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Journal articles on the topic 'Deep Learning in CI'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Nagasawa, Toshihiko, Hitoshi Tabuchi, Hiroki Masumoto, et al. "Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes." PeerJ 6 (October 22, 2018): e5696. http://dx.doi.org/10.7717/peerj.5696.

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We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5

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Marzouk, Mohamed, and Mohamed Zaher. "Artificial intelligence exploitation in facility management using deep learning." Construction Innovation 20, no. 4 (2020): 609–24. http://dx.doi.org/10.1108/ci-12-2019-0138.

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Purpose This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team. Design/methodology/approach This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional n

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Lei, Ziyue, Xuewen Liao, Zhenzhen Gao, and Ang Li. "CI-NN: A Model-Driven Deep Learning-Based Constructive Interference Precoding Scheme." IEEE Communications Letters 25, no. 6 (2021): 1896–900. http://dx.doi.org/10.1109/lcomm.2021.3060065.

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DePaula Oliveira, Lia, Jiayun Lu, Eric Erak, et al. "Comparison of pathologist and deep learning–based prostate cancer grading for prediction of metastatic outcomes in primary prostate cancer." Journal of Clinical Oncology 42, no. 4_suppl (2024): 345. http://dx.doi.org/10.1200/jco.2024.42.4_suppl.345.

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345 Background: Gleason grading is the most potent prognostic variable in primary prostate cancer, however inter-observer variability remains a major issue, particularly where subspecialty-trained pathologists are not available. Artificial intelligence algorithms for prostate cancer grading may improve health care equity by ensuring widespread access to standardized, high quality grading, however most algorithms have not been tested for performance with respect to oncologic outcomes. Here, we compared deep learning-based and pathologist-based Gleason grading for prediction of metastatic outcom

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Visweswaran, Shyam, Jason B. Colditz, Patrick O’Halloran, et al. "Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study." Journal of Medical Internet Research 22, no. 8 (2020): e17478. http://dx.doi.org/10.2196/17478.

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Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available u

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Rezk, Eman, Mohamed Eltorki, and Wael El-Dakhakhni. "Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach." JMIR Dermatology 5, no. 3 (2022): e39143. http://dx.doi.org/10.2196/39143.

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Background The lack of dark skin images in pathologic skin lesions in dermatology resources hinders the accurate diagnosis of skin lesions in people of color. Artificial intelligence applications have further disadvantaged people of color because those applications are mainly trained with light skin color images. Objective The aim of this study is to develop a deep learning approach that generates realistic images of darker skin colors to improve dermatology data diversity for various malignant and benign lesions. Methods We collected skin clinical images for common malignant and benign skin c

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R.Shankar and D. Sridhar Dr. "A Comprehensive Review on Test Case Prioritization in Continuous Integration Platforms." International Journal of Innovative Science and Research Technology 8, no. 4 (2023): 3223–29. https://doi.org/10.5281/zenodo.8282823.

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Continuous Integration (CI) platforms enable recurrent integration of software variations, creating software development rapidly and cost-effectively. In these platforms, integration, and regression testing play an essential role in Test Case Prioritization (TCP) to detect the test case order, which enhances specific objectives like early failure discovery. Currently, Artificial Intelligence (AI) models have emerged widely to solve complex software testing problems like integration and regression testing that create a huge quantity of data from iterative code commits and test executions. In CI

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Aliyev, Jamil. "A Conceptual Framework for Adaptive Ci/Cd Converyors Optimization Via Deep Reinforcement Learning." SCIENTIFIC RESEARCH 5, no. 5 (2025): 253–57. https://doi.org/10.36719/2789-6919/45/253-257.

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Xu, Lei, Junling Gao, Quan Wang, et al. "Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis." European Thyroid Journal 9, no. 4 (2019): 186–93. http://dx.doi.org/10.1159/000504390.

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Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review

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Amruthalingam, Ludovic, Oliver Buerzle, Philippe Gottfrois, et al. "Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning." Healthcare Informatics Research 28, no. 3 (2022): 222–30. http://dx.doi.org/10.4258/hir.2022.28.3.222.

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Objectives: Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians’ experience. Pustules and brown spots are the main efflorescences of the disease and directly correlate with its activity. We propose an automated deep learning model (DLM) to quantify lesions in terms of count and surface percentage from patient photographs. Methods: In this retrospective study, two dermatologists and a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was t

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Glaser, Dylan, Ahmad K. AlMekkawi, James P. Caruso, et al. "Deep learning for automated spinopelvic parameter measurement from radiographs: a meta-analysis." Artificial Intelligence Surgery 5, no. 1 (2025): 1–15. https://doi.org/10.20517/ais.2024.36.

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Aim: Quantitative measurement of spinopelvic parameters from radiographs is important for assessing spinal disorders but is limited by the subjectivity and inefficiency of manual techniques. Deep learning may enable automated measurement with accuracy rivaling human readers. Methods: PubMed, Embase, Scopus, and Cochrane databases were searched for relevant studies. Eligible studies were published in English, used deep learning for automated spinopelvic measurement from radiographs, and reported performance against human raters. Mean absolute errors and correlation coefficients were pooled in a

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Xiang, Fei, Xiang He, Xingyu Liu, et al. "Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables." Cancers 15, no. 14 (2023): 3543. http://dx.doi.org/10.3390/cancers15143543.

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Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under

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Sugibayashi, Takahiro, Shannon L. Walston, Toshimasa Matsumoto, Yasuhito Mitsuyama, Yukio Miki, and Daiju Ueda. "Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis." European Respiratory Review 32, no. 168 (2023): 220259. http://dx.doi.org/10.1183/16000617.0259-2022.

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BackgroundDeep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.MethodsA search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysisviaa hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Asse

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Wang, Yingxu, Bernard Widrow, Lotfi A. Zadeh, et al. "Cognitive Intelligence." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 4 (2016): 1–20. http://dx.doi.org/10.4018/ijcini.2016100101.

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The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who

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Ye, Xiao-Wei, Tao Jin, and Peng-Yu Chen. "Structural crack detection using deep learning–based fully convolutional networks." Advances in Structural Engineering 22, no. 16 (2019): 3412–19. http://dx.doi.org/10.1177/1369433219836292.

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Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structur

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Raimondo, Diego, Antonio Raffone, Anna Chiara Aru, et al. "Application of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 1724. http://dx.doi.org/10.3390/ijerph20031724.

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Background: This study aims to evaluate the diagnostic performance of Deep Learning (DL) machine for the detection of adenomyosis on uterine ultrasonographic images and compare it to intermediate ultrasound skilled trainees. Methods: Prospective observational study were conducted between 1 and 30 April 2022. Transvaginal ultrasound (TVUS) diagnosis of adenomyosis was investigated by an experienced sonographer on 100 fertile-age patients. Videoclips of the uterine corpus were recorded and sequential ultrasound images were extracted. Intermediate ultrasound-skilled trainees and DL machine were a

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Lee, Jinho, Jin-Soo Kim, Haeng Jin Lee, et al. "Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier." British Journal of Ophthalmology 104, no. 12 (2020): 1717–23. http://dx.doi.org/10.1136/bjophthalmol-2019-314330.

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Background/aimsTo assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive optic neuropathy (CON) based on ganglion cell–inner plexiform layer (GCIPL) and retinal nerve fibre layer (RNFL) spectral-domain optical coherence tomography (SD-OCT).MethodsEighty SD-OCT image sets from 80 eyes of 80 patients with GON along with 81 SD-OCT image sets from 54 eyes of 54 patients with CON were compiled for the study. The bottleneck features extracted from the GCIPL thickness map, GCIPL deviation map, RNFL thickness map and RNFL deviat

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C N, Darshan, and Prof Srinivas V. "Journal of Thoracic Oncology Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51454.

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Non–small cell lung cancer (NSCLC) is a highly virulent type of cancerous illness globally due to the restriction on its early diagnosis and prognosis prediction of disease. Therefore, in this paper, we present an innovative radio genomic model, which synergistically integrates CT-image-based radiomic features and mutational profiles in circulating tumor DNA (ctDNA) to enhance diagnosis capacity and prognosis prediction of NSCLC. We enrolled 200 high-risk patients in three hospitals for contrast-enhanced CT scan and blood test comparison between baseline of any treatment trial. We extracted 1,

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Zuo, Xiaohu, Jianfeng Liu, Ming Hu, Yong He, and Li Hong. "A Deep Learning Model for Cervical Optical Coherence Tomography Image Classification." Diagnostics 14, no. 18 (2024): 2009. http://dx.doi.org/10.3390/diagnostics14182009.

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Objectives: Optical coherence tomography (OCT) has recently been used in gynecology to detect cervical lesions in vivo and proven more effective than colposcopy in clinical trials. However, most gynecologists are unfamiliar with this new imaging technique, requiring intelligent computer-aided diagnosis approaches to help them interpret cervical OCT images efficiently. This study aims to (1) develop a clinically-usable deep learning (DL)-based classification model of 3D OCT volumes from cervical tissue and (2) validate the DL model’s effectiveness in detecting high-risk cervical lesions, includ

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Shen, Ming-Hung, Chi-Cheng Huang, Yu-Tsung Chen, et al. "Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study." Diagnostics 13, no. 8 (2023): 1473. http://dx.doi.org/10.3390/diagnostics13081473.

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The present study aimed to develop an AI-based system for the detection and classification of polyps using colonoscopy images. A total of about 256,220 colonoscopy images from 5000 colorectal cancer patients were collected and processed. We used the CNN model for polyp detection and the EfficientNet-b0 model for polyp classification. Data were partitioned into training, validation and testing sets, with a 70%, 15% and 15% ratio, respectively. After the model was trained/validated/tested, to evaluate its performance rigorously, we conducted a further external validation using both prospective (

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Khurshid, Shaan, Samuel Friedman, Christopher Reeder, et al. "ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation." Circulation 145, no. 2 (2022): 122–33. http://dx.doi.org/10.1161/circulationaha.121.057480.

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Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. Methods: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-A

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Saad, Maliazurina B., Lingzhi Hong, Muhammad Aminu, et al. "Deep learning signature from chest CT and association with immunotherapy outcomes in EGFR/ALK-negative NSCLC." Journal of Clinical Oncology 40, no. 16_suppl (2022): 9061. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.9061.

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9061 Background: Many clinicopathological and molecular features are associate with clinical benefit from immune checkpoint inhibitors (ICIs) for patients with non-small-cell lung cancer (NSCLC), yet none was exclusive underscoring the heterogeneity of lung cancers. As images may provide a holistic view of cancer, we attempted deep learning to chest CT scans to derive a predictor of response to ICIs and test its benefit relative to known clinicopathological factors. Methods: 928 stage IV, EGFR/ALK-negative NSCLC patients treated with ICIs alone or in combination (MD Anderson GEMINI Database) w

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Wang, Tianyi, Ruiyuan Chen, Ning Fan, et al. "Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis." Journal of Medical Internet Research 26 (December 23, 2024): e54676. https://doi.org/10.2196/54676.

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Background Lumbar spinal stenosis (LSS) is a major cause of pain and disability in older individuals worldwide. Although increasing studies of traditional machine learning (TML) and deep learning (DL) were conducted in the field of diagnosing LSS and gained prominent results, the performance of these models has not been analyzed systematically. Objective This systematic review and meta-analysis aimed to pool the results and evaluate the heterogeneity of the current studies in using TML or DL models to diagnose LSS, thereby providing more comprehensive information for further clinical applicati

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Hong, Seung Wook, Juntae Park, Junnam Lee, et al. "Non-invasive colorectal cancer detection using multimodal deep learning ensemble classifier." Journal of Clinical Oncology 42, no. 16_suppl (2024): 3066. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.3066.

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3066 Background: The demand for alternative, non-invasive methods for colorectal cancer (CRC) screening is substantial. Cell-free DNA (cfDNA) whole genome sequencing (WGS) offers a promising avenue, utilizing diverse fragmentomic data. We aimed to develop a new approach: integrating fragment end motif by size (FEMS) with genomic coverage (COV) of cfDNA to enhance CRC screening. Methods: Participants were comprised of 1,506 colonoscopy verified normal samples, 130 advanced adenoma (AA) patients, 302 CRC patients (stage I: 28.5%, stage II: 25.5%, stage III: 31.1%, stage IV: 14.2%, unknown: 0.7%)

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Gracioso, Luciana De Souza. "Indexação automática de imagens na web: tendências e desafios no contexto Deep Learning." Revista Ibero-Americana de Ciência da Informação 11, no. 2 (2018): 541–61. http://dx.doi.org/10.26512/rici.v11.n2.2018.8342.

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O objetivo deste estudo é investigar em que medida as pesquisas na Ciência da Informação (CI) tem aproximado às das técnicas de Deep Learning, sendo relacionadas à representação, descrição e recuperação de imagens na Web, e assim, aferir da mais valia destas pesquisas quando aplicadas aos métodos da área da CI. A partir de uma revisão integrativa de literatura nacional e internacional de modo contextualizado na CI, os documentos recuperados foram analisados conforme os critérios da revisão integrativa, identificando um conjunto de operações que poderiam ser integrados nas metodologias de repre

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Mi, Junjie, Xiaofang Han, Rong Wang, Ruijun Ma, and Danyu Zhao. "Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis." International Journal of Clinical Practice 2022 (March 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/9338139.

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Aim. As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. Method. Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. Results. Eigh

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Valiuškaitė, Viktorija, Vidas Raudonis, Rytis Maskeliūnas, Robertas Damaševičius, and Tomas Krilavičius. "Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination." Sensors 21, no. 1 (2020): 72. http://dx.doi.org/10.3390/s21010072.

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We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the proposed central coordinate tracking algorithm allows us to calculate the movement speed of sperm heads. We have achieved 91.77% (95% CI, 91.11–92.43%) accuracy of sperm head detection on the VISEM (A Multimodal Video Dataset of Human Spermatozoa) sperm sample video dataset. The mean absolute error (MAE) of sperm head vitality prediction was 2.92 (95%

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Weng, Wei-Hung, Sebastien Baur, Mayank Daswani, et al. "Predicting cardiovascular disease risk using photoplethysmography and deep learning." PLOS Global Public Health 4, no. 6 (2024): e0003204. http://dx.doi.org/10.1371/journal.pgph.0003204.

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Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-b

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Valliani, Aly A., Faris F. Gulamali, Young Joon Kwon, et al. "Deploying deep learning models on unseen medical imaging using adversarial domain adaptation." PLOS ONE 17, no. 10 (2022): e0273262. http://dx.doi.org/10.1371/journal.pone.0273262.

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The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 chest radiographs obtained from four academic medical centers. Efficacy was assessed by comparing area under the curve (AUC) pre- and post-adaptation. On the digit recognition task, the baseline CNN achieved an average internal test AUC of 99.87% (95% CI, 99.87-99.87%), which decreased to an average

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Brant, Arthur, Preeti Singh, Xiang Yin, et al. "Performance of a Deep Learning Diabetic Retinopathy Algorithm in India." JAMA Network Open 8, no. 3 (2025): e250984. https://doi.org/10.1001/jamanetworkopen.2025.0984.

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ImportanceWhile prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of these algorithms.ObjectiveTo evaluate the clinical performance of an automated retinal disease assessment (ARDA) algorithm in the postdeployment setting at Aravind Eye Hospital in India.Design, Setting, and ParticipantsThis cross-sectional analysis involved an approximate 1% sample of fundus photographs from patients screened using ARDA. Images were grad

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Ersöz, Betül, Ali Öter, Seref Sagiroglu, Erkan Akkaş, and Mustafa Yapar. "Deep learning based ResNet integrated U-Net approach for segmentation and classification of breast cancer images." Computers and Informatics 5, no. 1 (2025). https://doi.org/10.62189/ci.1604037.

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Deep learning models, particularly Convolutional Neural Networks and U-Net architectures, are successfully utilized for segmenting breast cancer histology images, enabling precise identification of anatomical structures and pathological lesions. This study highlights the effectiveness of the U-Net architecture in histology imaging and segmentation, demonstrating its potential to enhance the diagnosis process in medical imaging. Such advancements are crucial for improving the speed and accuracy of breast cancer diagnosis, potentially benefiting thousands of patients annually, primarily women, a

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Guimarães, Pedro, Andreas Keller, Tobias Fehlmann, Frank Lammert, and Markus Casper. "Deep learning-based detection of eosinophilic esophagitis." Endoscopy, May 31, 2021. http://dx.doi.org/10.1055/a-1520-8116.

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Abstract Background For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorithms have demonstrated potential in medical image analysis. Here we establish a convolutional neuronal network (CNN)-based approach that can distinguish the appearance of EoE from normal findings and candida esophagitis. Methods We trained and tested a CNN using 484 real-world endoscopic images from 134 subjects consisting of three classes (normal, EoE, and candidiasis). Images were split into two completely independent datasets. The propos

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Nieri, Michele, Lapo Serni, Tommaso Clauser, Costanza Paoletti, and Lorenzo Franchi. "Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review." Oral Diseases, March 31, 2025. https://doi.org/10.1111/odi.15330.

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ABSTRACTObjectiveTo directly compare the diagnostic accuracy of deep learning models with human experts and other diagnostic methods used for the clinical detection of oral cancer.MethodsComparative diagnostic studies involving patients with photographic images of oral mucosal lesions (cancer or non‐cancer) were included. Only studies using deep learning methods were eligible. Medline, EMBASE, Scopus, Google Scholar, and ClinicalTrials.gov were searched until September 2024. QUADAS‐C assessed the risk of bias. A Bayesian meta‐analysis compared diagnostic test accuracy.ResultsEight studies were

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Elghaish, Faris, Sandra T. Matarneh, Saeed Talebi, Soliman Abu-Samra, Ghazal Salimi, and Christopher Rausch. "Deep learning for detecting distresses in buildings and pavements: a critical gap analysis." Construction Innovation, November 9, 2021. http://dx.doi.org/10.1108/ci-09-2021-0171.

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Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detectio

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Ji, Qingqing, Guohua Zhou, and Xiangxiang Sun. "Deep learning signature to predict postoperative anxiety in patients receiving lung cancer surgery." Frontiers in Surgery 12 (March 24, 2025). https://doi.org/10.3389/fsurg.2025.1573370.

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This study aims on establishing and validate a deep learning signature based on magnetic resonance imaging (MRI) to predict postoperative anxiety in patients receiving lung cancer surgery. In the current study, 202 patients receiving lung cancer surgery were included. Preoperative MRI-T1WI images were collected to train the deep learning signature utilized the ResNet-152 algorithm. The relationships between clinical variables and postoperative anxiety were explored via Logistic regression and the predictive performances of the developed deep learning signature were evaluated via receiver opera

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Li, Pinhao, Yan Wang, Hui Li, et al. "Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study." Aging Clinical and Experimental Research, January 4, 2023. http://dx.doi.org/10.1007/s40520-022-02325-3.

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AbstractElderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep

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Elghaish, Faris, Sandra T. Matarneh, and Mohammad Alhusban. "The application of “deep learning” in construction site management: scientometric, thematic and critical analysis." Construction Innovation, December 28, 2021. http://dx.doi.org/10.1108/ci-10-2021-0195.

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Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learn

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Chen, Xiehui, Wenqin Guo, Lingyue Zhao, et al. "Acute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms." Frontiers in Cardiovascular Medicine 8 (August 24, 2021). http://dx.doi.org/10.3389/fcvm.2021.654515.

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Background: Acute myocardial infarction (AMI) is associated with a poor prognosis. Therefore, accurate diagnosis and early intervention of the culprit lesion are of extreme importance. Therefore, we developed a neural network algorithm in this study to automatically diagnose AMI from 12-lead electrocardiograms (ECGs).Methods: We used the open-source PTB-XL database as the training and validation sets, with a 7:3 sample size ratio. Twenty-One thousand, eight hundred thirty-seven clinical 12-lead ECGs from the PTB-XL dataset were available for training and validation (15,285 were used in the tra

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Vrudhula, Amey, Milos Vukadinovic, Christiane Haeffele, et al. "Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography." JAMA Cardiology, April 16, 2025. https://doi.org/10.1001/jamacardio.2025.0498.

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ImportanceAccurate assessment of tricuspid regurgitation (TR) is necessary for identification and risk stratification.ObjectiveTo design a deep learning computer vision workflow for identifying color Doppler echocardiogram videos and characterizing TR severity.Design, Setting, and ParticipantsAn automated deep learning workflow was developed using 47 312 studies (2 079 898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. Data analysis was performed in 2024. The pipeline was tested on a temporally distinct test set of 2462 studies (108 138 videos) obtained in 2022 at CSMC

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Araki, Makoto, Sangjoon Park, Akihiro Nakajima, Hang Lee, Jong Chul Ye, and Ik-Kyung Jang. "Diagnosis of coronary layered plaque by deep learning." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-29293-6.

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AbstractHealed coronary plaques, morphologically characterized by a layered phenotype, are signs of previous plaque destabilization and healing. Recent optical coherence tomography (OCT) studies demonstrated that layered plaque is associated with higher levels of local and systemic inflammation and rapid plaque progression. However, the diagnosis of layered plaque needs expertise in OCT image analysis and is susceptible to inter-observer variability. We developed a deep learning (DL) model for an accurate diagnosis of layered plaque. A Visual Transformer (ViT)-based DL model that integrates in

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Ahn, Sangil, Yoosoo Chang, Ria Kwon, et al. "Mammography-Based Deep Learning Model for Coronary Artery Calcification." European Heart Journal - Cardiovascular Imaging, November 21, 2023. http://dx.doi.org/10.1093/ehjci/jead307.

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Abstract Aims Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events. Methods and results We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the r

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Lehman, Constance D., Sarah Mercaldo, Leslie R. Lamb, et al. "Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening." JNCI: Journal of the National Cancer Institute, July 25, 2022. http://dx.doi.org/10.1093/jnci/djac142.

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Abstract Background Deep learning breast cancer risk models demonstrate improved accuracy compared to traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient’s prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening. Methods We collected data on 119,139 bilateral screening mammograms in 57,617 consecutive patients screened at five facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic me

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Zhang, Wen-fei, Dong-hong Li, Qi-jie Wei, et al. "The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy." Frontiers in Medicine 9 (May 16, 2022). http://dx.doi.org/10.3389/fmed.2022.839088.

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PurposeTo evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR).Materials and MethodsThe prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity and specificity) and secondary evaluation index like positive predictive values (PPV), negative predict

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Mahamivanan, Hadi, Navid Ghassemi, Mohammad Tayarani Darbandi, et al. "Material recognition for construction quality monitoring using deep learning methods." Construction Innovation, July 12, 2023. http://dx.doi.org/10.1108/ci-04-2022-0074.

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Purpose This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring. Design/methodology/approach A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputtin

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Xie, He, Zhongwen Li, Chengchao Wu, et al. "Deep learning for detecting visually impaired cataracts using fundus images." Frontiers in Cell and Developmental Biology 11 (July 28, 2023). http://dx.doi.org/10.3389/fcell.2023.1197239.

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Purpose: To develop a visual function-based deep learning system (DLS) using fundus images to screen for visually impaired cataracts.Materials and methods: A total of 8,395 fundus images (5,245 subjects) with corresponding visual function parameters collected from three clinical centers were used to develop and evaluate a DLS for classifying non-cataracts, mild cataracts, and visually impaired cataracts. Three deep learning algorithms (DenseNet121, Inception V3, and ResNet50) were leveraged to train models to obtain the best one for the system. The performance of the system was evaluated using

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Park, S., M. Arakai, A. Nakajima, H. Lee, J. C. Ye, and I. K. Jang. "Diagnosis of coronary layered plaque by deep learning." European Heart Journal 43, Supplement_2 (2022). http://dx.doi.org/10.1093/eurheartj/ehac544.338.

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Abstract Background/Introduction Healed coronary plaques, morphologically characterized by a layered pattern, are signatures of previous plaque disruption and healing. Recent optical coherence tomography (OCT) studies showed that layered plaque is associated with vascular vulnerability and rapid plaque progression. However, the diagnosis of layered plaque requires expertise in OCT image interpretation and is susceptible to interobserver variability. Purpose We aimed to develop a deep learning (DL) model for an accurate diagnosis of layered plaque. Methods We developed a Visual Transformer (ViT

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Holste, Gregory, Evangelos K. Oikonomou, Bobak J. Mortazavi, Zhangyang Wang, and Rohan Khera. "Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning." Communications Medicine 4, no. 1 (2024). http://dx.doi.org/10.1038/s43856-024-00538-3.

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Abstract Background Advances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets. This label efficiency is often desirable, given the difficulty of obtaining expert labels for medical image recognition tasks. However, most efforts toward SSL in medical imaging are not adapted to video-based modalities, such as echocardiography. Methods We developed a self-supervised contrastive learning approach, EchoCLR, for echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream c

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Zhang, Zheming, Qi Gao, Dong Fang, et al. "Effective automatic classification methods via deep learning for myopic maculopathy." Frontiers in Medicine 11 (November 13, 2024). http://dx.doi.org/10.3389/fmed.2024.1492808.

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BackgroundPathologic myopia (PM) associated with myopic maculopathy (MM) is a significant cause of visual impairment, especially in East Asia, where its prevalence has surged. Early detection and accurate classification of myopia-related fundus lesions are critical for managing PM. Traditional clinical analysis of fundus images is time-consuming and dependent on specialist expertise, driving the need for automated, accurate diagnostic tools.MethodsThis study developed a deep learning-based system for classifying five types of MM using color fundus photographs. Five architectures—ResNet50, Effi

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Li, Ning, Zhe Wu, Chao Jiang, et al. "An automatic FreshRib fracture detection and positioning system using deep learning." British Journal of Radiology, March 27, 2023. http://dx.doi.org/10.1259/bjr.20221006.

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Objective: To evaluate the performance and robustness of a deep learning-basedautomatic freshrib fracture detection and positioningsystem (FRF-DPS). Methods: CT scans of 18,172 participants admitted to eighthospitals from 2009.06 to 2019.03 were retrospectively collected. Patients were divided into development set (14,241), multi center internal test set (1,612), and external test set (2,319). In internal test set, sensitivity, false-positives (FPs) and specificity were used to assess fresh rib fracture detection performance at the lesion- and examination-levels. In external test set, the perf

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Tan, Yuhe, Yunxi Ma, Suyun Rao, and Xufang Sun. "Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis." European Journal of Ophthalmology, September 6, 2023. http://dx.doi.org/10.1177/11206721231199848.

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Objective Deep learning has been used to detect chronic kidney disease (CKD) from retinal fundus photographs. We aim to evaluate the performance of deep learning for CKD detection. Methods The original studies in CKD patients detected by deep learning from retinal fundus photographs were eligible for inclusion. PubMed, Embase, the Cochrane Library, and Web of Science were searched up to October 31, 2022. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias. Results Four studies enrolled 114,860 subjects were included. The pooled sensitivit

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