristic glaucomatous morphology. RNFL thickness in every quadrant, clock-hour and average thickness were used to feed machine learning algorithms. Cluster analysis was conducted to detect and exclude outliers. Tree gradient boosting algorithms were used to calculate the importance of parameters on the classifier and to check the relation between their values and its impact on the classifier. Parameters with the lowest importance were excluded and a weighted decision tree analysis was applied to obtain an interpretable classifier. Area under the ROC curve (AUC), accuracy and generalization ability of the model were estimated using cross validation techniques. RESULTS: Average and 7 clock-hour RNFL thicknesses were the parameters with the highest importance. Correlation between parameter values and impact on classification displayed a stepped pattern for average thickness. Decision tree model revealed that average thickness lower than 82 µm was a high predictor for early glaucoma. Model scores had AUC of 0.953 (95%CI: 0.903- 0998), with an accuracy of 89%. CONCLUSION: Gradient boosting methods provide accurate and highly interpretable classifiers to discriminate between early glaucoma and healthy eyes. Average and 7-hour RNFL thicknesses have the best discriminant power.

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Wang, Peiyu, Jian Shen, Ryuna Chang, Maemae Moloney, Mina Torres, Bruce Burkemper, Xuejuan Jiang, Damien Rodger, Rohit Varma, and Grace M. Richter. "Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps." Ophthalmology Glaucoma 2, no. 6 (November 2019): 422–28. http://dx.doi.org/10.1016/j.ogla.2019.08.004.

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Oh, Sejong, Yuli Park, Kyong Jin Cho, and Seong Jae Kim. "Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation." Diagnostics 11, no. 3 (March 13, 2021): 510. http://dx.doi.org/10.3390/diagnostics11030510.

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The aim is to develop a machine learning prediction model for the diagnosis of glaucoma and an explanation system for a specific prediction. Clinical data of the patients based on a visual field test, a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, a general examination including an intraocular pressure (IOP) measurement, and fundus photography were provided for the feature selection process. Five selected features (variables) were used to develop a machine learning prediction model. The support vector machine, C5.0, random forest, and XGboost algorithms were tested for the prediction model. The performance of the prediction models was tested with 10-fold cross-validation. Statistical charts, such as gauge, radar, and Shapley Additive Explanations (SHAP), were used to explain the prediction case. All four models achieved similarly high diagnostic performance, with accuracy values ranging from 0.903 to 0.947. The XGboost model is the best model with an accuracy of 0.947, sensitivity of 0.941, specificity of 0.950, and AUC of 0.945. Three statistical charts were established to explain the prediction based on the characteristics of the XGboost model. Higher diagnostic performance was achieved with the XGboost model. These three statistical charts can help us understand why the machine learning model produces a specific prediction result. This may be the first attempt to apply “explainable artificial intelligence” to eye disease diagnosis.

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Romeni, Simone, Davide Zoccolan, and Silvestro Micera. "A machine learning framework to optimize optic nerve electrical stimulation for vision restoration." Patterns 2, no. 7 (July 2021): 100286. http://dx.doi.org/10.1016/j.patter.2021.100286.

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Wu, Chao-Wei, Hsiang-Li Shen, Chi-Jie Lu, Ssu-Han Chen, and Hsin-Yi Chen. "Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT." Diagnostics 11, no. 9 (September 19, 2021): 1718. http://dx.doi.org/10.3390/diagnostics11091718.

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Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

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Omodaka, Kazuko, Guangzhou An, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, Tsutomu Kikawa, Hidetoshi Takahashi, Hideo Yokota, Masahiro Akiba, and Toru Nakazawa. "Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters." PLOS ONE 12, no. 12 (December 19, 2017): e0190012. http://dx.doi.org/10.1371/journal.pone.0190012.

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Kim, Mijung, Jong Chul Han, Seung Hyup Hyun, Olivier Janssens, Sofie Van Hoecke, Changwon Kee, and Wesley De Neve. "Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †." Applied Sciences 9, no. 15 (July 29, 2019): 3064. http://dx.doi.org/10.3390/app9153064.

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Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end.

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Schwaner, Stephen A., Andrew J. Feola, and C. Ross Ethier. "Factors affecting optic nerve head biomechanics in a rat model of glaucoma." Journal of The Royal Society Interface 17, no. 165 (April 2020): 20190695. http://dx.doi.org/10.1098/rsif.2019.0695.

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Glaucoma is the leading cause of irreversible blindness and is characterized by the death of retinal ganglion cells, which carry vision information from the retina to the brain. Although it is well accepted that biomechanics is an important part of the glaucomatous disease process, the mechanisms by which biomechanical insult, usually due to elevated intraocular pressure (IOP), leads to retinal ganglion cell death are not understood. Rat models of glaucoma afford an opportunity for learning more about these mechanisms, but the biomechanics of the rat optic nerve head (ONH), a primary region of damage in glaucoma, are only just beginning to be characterized. In a previous study, we built finite-element models with individual-specific rat ONH geometries. Here, we developed a parametrized model of the rat ONH and used it to perform a sensitivity study to determine the influence that six geometric parameters and 13 tissue material properties have on rat optic nerve biomechanical strains due to IOP elevation. Strain magnitudes and patterns in the parametrized model generally matched those from individual-specific models, suggesting that the parametrized model sufficiently approximated rat ONH anatomy. Similar to previous studies in human eyes, we found that scleral properties were highly influential: the six parameters with highest influence on optic nerve strains were optic nerve stiffness, IOP, scleral thickness, the degree of alignment of scleral collagen fibres, scleral ground substance stiffness and the scleral collagen fibre uncrimping coefficient. We conclude that a parametrized modelling strategy is an efficient approach that allows insight into rat ONH biomechanics. Further, scleral properties are important influences on rat ONH biomechanics, and additional efforts should be made to better characterize rat scleral collagen fibre organization.

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Antaki, Fares, Razek Georges Coussa, Karim Hammamji, and Renaud Duval. "Revisiting the Problem of Optic Nerve Detection in a Retinal Image Using Automated Machine Learning." Asia-Pacific Journal of Ophthalmology 10, no. 3 (May 2021): 335–36. http://dx.doi.org/10.1097/apo.0000000000000398.

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Alipanahi, Babak, Farhad Hormozdiari, Babak Behsaz, Justin Cosentino, Zachary R. McCaw, Emanuel Schorsch, D. Sculley, et al. "Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology." American Journal of Human Genetics 108, no. 7 (July 2021): 1217–30. http://dx.doi.org/10.1016/j.ajhg.2021.05.004.

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Wang, Hui Bin, Yu Rong Wu, Jie Shen, and Zhe Chen. "Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve." Applied Mechanics and Materials 347-350 (August 2013): 2178–84. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2178.

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Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

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Fumero Batista, Francisco José, Tinguaro Diaz-Aleman, Jose Sigut, Silvia Alayon, Rafael Arnay, and Denisse Angel-Pereira. "RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning." Image Analysis & Stereology 39, no. 3 (November 25, 2020): 161–67. http://dx.doi.org/10.5566/ias.2346.

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The first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference images for segmenting the optic disc, in recent years we have observed that its use has been more oriented toward training and testing deep learning models. The recent REFUGE challenge laid out some criteria that a set of images of these characteristics must satisfy to be used as a standard reference for validating deep learning methods that rely on the use of these data. This, combined with the certain confusion and even improper use observed in some cases of the three versions published, led us to consider revising and combining them into a new, publicly available version called RIM-ONE DL (RIM-ONE for Deep Learning). This paper describes this set of images, consisting of 313 retinographies from normal subjects and 172 retinographies from patients with glaucoma. All of these images have been assessed by two experts and include a manual segmentation of the disc and cup. It also describes an evaluation benchmark with different models of well-known convolutional neural networks.

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Odaibo, Stephen G. "Re: Wang et al.: Machine learning models for diagnosing glaucoma from retinal nerve fiber layer thickness maps (Ophthalmology Glaucoma. 2019;2:422–428)." Ophthalmology Glaucoma 3, no. 3 (May 2020): e3. http://dx.doi.org/10.1016/j.ogla.2020.03.002.

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Kirar, Bhupendra, and Dheeraj Agrawal. "Glaucoma diagnosis using discrete wavelet transform and histogram features from fundus images." International Journal of Engineering & Technology 7, no. 4 (September 25, 2018): 2546. http://dx.doi.org/10.14419/ijet.v7i4.14809.

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Glaucoma is one of the main eye diseases; it cause progressive deterioration of optic nerve fibers due to increased fluid pressure. The existing methods of glaucoma diagnosis are time consuming, expensive and require practiced clinicians to understand the eye problems. Hence fast, cheap and more accurate glaucoma diagnosis methods are needed. This paper presents an innovative idea for diagnosis of glaucoma using third level two dimensional discrete wavelet transform (2D DWT) and histogram features from fundus images. The 2D DWT is used to decompose the glaucoma and healthy images and histogram features are extracted from 2D DWT decomposed sub band images. The least square support vector machine (LS-SVM) is used as a classifier which classifies the glaucoma and healthy images using the extracted features. The proposed method yielded classification accuracy of 88.33%, 87.50%, and 86.67% for ten, eight and fivefold cross validation respectively. The obtained classification accuracy, sensitivity and specificity are 88.33%, 90.00%, and 85.00% for tenfold cross validation respectively. Obtained results prove that the performance of the proposed method is better compared to the existing methods. It may considerably increases the diagnosis speed of ophthalmologists.

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Soh, Zhi Da, Mihir Deshmukh, Tyler Hyungtaek Rim, and Ching-Yu Cheng. "Response to: Revisiting the Problem of Optic Nerve Detection in a Retinal Image Using Automated Machine Learning." Asia-Pacific Journal of Ophthalmology 10, no. 3 (May 2021): 337. http://dx.doi.org/10.1097/01.apo.0000769904.75814.b5.

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Zangwill, Linda M., Kwokleung Chan, Christopher Bowd, Jicuang Hao, Te-Won Lee, Robert N. Weinreb, Terrence J. Sejnowski, and Michael H. Goldbaum. "Heidelberg Retina Tomograph Measurements of the Optic Disc and Parapapillary Retina for Detecting Glaucoma Analyzed by Machine Learning Classifiers." Investigative Opthalmology & Visual Science 45, no. 9 (September 1, 2004): 3144. http://dx.doi.org/10.1167/iovs.04-0202.

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Gonzalez-Hernandez, Marta, Daniel Gonzalez-Hernandez, Daniel Perez-Barbudo, Paloma Rodriguez-Esteve, Nisamar Betancor-Caro, and Manuel Gonzalez de la Rosa. "Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma." Journal of Clinical Medicine 10, no. 15 (July 22, 2021): 3231. http://dx.doi.org/10.3390/jcm10153231.

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Background: Laguna-ONhE is an application for the colorimetric analysis of optic nerve images, which topographically assesses the cup and the presence of haemoglobin. Its latest version has been fully automated with five deep learning models. In this paper, perimetry in combination with Laguna-ONhE or Cirrus-OCT was evaluated. Methods: The morphology and perfusion estimated by Laguna ONhE were compiled into a “Globin Distribution Function” (GDF). Visual field irregularity was measured with the usual pattern standard deviation (PSD) and the threshold coefficient of variation (TCV), which analyses its harmony without taking into account age-corrected values. In total, 477 normal eyes, 235 confirmed, and 98 suspected glaucoma cases were examined with Cirrus-OCT and different fundus cameras and perimeters. Results: The best Receiver Operating Characteristic (ROC) analysis results for confirmed and suspected glaucoma were obtained with the combination of GDF and TCV (AUC: 0.995 and 0.935, respectively. Sensitivities: 94.5% and 45.9%, respectively, for 99% specificity). The best combination of OCT and perimetry was obtained with the vertical cup/disc ratio and PSD (AUC: 0.988 and 0.847, respectively. Sensitivities: 84.7% and 18.4%, respectively, for 99% specificity). Conclusion: Using Laguna ONhE, morphology, perfusion, and function can be mutually enhanced with the methods described for the purpose of glaucoma assessment, providing early sensitivity.

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Christopher, Mark, Akram Belghith, Robert N. Weinreb, Christopher Bowd, Michael H. Goldbaum, Luke J. Saunders, Felipe A. Medeiros, and Linda M. Zangwill. "Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression." Investigative Opthalmology & Visual Science 59, no. 7 (June 1, 2018): 2748. http://dx.doi.org/10.1167/iovs.17-23387.

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Bizios, Dimitrios, Anders Heijl, Jesper Leth Hougaard, and Boel Bengtsson. "Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT." Acta Ophthalmologica 88, no. 1 (February 2010): 44–52. http://dx.doi.org/10.1111/j.1755-3768.2009.01784.x.

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Elze, Tobias, Louis R. Pasquale, Lucy Q. Shen, Teresa C. Chen, Janey L. Wiggs, and Peter J. Bex. "Patterns of functional vision loss in glaucoma determined with archetypal analysis." Journal of The Royal Society Interface 12, no. 103 (February 2015): 20141118. http://dx.doi.org/10.1098/rsif.2014.1118.

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Glaucoma is an optic neuropathy accompanied by vision loss which can be mapped by visual field (VF) testing revealing characteristic patterns related to the retinal nerve fibre layer anatomy. While detailed knowledge about these patterns is important to understand the anatomic and genetic aspects of glaucoma, current classification schemes are typically predominantly derived qualitatively. Here, we classify glaucomatous vision loss quantitatively by statistically learning prototypical patterns on the convex hull of the data space. In contrast to component-based approaches, this method emphasizes distinct aspects of the data and provides patterns that are easier to interpret for clinicians. Based on 13 231 reliable Humphrey VFs from a large clinical glaucoma practice, we identify an optimal solution with 17 glaucomatous vision loss prototypes which fit well with previously described qualitative patterns from a large clinical study. We illustrate relations of our patterns to retinal structure by a previously developed mathematical model. In contrast to the qualitative clinical approaches, our results can serve as a framework to quantify the various subtypes of glaucomatous visual field loss.

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Gaillet, Vivien, Eleonora Borda, Elodie Geneviève Zollinger, and Diego Ghezzi. "A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve." Journal of Neural Engineering 18, no. 4 (April 26, 2021): 046031. http://dx.doi.org/10.1088/1741-2552/abf523.

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Bhuiyan, Alauddin, Arun Govindaiah, and R. Theodore Smith. "An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging." Journal of Ophthalmology 2021 (May 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/6694784.

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Background and Objective. Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial-intelligence-based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting, and Participants. 1546 disc-centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age-Related Eye Disease Study and Singapore Malay Eye Study to develop the system. First, a proprietary semiautomated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep-learning architectures were used to train and test a binary classifier system. Measurements. The binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results. The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). Conclusions. Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.

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Kasaragod, Deepa, Shuichi Makita, Young-Joo Hong, and Yoshiaki Yasuno. "Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation." Biomedical Optics Express 9, no. 7 (June 21, 2018): 3220. http://dx.doi.org/10.1364/boe.9.003220.

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Hitzl, W., H. A. Reitsamer, K. Hornykewycz, A. Mistlberger, and G. Grabner. "Application of Discriminant, Classification Tree and Neural Network Analysis to Differentiate between Potential Glaucoma Suspects with and without Visual Field Defects." Journal of Theoretical Medicine 5, no. 3-4 (2003): 161–70. http://dx.doi.org/10.1080/10273360410001728011.

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Purpose: This study has two objectives. The first one is to investigate the question whether it is possible to discriminate between eyes with and without a glaucomateous visual field defect based on standard ophthalmologic examinations as well as optic nerve head topographic parameters. The second objective raises the question of the ability of several suggested statistical models to generalize their results to new, previously unseen patients.Methods: To investigate the above addressed question: (a) independent, two-sidedt-tests, (b) a linear discriminant analysis with a forward stepwise variable selection algorithm, (c) four classification tree analyses and (d) three different neural network models with a forward, backward and a genetic variable selection algorithm were applied to 1020 subjects with a normal visual field and 110 subjects with a glaucomateous visual field defect. The Humphrey Visual Field Analyzer was used to test the visual fields and the TopSS®Scanning Laser Tomograph measured the optic nerve topography. A 10-fold cross-validation method was used for the models (b), (c) and (d) to compute approximative 95% confidence intervals for the specificity and sensitivity rates.A literature study of 18 studies dealt with the question whether/how the generalization error was controlled (control of sample bias, cross-validation procedures, training net size for valid generalization). It was also looked up whether point estimators or 95% confidence intervals were used to report specificity and sensitivity rates.Results: (a) Only few differences of the means could be found between both groups, including age, existing eye diseases, best corrected visual acuity and visual field parameters. The linear discriminant analysis (b), the classification tree analyses (c) and the neural networks (d) ended up with high specificity rates, but low sensitivity rates.The literature study showed that three models did not apply a cross-validation procedure to report their results. Two models used a test sample cross-validation and thirteen used a v-fold cross-validation method. Although most authors reported confidence intervals for the area under the ROC, no author reported confidence intervals for the true, but unknown sensitivity and specificity rates.Conclusions: (a) The results of this study suggest that the combination of standard ophthalmologic eye parameters and optic nerve head topographic parameters of the TopSS®instrument are not sufficient to discriminate properly among normal eyes and eyes with a glaucomateous visual field defect. (b) We follow important suggestions given in statistical learning theory for proper generalization and suggest to apply these methods to the given models or to models in future. At least three conditions should be met: (1) confidence intervals instead of point estimators to assess the classification performance of a model (control of test sample bias); (2) sensitivity and specificity rates should be estimated instead of reporting a point estimator for the area under the ROC and (3) the generalization error should be reported both in a training and a test sample and methods should be applied to select an appropriate training sample size for valid generalization.

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Cavaliere, Carlo, Elisa Vilades, Mª Alonso-Rodríguez, María Rodrigo, Luis Pablo, Juan Miguel, Elena López-Guillén, Eva Morla, Luciano Boquete, and Elena Garcia-Martin. "Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features." Sensors 19, no. 23 (December 3, 2019): 5323. http://dx.doi.org/10.3390/s19235323.

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The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer—GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew’s correlation coefficient (MCC) and the AUCCLASSIFIER. The most discriminant variables were found to be the total GCL++ thickness (between inner limiting membrane to inner nuclear layer boundaries), evaluated in the peripapillary area and macular retina thickness in the nasal quadrant of the outer and inner rings. Using the SVM classifier, we obtained the following values: MCC = 0.81, sensitivity = 0.89, specificity = 0.92, accuracy = 0.91, and AUCCLASSIFIER = 0.97. Our findings suggest that it is possible to classify control subjects and MS patients without previous optic neuritis by applying machine-learning techniques to study the structural neurodegeneration in the retina.

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Li, Shuo, Chiru Ge, Xiaodan Sui, Yuanjie Zheng, and Weikuan Jia. "Channel and Spatial Attention Regression Network for Cup-to-Disc Ratio Estimation." Electronics 9, no. 6 (May 29, 2020): 909. http://dx.doi.org/10.3390/electronics9060909.

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Cup-to-disc ratio (CDR) is of great importance during assessing structural changes at the optic nerve head (ONH) and diagnosis of glaucoma. While most efforts have been put on acquiring the CDR number through CNN-based segmentation algorithms followed by the calculation of CDR, these methods usually only focus on the features in the convolution kernel, which is, after all, the operation of the local region, ignoring the contribution of rich global features (such as distant pixels) to the current features. In this paper, a new end-to-end channel and spatial attention regression deep learning network is proposed to deduces CDR number from the regression perspective and combine the self-attention mechanism with the regression network. Our network consists of four modules: the feature extraction module to extract deep features expressing the complicated pattern of optic disc (OD) and optic cup (OC), the attention module including the channel attention block (CAB) and the spatial attention block (SAB) to improve feature representation by aggregating long-range contextual information, the regression module to deduce CDR number directly, and the segmentation-auxiliary module to focus the model’s attention on the relevant features instead of the background region. Especially, the CAB selects relatively important feature maps in channel dimension, shifting the emphasis on the OD and OC region; meanwhile, the SAB learns the discriminative ability of feature representation at pixel level by capturing the relationship of intra-feature map. The experimental results of ORIGA dataset show that our method obtains absolute CDR error of 0.067 and the Pearson’s correlation coefficient of 0.694 in estimating CDR and our method has a great potential in predicting the CDR number.

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Sarhan, Abdullah, Andrew Swift, Adam Gorner, Jon Rokne, Reda Alhajj, Gavin Docherty, and Andrew Crichton. "Utilizing a responsive web portal for studying disc tracing agreement in retinal images." PLOS ONE 16, no. 5 (May 25, 2021): e0251703. http://dx.doi.org/10.1371/journal.pone.0251703.

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Glaucoma is a leading cause of blindness worldwide whose detection is based on multiple factors, including measuring the cup to disc ratio, retinal nerve fiber layer and visual field defects. Advances in image processing and machine learning have allowed the development of automated approached for segmenting objects from fundus images. However, to build a robust system, a reliable ground truth dataset is required for proper training and validation of the model. In this study, we investigate the level of agreement in properly detecting the retinal disc in fundus images using an online portal built for such purposes. Two Doctors of Optometry independently traced the discs for 159 fundus images obtained from publicly available datasets using a purpose-built online portal. Additionally, we studied the effectiveness of ellipse fitting in handling misalignments in tracing. We measured tracing precision, interobserver variability, and average boundary distance between the results provided by ophthalmologists, and optometrist tracing. We also studied whether ellipse fitting has a positive or negative impact on properly detecting disc boundaries. The overall agreement between the optometrists in terms of locating the disc region in these images was 0.87. However, we found that there was a fair agreement on the disc border with kappa = 0.21. Disagreements were mainly in fundus images obtained from glaucomatous patients. The resulting dataset was deemed to be an acceptable ground truth dataset for training a validation of models for automatic detection of objects in fundus images.

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Prakash, K., and M. Sudharsan. "A Mathematical Study of Glaucoma using Machine Learning Algorithms for Retina." International Journal of Advanced Research in Science, Communication and Technology, February 15, 2021, 31–33. http://dx.doi.org/10.48175/ijarsct-v2-i3-305.

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Glaucoma is a category of visual disorders represented by optic nerve neuropathy, a means of gradually declining optic nerve neuropathy. In ground vision, resulting in sight loss. In this article, a novel retinal therapeutic support vector machine for glaucoma using a machine Algorithms for learning are conservative. The algorithm has sufficient pragmatism; the correlation clustering mode is subsequently retained The estimated preparation deterrent on a data set has a 91 percent achievement rate on a data set. Consolidation of 500 realistic resolute and glaucoma retina images; hence, depending on the cluster, the computational advantage of In glaucoma therapy, the overlapping device pedestal on the machine learning algorithm has maximum output.

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Kayani, Huma. "Artificial intelligence and its applications in ophthalmology." Journal of Fatima Jinnah Medical University 13, no. 4 (January 15, 2020). http://dx.doi.org/10.37018/jfjmu.724.

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The term artificial intelligence (AI) was proposed in 1956 by Dartmouth scholar John McCarthy, which refers to hardware or software that exhibits behavior which appears intelligent.1 During recent times, AI gained immense popularity as new algorithms, specialized hardware, huge data and cloud-based services were developed. Machine learning (ML), a subset of AI, originated in 1980 and is defined as a set of methods that automatically detect patterns in data and then incorporate this information to predict future data under uncertain conditions. Another escalating technology of ML called Deep learning (DL), launched in 2000s, is an escalating technology of ML and has revolutionized the world of AI. These technologies are powerful tools utilized by modern society for objects' recognition in images, real-time languages' translation, device manipulation via speech (such as Apple's Siri®, Amazon’s Alexa®, Microsoft’s Cortana®, etc.). The steps for AI model include preprocessing image data, train, validate and test the model, and evaluate the trained model's performance. To increase AI prediction efficiency, raw data need to be preprocessed. Data collected from different sources needs to be integrated and the most relevant features selected and extracted to improve the learning process performance. Data set is randomly partitioned into two independent subsets, one is for modeling and the other is for testing. The test set is used to evaluate the final performance of the trained model. The area under receiver operating characteristic curves (AUC) is most used evaluation metrics for quantitative assessment of a model in AI diagnosis. The AUCs effective models range from 0.5 to 1; higher the value of AUC, better the performance of the model.2 In the medical field, AI gained popularity by visualization of input images of highly potential abnormal sites which can be reviewed and analyzed in future. AI and DL algorithms or systems are also widely used in field of ophthalmology. More intensively studied fields are diabetic retinopathy, age related macular degeneration, and cataract and glaucoma. Various ophthalmic imaging modalities used for AI diagnosis include fundus image, optical coherence tomography (OCT), ocular ultrasound, slit-lamp image and visual field. Diabetic retinopathy (DR), a diabetic complication, is a vasculopathy that affects one-third of diabetic patients leading to irreversible blindness. AI has been in use to predict DR risk and its progression. Gulshan and colleague were the first to report the application of DL for DR identification.3 They used large fundus image data sets in supervised manner for DR detection. Other studies applied DL to identify and stage DR. DL-based computer-aided system was introduced to detect DR through OCT images, achieving a specificity of 0.98.4 A computer-aided diagnostic (CAD) system based on CML algorithms using optical coherence tomography angiography images to automatically diagnose non-proliferative DR (NPDR) also achieved high accuracy and AUC.5 Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among old people in the developed world. ML algorithms are being used to identify AMD lesions and prompt early treatment with accuracy usually over 80%.6 Using ML to predict treatment of retinal neovascularity in AMD and DR by anti-vascular endothelial growth factor (Anti VEGF) injection requirements can manage patients' economic burden and resource management. ML algorithms have been applied to diagnose and grade cataract using fundus images, ultrasounds images, and visible wavelength eye images.7 Glaucoma is the third largest sight-threatening eye disease around the world. Glaucoma patients suffered from high intraocular pressure, damage of the optic nerve head, retina nerve fiber layer defect, and gradual vision loss. Studies using DL methods to diagnose glaucoma are few. So far, fundus images and wide-field OCT scans have all been used to construct DL-based glaucomatous diagnostic models. Mostly, the DL-based methods show excellent results.8 In this era of “evidence-based medicine,” clinicians and patients find it difficult to trust a mysterious machine to diagnose yet cannot provide explanations of why the patient has certain disease. In future, advanced AI interpreters will be launched which will contribute significantly to revolutionize current disease diagnostic pattern.

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Khan, Sibghatullah I., Shruti Bhargava Choubey, Abhishek Choubey, Abhishek Bhatt, Pandya Vyomal Naishadhkumar, and Mohammed Mahaboob Basha. "Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning." Concurrent Engineering, July 9, 2021, 1063293X2110266. http://dx.doi.org/10.1177/1063293x211026620.

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Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.

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Chandrinos, Aristeidis, and Dorotheos-Dimitrios Tzamouranis. "A Review of Learning Effect in Perimetry." Ophthalmology Research: An International Journal, April 21, 2020, 23–30. http://dx.doi.org/10.9734/or/2020/v12i230144.

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Glaucoma is the second most common cause of visual impairment in the UK, with visual impairment registrations have increased by 22% since 2010. Glaucoma refers to a group of optic neuropathies leading to visual impairment and blindness. If glaucoma remains untreated, it may produce optic nerve damage, leading to vision loss. Consequently, visual field tests can be extremely valuable for glaucoma. At the same time, visual field assessment should be performed at baseline and periodically in the glaucoma follow-up or monitor the effectiveness of adopted therapeutic schemes. Any visual field test can be masked by one or more artefacts, which can either lead to the incorrect result of visual field loss or to the possible deterioration of existing loss. One of the most important factors is the perimetric learning effect that is present in almost all types of perimetry. To minimize the learning effect, we either have to conduct a practice test procedure, as a demonstration for the patient without collecting data, or to calculate and establish a learning index of the specific patient. By the establishment of such an index, assist the clinician in detecting possible masked or overestimated visual field defects or progression of glaucoma damage. Conclusion: Potentially, the intense data collection at a large number of locations throughout the field in a larger cohort of subjects (visually healthy and glaucomatous) would be required for a better index establishment. The incorporation of fatigue also may be required to form a robust index enough to simulate procedures of glaucoma prognosis. The low signal to noise ratio associated with perimetric testing suggests that improvements will always be difficult to make.

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Romeni, Simone, Davide Zoccolan, and Silvestro Micera. "A Machine Learning Framework to Optimize Optic Nerve Electrical Stimulation for Vision Restoration." SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3787903.

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Zou, Beiji, Changlong Chen, Rongchang Zhao, Pingbo Ouyang, Chengzhang Zhu, Qilin Chen, and Xuanchu Duan. "A novel glaucomatous representation method based on Radon and wavelet transform." BMC Bioinformatics 20, S25 (December 2019). http://dx.doi.org/10.1186/s12859-019-3267-6.

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Abstract Background Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns. Method We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection. Results The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively. Conclusion In conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.

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Xu, Yongli, Man Hu, Hanruo Liu, Hao Yang, Huaizhou Wang, Shuai Lu, Tianwei Liang, et al. "A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis." npj Digital Medicine 4, no. 1 (March 11, 2021). http://dx.doi.org/10.1038/s41746-021-00417-4.

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AbstractThe application of deep learning algorithms for medical diagnosis in the real world faces challenges with transparency and interpretability. The labeling of large-scale samples leads to costly investment in developing deep learning algorithms. The application of human prior knowledge is an effective way to solve these problems. Previously, we developed a deep learning system for glaucoma diagnosis based on a large number of samples that had high sensitivity and specificity. However, it is a black box and the specific analytic methods cannot be elucidated. Here, we establish a hierarchical deep learning system based on a small number of samples that comprehensively simulates the diagnostic thinking of human experts. This system can extract the anatomical characteristics of the fundus images, including the optic disc, optic cup, and appearance of the retinal nerve fiber layer to realize automatic diagnosis of glaucoma. In addition, this system is transparent and interpretable, and the intermediate process of prediction can be visualized. Applying this system to three validation datasets of fundus images, we demonstrate performance comparable to that of human experts in diagnosing glaucoma. Moreover, it markedly improves the diagnostic accuracy of ophthalmologists. This system may expedite the screening and diagnosis of glaucoma, resulting in improved clinical outcomes.

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"Recent Automated Glaucoma Detection Techniques using Color Fundus Images." International Journal of Innovative Technology and Exploring Engineering 8, no. 9S (August 23, 2019): 737–42. http://dx.doi.org/10.35940/ijitee.i1119.0789s19.

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One of the areas in which C-DAC, Mohali is actively engaged, is development of AI powered fundus imaging system providing insight into several severe eye diseases. Glaucoma, one of the most hazardous ocular disease, continues to affect and burden a large section of our population. Neuropathy of optic nerve cells is the prime cause of glaucoma and is the second leading cause of blindness worldwide. It doesn’t manifest itself and is often termed as the silent thief of eye sight. The damage caused by glaucoma is irreversible. Therefore, it is imperative to detect glaucoma at an early stage. The medical literature related to glaucoma indicates that glaucoma detection is a complex process and depends on combination of several parameters. The conventional methods of hand-crafted feature extraction are tedious, time consuming and require human intervention. Even though many such systems have recently shown promising results, but these systems require extensive feature engineering and have limited representation power owing to varied morphology of the optic nerve head. Most of the proposed systems have targeted the parameter cup to disc ratio (CDR) for detection of glaucoma, but that may not be the best approach for building efficient, robust and accurate automated system for glaucoma diagnosis. This paper advocates the use of hybrid approach of manual feature crafting with deep learning. It holds promise of improving the accuracy of glaucoma diagnosis through the automated techniques. It is further proposed that if diagnosis based on CDR remains inconclusive other methods of diagnosis should be adopted to come to a certain conclusion.

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"Automated Fundoscopy for Glaucoma Detection and Classifiction." International Journal of Recent Technology and Engineering 8, no. 5 (January 30, 2020): 3274–78. http://dx.doi.org/10.35940/ijrte.e6370.018520.

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glaucoma is leading chronic eye dieses in the world that leads to vision lost. The main cause of Glaucoma is intrinsic deterioration of the optic nerve which leads high intraocular pressure of the eye. Manually detection of glaucoma is tedious and costly. In our work we are providing automated system for glaucoma detection which is based on fully connected conditional random filed (FC-CRF) model, it works on long and thin structure. Conditional random filed provide a platform for structure prediction. Taking benefit of current results, validating assumption and parameters of our system learned automatically with the help of structured output support vector machine. Our system trained both quantitatively and qualitatively on publically existing data sets: DRIVE, STARE, CHASEDB1 and HRF. Once we obtain segmentation results further classification is done by SVM and K-NN classifier results of our proposed system is analyzed with gold standard labeling provided each data sets in terms of TP,TN,FP and FN. importance of our proposed system is it works for enlarge structure which can provide a platform to other biomedical and biological applications.

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Nwokocha, C. G., and C. S. Ejimadu. "Analysis of Optic Disc and Vertical Cup Disc Ratio among Glaucoma Suspects in a Black Population." Journal of Advances in Medicine and Medical Research, June 15, 2019, 1–8. http://dx.doi.org/10.9734/jammr/2019/v29i1130145.

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Aim: To analyze the optic discs and vertical cup disc ratio in a black population. Method: This is a retrospective study of glaucoma suspects who presented to the clinic. Medical history was recorded and comprehensive ocular examination done on each of the subjects. Ocular examination included visual acuity, visual field, tonometry and ophthalmoscopy. Instruments used during the research were Pen torch for examination of the external structures of the eyes, Keeler ophthalmoscopes for fundus examination, Snellen’s charts both literate and illiterate charts for visual acuity assessment, Reichert AT 555 Auto non-contact tonometer for measurementof the intra-ocular pressure. The optic discs were analyzed using Optical Coherence Tomography machine. Data was analyzed using the statistical package EPI info version 6.04d, a software package designed by the Centers for Disease Control and Prevention (CDC), USA in 2001. Results: This study included total of 240 optic discs of 120 participants comprising 60 males and 60 females were examined with a mean age of 42.8±13.79; the age range was 19 to 75 years. Very Small discs (<1.0mm) 3 accounted for1.3%, Small discs (1.0-1.3mm) 4 accounted for 1.7%, Medium (1.4-1.7mm) 67 accounted for 27.9%, Large (1.8-2.0mm) 58 accounted for 24.2% while Very Large (>2.0mm) 108 accounted for 45.0% in this study. VCDR was noticed to have increased with increasing disc diameter. Optic disc diameter increased with increasing RNFL thickness as well (p < 0.05; r = 0.18). All the very small as well as the small discs were cupped 3% (n = 7/240), 14.6% (n = 35/240) of the medium to very large are also cupped while the remaining 82.5% ( n = 198/240) are normal. Conclusion: There was no significant correlation between disc diameter and VCDR. There was also a weak positive correlation between the optic disc diameter and the retinal nerve fiber layer thickness of the subjects, such a correlation may be the result of either an increased number of nerve fibers in eyes with larger discs or a smaller distance between the circular scan and the true optic disc margin.

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Rezapour, Jasmin, Christopher Bowd, Jade Dohleman, Akram Belghith, James A. Proudfoot, Mark Christopher, Leslie Hyman, et al. "The influence of axial myopia on optic disc characteristics of glaucoma eyes." Scientific Reports 11, no. 1 (April 23, 2021). http://dx.doi.org/10.1038/s41598-021-88406-1.

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AbstractThis study characterizes differences in glaucomatous eyes with and without high axial myopia using custom automated analysis of OCT images. 452 eyes of 277 glaucoma patients were stratified into non (n = 145 eyes), mild (n = 214 eyes), and high axial myopia (axial length (AL) > 26 mm, n = 93 eyes). Optic disc ovality index, tilt and rotation angle of Bruch´s membrane opening (BMO) and peripapillary choroidal thickness (PCT) were calculated using automated and deep learning strategies. High myopic optic discs were more oval and had larger BMO tilt than mild and non-myopic discs (both p < 0.001). Mean PCT was thinnest in high myopic eyes followed by mild and non-myopic eyes (p < 0.001). BMO rotation angle, global retinal nerve fiber layer (RNFL) thickness and BMO-minimum rim width (MRW) were similar among groups. Temporal RNFL was thicker and supranasal BMO-MRW was thinner in high myopic eyes. BMO tilt and PCT showed moderate and temporal RNFL and nasal BMO-MRW showed weak but significant associations with AL in multivariable analyses (all p < 0.05). Large BMO tilt angle and thin PCT are characteristics of highly myopic discs and were not associated with severity of glaucoma. Caution should be exercised when using sectoral BMO-MRW and RNFL thickness for glaucoma management decisions in myopic eyes.

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Seo, Sat byul, and Hyun-kyung Cho. "Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch’s membrane opening-minimum rim width and RNFL." Scientific Reports 10, no. 1 (November 4, 2020). http://dx.doi.org/10.1038/s41598-020-76154-7.

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Abstract We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch’s membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learning model. Discriminating early-stage glaucoma and GS is challenging and a deep-learning model may be helpful to clinicians. NTG accounts for an average 77% of open-angle glaucoma in Asians. BMO-MRW is a new structural parameter that has advantages in assessing neuroretinal rim tissue more accurately than conventional parameters. A dataset consisted of 229 eyes out of 277 GS and 168 eyes of 285 patients with early NTG. A deep-learning algorithm was developed to discriminate between GS and early NTG using a training set, and its accuracy was validated in the testing dataset using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The deep neural network model (DNN) achieved highest diagnostic performance, with an AUC of 0.966 (95%confidence interval 0.929–1.000) in classifying either GS or early NTG, while AUCs of 0.927–0.947 were obtained by other machine-learning models. The performance of the DNN model considering all three OCT-based parameters was the highest (AUC 0.966) compared to the combinations of just two parameters. As a single parameter, BMO-MRW (0.959) performed better than RNFL alone (0.914).

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Datta, Shounak, Eduardo B. Mariottoni, David Dov, Alessandro A. Jammal, Lawrence Carin, and Felipe A. Medeiros. "RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure." Scientific Reports 11, no. 1 (June 15, 2021). http://dx.doi.org/10.1038/s41598-021-91493-9.

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AbstractGlaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test’s innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the retinal nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness around the optic disc, to estimate individual age-corrected 24-2 SAP values. Recursive passes through the proposed network sequentially yield estimates of the visual locations progressively farther from the optic disc. While all the methods used for our experiments exhibit lower performance for the advanced disease group (possibly due to the “floor effect” for the SDOCT test), the proposed network is observed to be more accurate than all the baselines for estimating the individual visual field values. We further augment the proposed network to additionally predict the SAP Mean Deviation values and also facilitate the assignment of higher weightage to the underrepresented groups in the data. We then study the resulting performance trade-offs of the RetiNerveNet on the early, moderate and severe disease groups.

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Journal articles on the topic 'Glaucoma Optic nerve Machine learning'

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