Journal articles: 'Angle Classification'

1

Weinberger, Tom. "Angle classification." American Journal of Orthodontics and Dentofacial Orthopedics 103, no. 4 (April 1993): 26A—28A. http://dx.doi.org/10.1016/s0889-5406(05)80388-9.

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Katz, Morton I. "Angle classification revisited 2: A modified Angle classification." American Journal of Orthodontics and Dentofacial Orthopedics 102, no. 3 (September 1992): 277–84. http://dx.doi.org/10.1016/s0889-5406(05)81064-9.

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3

Barroco, Rui dos Santos, Bruno Rodrigues de Miranda, Herbert Amantéa Fernandes, Gregory Bittar Pessoa, Danilo Ryuko Cândido Nishikawa, Leticia Zaccaria Prates de Oliveira, Álvaro Diego Pupa De Freitas, and Caio Ivo de Almeida. "Inter-rater reliability of Böhler and Gissane angles in different calcaneal fracture according to the Essex-Lopresti and Sanders classifications." Journal of the Foot & Ankle 15, no. 2 (August 31, 2021): 133–39. http://dx.doi.org/10.30795/jfootankle.2021.v15.1535.

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Objective: To evaluate the inter-rater reliability and intra-class correlation coefficients (ICC) of Böhler’s angle and the critical angle of Gissane in calcaneal fractures, stratified by severity and by the Essex-Lopresti and Sanders classifications. Methods: Retrospective study of radiographs obtained from 97 patients: 67 with calcaneal fractures and 30 with normal lateral radiographs (used as a control group). Böhler’s angle and the angle of Gissane were measured by six raters: two orthopedic surgery residents, two musculoskeletal radiologists, a foot and ankle surgery fellow, and a senior consultant in foot surgery. Statistical analysis of inter-rater reliability was performed for the two angles, in the sample overall and stratified by the different radiographic and CT subtypes of calcaneal fractures. Results: For the angle of Gissane, the ICC was at best 0.400 (95% CI: 0.250 to 0.581) for normal radiographs, with poor agreement across all classifications and severity stratifications. For Böhler’s angle, the ICC values indicated weak to moderate agreement, with the best reproducibility obtained for the overall sample (0.740; 95% CI: 0.673 to 0.801). In Sanders type 1 fractures, the ICC was 0.704 (95% CI: 0.397 to 0.940), and in Sanders type 2 fractures, 0.762 (95% CI: 0.634 to 0.870). Conclusion: Böhler’s angle is more reproducible than the critical angle of Gissane, with greater inter-rater reliability among fractures deemed less severe on the Sanders classification, although the overall ICC ranged from weak to moderate at best. Level of Evidence III; Case Control Study; Diagnostic Studies.

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Li, Zijia, and Josef Schicho. "Classification of angle-symmetric 6R linkages." Mechanism and Machine Theory 70 (December 2013): 372–79. http://dx.doi.org/10.1016/j.mechmachtheory.2013.08.002.

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5

Zhang, C., and Y. Liu. "Multicategory angle-based large-margin classification." Biometrika 101, no. 3 (July 23, 2014): 625–40. http://dx.doi.org/10.1093/biomet/asu017.

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Roberts, Paul L. D., and Jules S. Jaffe. "Multiple angle acoustic classification of zooplankton." Journal of the Acoustical Society of America 121, no. 4 (April 2007): 2060–70. http://dx.doi.org/10.1121/1.2697471.

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Sihota, Ramanjit. "Classification of primary angle closure disease." Current Opinion in Ophthalmology 22, no. 2 (March 2011): 87–95. http://dx.doi.org/10.1097/icu.0b013e328343729f.

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Yang, Yi, Yuxuan Guo, and Xiangyu Chang. "Angle-based cost-sensitive multicategory classification." Computational Statistics & Data Analysis 156 (April 2021): 107107. http://dx.doi.org/10.1016/j.csda.2020.107107.

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Kowsher, Md, Imran Hossen, Anik Tahabilder, Nusrat Jahan Prottasha, Md Murad Hossain Sarker, Nazmul Ahasan, and Md Imdadul Hoque. "SDSV: Angle Measurement for Supervised Classification." Procedia Computer Science 189 (2021): 216–23. http://dx.doi.org/10.1016/j.procs.2021.05.085.

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Kim, Seok Kwun, Jae Jung Han, and Jeong Tae Kim. "Classification and Treatment of Prominent Mandibular Angle." Aesthetic Plastic Surgery 25, no. 5 (September 2001): 382–87. http://dx.doi.org/10.1007/s002660010150.

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Park, Sung-Sik, Jung-Shin Lee, and Dong-Eun Lee. "Aggregate Roundness Classification Using a Wire Mesh Method." Materials 13, no. 17 (August 20, 2020): 3682. http://dx.doi.org/10.3390/ma13173682.

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Herein, we suggest a wire mesh method to classify the particle shape of large amounts of aggregate. This method is controlled by the tilting angle and opening size of the wire mesh. The more rounded the aggregate particles, the more they roll on the tilted wire mesh. Three different sizes of aggregate: 11–15, 17–32, and 33–51 mm were used for assessing their roundness after classification using the sphericity index into rounded, sub-rounded/sub-angular, and angular. The aggregate particles with different sphericities were colored differently and then used for classification via the wire mesh method. The opening sizes of the wire mesh were 6, 11, and 17 mm and its frame was 0.5 m wide and 1.8 m long. The ratio of aggregate size to mesh-opening size was between 0.6 and 8.5. The wire mesh was inclined at various angles of 10°, 15°, 20°, 25°, and 30° to evaluate the rolling degree of the aggregates. The aggregates were rolled and remained on the wire mesh between 0.0–0.6, 0.6–1.2, and 1.2–1.8 m depending on their sphericity. A tilting angle of 25° was the most suitable angle for classifying aggregate size ranging from 11–15 mm, while the most suitable angle for aggregate sizes of 17–32 and 33–51 mm was 20°. The best ratio for the average aggregate size to mesh-opening size for aggregate roundness classification was 2.

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12

Gao, Qinghua, Shuo Jiang, and Peter B. Shull. "Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model." Sensors 20, no. 10 (May 24, 2020): 2972. http://dx.doi.org/10.3390/s20102972.

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Hand gesture classification and finger angle estimation are both critical for intuitive human–computer interaction. However, most approaches study them in isolation. We thus propose a dual-output deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human–computer interaction and in control environments with both discrete and continuous variables.

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Okşayan, Rıdvan, Ali Murat Aktan, Oral Sökücü, Esin Haştar, and Mehmet Ertuğrul Ciftci. "Does the Panoramic Radiography Have the Power to Identify the Gonial Angle in Orthodontics?" Scientific World Journal 2012 (2012): 1–4. http://dx.doi.org/10.1100/2012/219708.

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Purpose. The objective of this study was to assess gonial angle under the angle classification by comparing panoramic radiograph and lateral cephalometric radiograph.Materials and Methods. 49 patients (25 males, 24 females) with an age range of 12–29 years participated in the present study. Subjects were retrospectively selected among those categorised as skeletal and dental Class I, II, and III malocclusion group. Using lateral cephalometric radiograph, mandibular and ramal planes were drawn and based on these planes. Gonial angle was determined from two tangents which were drawn from the inferior border of the mandible and posterior borders of the condyle and ramus of both sides in the panoramic radiographs. Multiple comparison tests (ANOVA) were used to determine differences between the three angle groups.Results. There were no significant differences between Class I, II, and III malocclusion group values of gonial angles determined by lateral cephalometric radiograph and panoramic radiographs ().Conclusion. Panoramic radiograph results were shown to be as reliable as lateral cephalometric radiograph in all angle classifications. Panoramic radiography can be used as an alternative radiographic technique to detect gonial angle in orthodontic patients.

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Hwang, Jaejin, and Kyung-Sun Lee. "Classification of Whole-Body Postural Discomfort Using Cluster Analysis." International Journal of Environmental Research and Public Health 17, no. 22 (November 10, 2020): 8314. http://dx.doi.org/10.3390/ijerph17228314.

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The objectives of this study were to evaluate the effect of gender and postures of the neck, trunk, and knee on overall postural discomfort, and to classify combined postures into different postural discomfort groups. A total of 95 participants (42 males and 53 females) performed 45 different static postures, which were a combination of 3 neck angles, 5 trunk angles, and 3 knee angles, and rated the perceived postural discomfort. Non-hierarchical K-means cluster analysis was employed to classify the 45 different combined postures into several postural discomfort groups. Postural discomfort was significantly affected by gender and postures of the neck, trunk, and knee (p < 0.001). Three clusters (high, medium, and low discomfort) were identified and the postural discomfort was significantly different between clusters (p < 0.001). The high discomfort group consisted of mostly males with high knee and trunk flexion angles and a moderate neck flexion angle. The low discomfort group was female-dominant with low neck and trunk flexion angles and a moderate knee flexion angle. The different flexibility (stiffness) of the joint motions between genders may affect the gender difference in postural discomfort. The knee and trunk postures were critical to the postural balance, which may affect the perception of whole-body discomfort. This result will be useful for developing and improving postural observation tools.

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Yan, Deng, Liu, and Zhu. "Application of UAV-Based Multi-Angle Hyperspectral Remote Sensing in Fine Vegetation Classification." Remote Sensing 11, no. 23 (November 22, 2019): 2753. http://dx.doi.org/10.3390/rs11232753.

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To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF_0°) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF_0° was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF_PP) and on the cross-principal plane (BRDF_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF_PP features performed better than BRDF_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10° and −20° were most favorable for vegetation classification (solar position: zenith angle 28.86°, azimuth 169.07°) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring.

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Saville, Michael A., Julie A. Jackson, and Dane F. Fuller. "Rethinking vehicle classification with wide-angle polarimetric SAR." IEEE Aerospace and Electronic Systems Magazine 29, no. 1 (January 2014): 41–49. http://dx.doi.org/10.1109/maes.2014.130057.

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17

Chen, Jin, Runsheng Wang, and Cheng Wang. "A multiresolution spectral angle‐based hyperspectral classification method." International Journal of Remote Sensing 29, no. 11 (May 19, 2008): 3159–69. http://dx.doi.org/10.1080/01431160701442138.

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18

Roberts, Paul L. D., and Jules S. Jaffe. "Multiple‐angle acoustic scattering and classification of zooplankton." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 3108–9. http://dx.doi.org/10.1121/1.4787585.

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19

Katz, Morton I. "Angle classification revisited 1: Is current use reliable?" American Journal of Orthodontics and Dentofacial Orthopedics 102, no. 2 (August 1992): 173–79. http://dx.doi.org/10.1016/0889-5406(92)70030-e.

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20

Aksu, Neslihan, Vefa Atansay, Işık Karalök, Taner Aksu, Ayhan Nedim Kara, and Azmi Hamzaoglu. "Relationship of Patellofemoral Angles and Tibiofemoral Rotational Angles With Jumper’s Knee in Professional Dancers: An MRI Analysis." Orthopaedic Journal of Sports Medicine 9, no. 3 (March 1, 2021): 232596712098522. http://dx.doi.org/10.1177/2325967120985229.

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Background: Jumper’s knee is a type of tendinopathy affecting the distal insertion of the quadriceps tendon (25% of cases) or the patellar tendon. It has been shown that frontal-plane measurements, such as genu valgum, genu varum, an increased quadriceps angle, a protuberant tibial tuberosity, patella alta, and short hamstring muscles, may be related to jumper’s knee. Purpose: To investigate the effects of tibiofemoral rotational angles and patellofemoral (PF) angles on the development of jumper’s knee in professional folk dancers. Study Design: Case-control study; Level of evidence, 3. Methods: We examined 26 dancers (16 male, 10 female) with knee pain using magnetic resonance imaging (MRI), for a total of 32 knees. Of the knees, 21 with quadriceps tendinopathy (QT) and 7 with patellar tendinopathy (PT) were detected. Using MRI scans, we measured PF angles (PF sulcus angle, lateral PF angle, patellar tilt angle, lateral trochlear inclination angle, lateral patellar tilt angle, and PF congruence angle) and tibiofemoral rotational angles (condylar twist angle, posterior condylar angle, femoral Insall angle, tibial Insall angle, posterior tibiofemoral angle, and angle between the Whiteside line and posterior femoral condylar line) and noted specifics such as patella alta, patella baja, and the Wiberg classification of the patellar shape between the patients with versus without QT and between patients with versus without PT to understand if there was any relationship with tendinopathy. Results: No statistically significant difference was observed in age, sex, patella alta, or the Wiberg classification between the QT groups (with vs without) and between the PT groups (with vs without) ( P > .05). Having QT was found to be significantly associated with the PF sulcus angle ( P = .009), and having PT was found to be significantly associated with the femoral Insall angle ( P = .029). Conclusion: Jumper’s knee was found to be associated with anatomic variations of the PF sulcus angle and rotation of the patellar tendon in relation to the femur (femoral Insall angle) on axial MRI scans in professional dancers. Unlike those of other athletes, dancers’ knees are exposed more to external rotation forces because of turnout, and this can be the cause of jumper’s knee.

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21

Kaygisiz, Emine, Fatma Deniz Uzuner, and Lale Taner. "A Comparison of Three Orthodontic Treatment Indices with Regard to Angle Classification." Journal of Clinical Pediatric Dentistry 40, no. 2 (March 1, 2016): 169–74. http://dx.doi.org/10.17796/1053-4628-40.2.169.

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Objectives: To calculate the agreement between the Dental Aesthetic Index (DAI) and the Index of Complexity, Outcome and Need (ICON) in assessing orthodontic treatment need and to determine correlations between the Peer Assessment Rating (PAR) and DAI and ICON scores according to Angle classification among patients referred for orthodontic evaluation. Study Design: This study included 457 randomly selected patients between 9 to17 years of age. Patients were divided into four groups according to Angle classification [Class I (n=154), Class II division 1(Class II/1) (n=155), Class II division 2(Class II/2) (n=52) and Class III (n=96)]. Relationships between PAR scores and ICON and DAI scores were evaluated with the Spearman correlation test. Unweighted kappa statistics were used to analyse agreement between the ICON and DAI on the need for treatment, according to Angle classification. Results: Class I malocclusions scored significantly lower than other Angle classifications in all indices. Both the ICON and DAI showed significant positive correlations with the PAR in the general study population. For Class II/2 patients, no correlation was found between PAR and DAI scores. There was significant agreement between the ICON and DAI on treatment need among Class I, Class II/1 and Class II/2 patients however, no agreement was found for Class III malocclusions. Conclusions: The ICON, DAI and PAR produce similar results and can be used interchangeably for the general orthodontic patient population. However, based on Angle classification, prominent differences exist in scoring certain occlusal features.

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Tsutsui, Yoichiro, Takayuki Tanaka, Shun'ichi Kaneko, Yukinobu Sakata, and Maria Q. Feng. "Human Joint Motion Recognition Using Ultrasound Pulse Echo Based on Test Feature Classifier." Journal of Robotics and Mechatronics 21, no. 5 (October 20, 2009): 597–606. http://dx.doi.org/10.20965/jrm.2009.p0597.

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The simultaneous joint torque and angle recognition for dynamic movement we propose using ultrasound echo and is test feature classifier is for application in human-machine systems such as wearable robots. Ultrasound emitted on the skin surface and reflected within the body is used to recognize features extracted from the echo. Features is change based on the muscle movement related to target joint movements enabling joint movement to be recognized. Experiments conducted to recognize elbow movement involved classifying a test data set into torque and angle classes based on feature scores rectified and integrated in 6 blocks extracted from ultrasound echoes. Correct classification was 54% for torque recognition, and 67% for angle recognition. Some data classified incorrectly was still in classes close to the correct class, with quasi correct classification 88% for torque and 94% for angle, corresponding to allowing larger classes for correct classification. Results demonstrated that recognition using our proposal was more successful than random selection at 11% for torque recognition and 13% for anble recognition. A strategy we describe for reducing the number of features to decrease calculation time cost, reduced by 50% while maintaining accuracy.

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23

McLean, K. Robin. "Cyclotomic and double angle polynomials." Mathematical Gazette 88, no. 512 (July 2004): 208–14. http://dx.doi.org/10.1017/s0025557200174923.

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One admires and applauds the enterprise of anyone who uses Gauss’s 1801 Disquisitiones arithmeticae as the starting point for mathematical exploration. I enjoyed McKeon and Sherry’s description of their journey [1] and the challenge of their conjectures. They drew attention to a class of polynomials that satisfy what they called the double angle condition ((1) below). Unfortunately, their failure to work with an appropriate definition of cyclotomic polynomials seriously handicapped their computer-aided attempt to classify double angle polynomials. Once this is remedied, a pleasant classification emerges, at least for polynomials with rational coefficients, without recourse to a computer. The main aim of this article is to present this classification. A brief final section considers McKeon and Sherry’s conjectures about irreducible double angle polynomials.

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Yao, Yuan, Yee Leung, Tung Fung, Zhenfeng Shao, Jie Lu, Deyu Meng, Hanchi Ying, and Yu Zhou. "Continuous Multi-Angle Remote Sensing and Its Application in Urban Land Cover Classification." Remote Sensing 13, no. 3 (January 25, 2021): 413. http://dx.doi.org/10.3390/rs13030413.

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Because of the limitations of hardware devices, such as the sensors, processing capacity, and high accuracy altitude control equipment, traditional optical remote sensing (RS) imageries capture information regarding the same scene from mostly one single angle or a very small number of angles. Nowadays, with video satellites coming into service, obtaining imageries of the same scene from a more-or-less continuous array of angles has become a reality. In this paper, we analyze the differences between the traditional RS data and continuous multi-angle remote sensing (CMARS) data, and unravel the characteristics of the CMARS data. We study the advantages of using CMARS data for classification and try to capitalize on the complementarity of multi-angle information and, at the same time, to reduce the embedded redundancy. Our arguments are substantiated by real-life experiments on the employment of CMARS data in order to classify urban land covers while using a support vector machine (SVM) classifier. They show the superiority of CMARS data over the traditional data for classification. The overall accuracy may increase up to about 9% with CMARS data. Furthermore, we investigate the advantages and disadvantages of directly using the CMARS data, and how such data can be better utilized through the extraction of key features that characterize the variations of spectral reflectance along the entire angular array. This research lay the foundation for the use of CMARS data in future research and applications.

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Li, Fei, Rouxi Zhou, Kai Gao, Ling Jin, and Xiulan Zhang. "Volumetric parameters-based differentiation of narrow angle from open angle and classification of angle configurations: an SS-OCT study." British Journal of Ophthalmology 104, no. 1 (April 29, 2019): 92–97. http://dx.doi.org/10.1136/bjophthalmol-2018-313386.

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BackgroundTo evaluate the diagnostic ability of volumetric parameters to differentiate narrow angle from open angle and distinguish different configurations of narrow angle.MethodsThe current study was composed of two parts. In the first part, with gonioscopy as reference standard, we tested power of each parameter to differentiate narrow angle from open angle. In the second part, we evaluated the efficacy of different parameters to distinguish angle configurations which were subclassified into type 1 (pupillary block) and type 2 (non-pupillary block and multiple mechanisms) based on ultrasound biomicroscopy (UBM) images.ResultsIn part 1, the training set was composed of 117 narrow-angle eyes and 60 open-angle eyes, and the validation set included 38 narrow-angle eyes and 37 open-angle eyes. Anterior chamber volume (ACV) outperformed all the other parameters with an area under the curve (AUC) of 0.988. The sensitivity and specificity of the cut-off value 98.1 mm3 in the validation set were 90.0% and 97.1%, respectively. In part 2, training set was composed of 96 eyes of 88 patients with primary angle-closure disease, with 49 diagnosed as type 1 and 47 as type 2 configuration. 32 eyes were used for validation. A model comprised of iris volume (IV), iris thickness (IT) 2000 µm from the scleral spur and angle open distance (AOD) 750 µm from the scleral spur was found to have an AUC of 0.793 (95% CI, 0.695 to 0.870). Sensitivity and specificity of the model were 82.6% and 77.8% respectively in the validation set.ConclusionsWith ACV, we can detect patients with narrow angle from open angle faster and more easily than AOD and anterior chamber depth. Then, for patients with narrow angle, the combination of IV, IT and AOD750 measured by swept-source optical coherence tomography could further classify configurations of angle closure compared with UBM.

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Rao, Aparna, Debananda Padhy, Gopinath Das, and Sarada Sarangi. "Evolving Paradigms in Classification of Primary Angle Closure Glaucoma." Seminars in Ophthalmology 32, no. 2 (August 20, 2015): 228–36. http://dx.doi.org/10.3109/08820538.2015.1053620.

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Boland, Michael V., and Harry A. Quigley. "Risk Factors and Open-angle Glaucoma: Classification and Application." Journal of Glaucoma 16, no. 4 (June 2007): 406–18. http://dx.doi.org/10.1097/ijg.0b013e31806540a1.

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Bourzgui, Farid, Asmaa Khribchi, Zineb Rachdy, Sami Housbane, and Mohamed Bennani Othmani. "Evaluation of arch forms depending on the angle classification." International Orthodontics 14, no. 4 (December 2016): 528–36. http://dx.doi.org/10.1016/j.ortho.2016.10.017.

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Shi, Pengfei, Zhongxin Zhao, Xinnan Fan, Xijun Yan, Wei Yan, and Yuanxue Xin. "Remote Sensing Image Object Detection Based on Angle Classification." IEEE Access 9 (2021): 118696–707. http://dx.doi.org/10.1109/access.2021.3107358.

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Pancherz, Hans, and Lars Bondemark. "The bite-type malocclusion classification – An extended Angle-method. Is the new classification reliable?" APOS Trends in Orthodontics 11 (July 9, 2021): 100–105. http://dx.doi.org/10.25259/apos_3_2021.

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Objectives: As part in appraising the usefulness of the new bite-type method, a reliability assessment of the classification system was performed. Materials and Methods: Eighty-five selected dental casts were appraised by six operators and compared to a “Gold Standard.” The reliability of the bite-type method was assessed by double registrations of each of the nine bite-type classes by every operator. Results: An overall acceptable bite-type classification of all examiners existed. Correct assessments were found in 75–90% of the dental casts. Thus, the bite-type classification is a reliable method for categorizing sagittal malocclusions. Conclusion: The procedure could favorably replace the angle classification as it is more precise and detailed, without losing the strength of the basic angle system as an uncomplicated communication tool.

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Qi, Jiangang, Zhaowen Zheng, and Huaqing Sun. "Classification of Sturm–Liouville differential equations with complex coefficients and operator realizations." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467, no. 2131 (December 15, 2010): 1835–50. http://dx.doi.org/10.1098/rspa.2010.0281.

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In this paper, a new classification of Sturm–Liouville differential equations with complex coefficients is given. Compared with the corresponding result of Brown et al ., this classification reveals the great effects of rotation angle and it is independent of the rotation angles. Moreover, the asymptotic behaviours of functions in the maximal domain are presented and J -self-adjoint extensions associated with the differential equations are characterized.

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Contreras-Murillo, Miguel, Sergio G. de-los-Cobos-Silva, Pedro Lara-Velázquez, Eric A. Rincón-García, Román A. Mora-Gutiérrez, and Miguel Á. Gutiérrez-Andrade. "Sex Classification via 2D-Skeletonization." Mathematical Problems in Engineering 2020 (November 23, 2020): 1–12. http://dx.doi.org/10.1155/2020/6182654.

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Sex classification is a challenging open problem in computer vision. It is useful from statistics up to people recognition on surveillance video. So far, the best performance can be achieved by using 3D cameras, but this approach requires the use of some especial hardware. Other 2D approaches achieve good results on normal situations but fail when the person wears loose clothing and carries bags or the camera angle changes as they rely on calculating borders, silhouettes, or the energy of the person in the image. This work aims to provide a novel sex classification methodology based on the creation of a virtual skeleton for each individual from 2D images and video; then, the distances between some points of the skeleton are measured and work as input of a sex classifier. This improves the results since clothing, bags, and the camera angle affect little the skeletonization process.

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Banks, Sarah, Lori White, Amir Behnamian, Zhaohua Chen, Benoit Montpetit, Brian Brisco, Jon Pasher, and Jason Duffe. "Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests." Remote Sensing 11, no. 6 (March 19, 2019): 670. http://dx.doi.org/10.3390/rs11060670.

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To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here.

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Ni Ni, Soe, J. Tian, Pina Marziliano, and Hong-Tym Wong. "Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images." Journal of Ophthalmology 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/942367.

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Optical coherence tomography is a high resolution, rapid, and noninvasive diagnostic tool for angle closure glaucoma. In this paper, we present a new strategy for the classification of the angle closure glaucoma using morphological shape analysis of the iridocorneal angle. The angle structure configuration is quantified by the following six features: (1) mean of the continuous measurement of the angle opening distance; (2) area of the trapezoidal profile of the iridocorneal angle centered at Schwalbe's line; (3) mean of the iris curvature from the extracted iris image; (4) complex shape descriptor, fractal dimension, to quantify the complexity, or changes of iridocorneal angle; (5) ellipticity moment shape descriptor; and (6) triangularity moment shape descriptor. Then, the fuzzyknearest neighbor (fkNN) classifier is utilized for classification of angle closure glaucoma. Two hundred and sixty-four swept source optical coherence tomography (SS-OCT) images from 148 patients were analyzed in this study. From the experimental results, the fkNN reveals the best classification accuracy (99.11±0.76%) and AUC (0.98±0.012) with the combination of fractal dimension and biometric parameters. It showed that the proposed approach has promising potential to become a computer aided diagnostic tool for angle closure glaucoma (ACG) disease.

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Guo, Changjun, Zongbao Liu, Yangbo Xu, Xingchen Li, Yuan Zhu, and Xiangyang Xu. "Supramalleolar Osteotomy Combined With an Intra-articular Osteotomy for the Reconstruction of Malunited Medial Impacted Ankle Fractures." Foot & Ankle International 39, no. 12 (September 6, 2018): 1457–63. http://dx.doi.org/10.1177/1071100718795309.

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Background: Malunion of a medial impacted ankle fracture may cause varus ankle deformity. This retrospective study examined the use of supramalleolar osteotomy combined with an intra-articular osteotomy in patients with malunited medial impacted ankle fractures. Methods: Twenty-four patients with malunited medial impacted ankle fracture were treated between January 2011 and December 2014. Using Weber’s classification, 10 had type A fractures and 14 had type B, and with the AO classification, 20 had 44A2 and 4 had 44B3. All of these patients had varus ankle deformity. Supramalleolar osteotomy combined with an intra-articular osteotomy was performed. The visual analog scale (VAS) for pain during daily activities, the Olerud and Molander Scale and the modified Takakura classification stage were used to determine the clinical outcomes and a radiographic analysis was performed. Results: The radiographic parameters, including the tibial ankle surface (TAS) angle and talar tilt angle (TTA), showed significant differences between the preoperative and follow-up assessments. The mean tibial lateral surface (TLS) did not show a significant change. The average Olerud and Molander Scale score improved significantly from 56.4 ± 6.21 preoperatively to 77.0 ± 6.11 at the latest follow-up ( P < .01). The mean VAS decreased significantly from 6.7 ± 0.8 preoperatively to 3.1 ± 0.6 at the latest follow-up ( P < .01). No significant difference in the modified Takakura classification stage was observed between the preoperative assessment and the last follow-up. Conclusions: The use of a supramalleolar osteotomy combined with an intra-articular osteotomy was an effective option for the treatment of malunited medial impacted ankle fractures associated with varus ankle deformity. Level of Evidence: Level IV, retrospective case series.

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Saleem, Farah, and Zubair H. Awiasi. "Determination of Sagittal Skeletal Discrepancy through Beta Angle: A Lateral Cephalometric Study." Orthodontic Journal of Nepal 10, no. 3 (December 31, 2020): 40–43. http://dx.doi.org/10.3126/ojn.v10i3.35492.

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Introduction: Accurate classification and treatment planning relies on correct diagnosis of skeletal and dentalrelationships. Commonly used measurements used to classify sagittal relationship ANB and Wit’s appraisal are not without potential inherent problems which might lead to less accurate classification of sagittal dysplasia. To avoid these problems, a new approach Beta angle was introduced by Baik in 2004. Beta angle as it involves different landmarks of classifying anterior-posterior relationships is said to be devoid of those problems thus more reliable. We conducted a cross sectional study to measure the angle among patients presenting in Nishtar Institute of Dentistry Multan Pakistan. Materials and Method: Ninety pretreatment cephalometric x-rays of patients between ages 12 to 30 years were selected and studied. They were divided into three classes based on ANB angle and Wit’s appraisal. For the measurement of Beta angle, a line was drawn from the center of condyle (C) to point A and other to point B. A third line joining A to B was drawn. A line from point A perpendicular to line C-B was drawn and angle was measured between this perpendicular and line joining A-B. ANOVA was used to compare means of three groups. Pearson correlational coefficient was used to correlate relationship between Beta angle and ANB angle. Result: The results showed Beta angle ranged between 27° and 34° for class I. Subjects having angle less than 27° can be classified as skeletal class II and those with angles larger than 34° as skeletal class III subjects. Conclusion: Beta angle is reliable method for assessing and classifying sagittal skeletal discrepancies

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Wang, Kuan, Zhen Deng, Zhengyan Li, Huihao Wang, and Hongsheng Zhan. "The Influence of Natural Head Position on the Cervical Sagittal Alignment." Journal of Healthcare Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/2941048.

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Introduction. This study investigated the relationship between the parameters related to the natural head position and cervical segmental angles and alignment of patients with neck pain. Material and Methods. The lateral radiographs of the cervical spine were collected from 103 patients and were used to retrospectively analyze the correlation between the natural head position, cervical local sagittal angles, and alignment. Sagittal measurements were as follows: cervical curvature classification, slope of McGregor’s line (McGS), local sagittal angles (C0–C2 angle, C2–C5 angle, C5–C7 angle, and C2–C7 angle), T1 slope, center of gravity of the head to sagittal vertical axis (CG–C7 SVA), and local sagittal alignment (C0–C2 SVA and C2–C7 SVA). Results. McGS was significantly correlated to C0–C2 angle (r=0.57), C0–C2 SVA (r=−0.53), C2–C7 SVA (r=−0.28), and CG–C7 SVA (r=−0.47). CG–C7 SVA was also significantly correlated to curvature type (r=0.27), C5–C7 angle (r=−0.37), and C2–C7 angle (r=−0.39). Conclusions. A backward shift with an extended head position may accompany a relatively normal curvature of the cervical spine. The effect of posture control in relieving abnormal mechanical state of the cervical spine needs to be further confirmed by biomechanical analysis.

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LUO, FULIN, JIAMIN LIU, HONG HUANG, and YUMEI LIU. "HYPERSPECTRAL IMAGE CLASSIFICATION USING LOCAL SPECTRAL ANGLE-BASED MANIFOLD LEARNING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 06 (September 2014): 1450016. http://dx.doi.org/10.1142/s0218001414500165.

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Locally linear embedding (LLE) depends on the Euclidean distance (ED) to select the k-nearest neighbors. However, the ED may not reflect the actual geometry structure of data, which may lead to the selection of ineffective neighbors. The aim of our work is to make full use of the local spectral angle (LSA) to find proper neighbors for dimensionality reduction (DR) and classification of hyperspectral remote sensing data. At first, we propose an improved LLE method, called local spectral angle LLE (LSA-LLE), for DR. It uses the ED of data to obtain large-scale neighbors, then utilizes the spectral angle to get the exact neighbors in the large-scale neighbors. Furthermore, a local spectral angle-based nearest neighbor classifier (LSANN) has been proposed for classification. Experiments on two hyperspectral image data sets demonstrate the effectiveness of the presented methods.

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Isobe, Shinnosuke, Satoshi Tamura, Satoru Hayamizu, Yuuto Gotoh, and Masaki Nose. "Multi-Angle Lipreading with Angle Classification-Based Feature Extraction and Its Application to Audio-Visual Speech Recognition." Future Internet 13, no. 7 (July 15, 2021): 182. http://dx.doi.org/10.3390/fi13070182.

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Recently, automatic speech recognition (ASR) and visual speech recognition (VSR) have been widely researched owing to the development in deep learning. Most VSR research works focus only on frontal face images. However, assuming real scenes, it is obvious that a VSR system should correctly recognize spoken contents from not only frontal but also diagonal or profile faces. In this paper, we propose a novel VSR method that is applicable to faces taken at any angle. Firstly, view classification is carried out to estimate face angles. Based on the results, feature extraction is then conducted using the best combination of pre-trained feature extraction models. Next, lipreading is carried out using the features. We also developed audio-visual speech recognition (AVSR) using the VSR in addition to conventional ASR. Audio results were obtained from ASR, followed by incorporating audio and visual results in a decision fusion manner. We evaluated our methods using OuluVS2, a multi-angle audio-visual database. We then confirmed that our approach achieved the best performance among conventional VSR schemes in a phrase classification task. In addition, we found that our AVSR results are better than ASR and VSR results.

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Ahmed, Solaiman, Tanveer Ahmed Bhuiyan, Taiki Kishi, Manabu Nii, and Syoji Kobashi. "Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot." Applied Sciences 11, no. 16 (August 5, 2021): 7230. http://dx.doi.org/10.3390/app11167230.

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We propose a single sensor-based activity classification method where the Poincare plot was introduced to analyze the variance of the angle between acceleration vector with gravity calculated from the raw accelerometer data for human activity classification. Two datasets named ‘Human Activity Recognition’ and ‘MHealth dataset’ were used to develop the model to classify activity from low to vigorous intensity activities and posture estimation. Short-term and long-term variability analyzing the property of the Poincare plot was used to classify activities according to the vibrational intensity of body movement. Commercially available Actigraph’s activity classification metric ‘count’ resembled value was used to compare the feasibility of the proposed classification algorithm. In the case of the HAR dataset, laying, sitting, standing, and walking activities were classified. Poincare plot parameters SD1, SD2, and SDRR of angle in the case of angle variance analysis and the mean count of X-, Y-, and Z-axis were fitted to a support vector machine (SVM) classifier individually and jointly. The variance- and count-based methods have 100% accuracy in the static–dynamic classification. Laying activity classification has 100% accuracy from other static conditions in the proposed method, whereas the count-based method has 98.08% accuracy with 10-fold cross-validation. In the sitting–standing classification, the proposed angle-based algorithm shows 88% accuracy, whereas the count-based approach has 58% accuracy with a support vector machine classifier with 10-fold cross-validation. In the classification of the variants of dynamic activities with the MHealth dataset, the accuracy for angle variance-based and count-based methods is 100%, in both cases, for fivefold cross validation with SVM classifiers.

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Wokes, Stephen Mark, Phil Palmer, and Mark Roberts. "Classification of Two-Dimensional Fixed-Sun-Angle Solar Sail Trajectories." Journal of Guidance, Control, and Dynamics 31, no. 5 (September 2008): 1249–58. http://dx.doi.org/10.2514/1.34466.

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Mo, Moon-Jung, and Wook-Hyun Kim. "A Block Classification and Rotation Angle Extraction for Document Image." KIPS Transactions:PartB 9B, no. 4 (August 1, 2002): 509–16. http://dx.doi.org/10.3745/kipstb.2002.9b.4.509.

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ZHAO, Hai-Ying, Yi-Fan YANG, and Zheng-Guang XU. "3D Facial Gender Classification Based on Multi-angle LBP Feature." Acta Automatica Sinica 38, no. 9 (2012): 1544. http://dx.doi.org/10.3724/sp.j.1004.2012.01544.

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Liu, Y., S. J. Frasier, and R. E. McIntosh. "Measurement and classification of low-grazing-angle radar sea spikes." IEEE Transactions on Antennas and Propagation 46, no. 1 (1998): 27–40. http://dx.doi.org/10.1109/8.655448.

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Singer, A., Z. Zhao, Y. Shkolnisky, and R. Hadani. "Viewing Angle Classification of Cryo-Electron Microscopy Images Using Eigenvectors." SIAM Journal on Imaging Sciences 4, no. 2 (January 2011): 723–59. http://dx.doi.org/10.1137/090778390.

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Roberts, Paul L., and Jules S. Jaffe. "Application of multiple angle acoustic scatter to remote fish classification." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3211. http://dx.doi.org/10.1121/1.2933389.

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Bui, Timothy, Carlito Lagman, Brittany Voth, Seung Lee, Natalie Barnette, Lawrance Chung, Cheng Chen, Quinton Gopen, and Isaac Yang. "A Systematic Review and Classification System for Cerebellopontine Angle Lipomas." Journal of Neurological Surgery Part B: Skull Base 78, S 01 (March 2, 2017): S1—S156. http://dx.doi.org/10.1055/s-0037-1600861.

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Siddiqi, Abdul Rahman, and Shahrul Naim Sidek. "Estimation of continuous thumb angle and force using electromyogram classification." International Journal of Advanced Robotic Systems 13, no. 5 (September 8, 2016): 172988141665817. http://dx.doi.org/10.1177/1729881416658179.

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Moghimi, Sasan, Ali Torkashvand, Massood Mohammadi, Mehdi Yaseri, Luke J. Saunders, Shan C. Lin, and Robert N. Weinreb. "Classification of primary angle closure spectrum with hierarchical cluster analysis." PLOS ONE 13, no. 7 (July 23, 2018): e0199157. http://dx.doi.org/10.1371/journal.pone.0199157.

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Miura, Hideharu, Shuichi Ozawa, Tsubasa Enosaki, Masahiro Hayata, Kiyoshi Yamada, and Yasushi Nagata. "Gantry angle classification with a fluence map in intensity-modulated radiotherapy for prostate cases using machine learning." Polish Journal of Medical Physics and Engineering 24, no. 4 (December 1, 2018): 165–69. http://dx.doi.org/10.2478/pjmpe-2018-0023.

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Abstract We investigated the gantry-angle classifier performance with a fluence map using three machine-learning algorithms, and compared it with human performance. Eighty prostate cases were investigated using a seven-field-intensity modulated radiotherapy treatment (IMRT) plan with beam angles of 0°, 50°, 100°, 155°, 205°, 260°, and 310°. The k-nearest neighbor (k-NN), logistic regression (LR), and support vector machine (SVM) algorithms were used. In the observer test, three radiotherapists assessed the gantry angle classification in a blind manner. The precision and recall rates were calculated for the machine learning and observer test. The average precision rate of the k-NN and LR algorithms were 94.8% and 97.9%, respectively. The average recall rate of the k-NN and LR algorithms were 94.3% and 97.9%, respectively. The SVM had 100% precision and recall rates. The gantry angles of 0°, 155°, and 205° had an accuracy of 100% in all algorithms. In the observer test, average precision and recall rates were 82.6% and 82.6%, respectively. All observers could easily classify the gantry angles of 0°, 155°, and 205° with a high degree of accuracy. Misclassifications occurred in gantry angles of 50°, 100°, 260°, and 310°. Machine learning could better classify gantry angles for prostate IMRT than human beings. In particular, the SVM algorithm had a perfect classification of 100%.

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Journal articles on the topic 'Angle Classification'

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