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Li F, Wang D, Yang Z, Zhang Y, Jiang J, Liu X, Kong K, Zhou F, Tham CC, Medeiros F, Han Y, Grzybowski A, Zangwill LM, Lam DSC, Zhang X. The AI revolution in glaucoma: Bridging challenges with opportunities. Prog Retin Eye Res 2024; 103:101291. [PMID: 39186968 DOI: 10.1016/j.preteyeres.2024.101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Deming Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Zefeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Yinhang Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Jiaxuan Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Xiaoyi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Kangjie Kong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Fengqi Zhou
- Ophthalmology, Mayo Clinic Health System, Eau Claire, WI, USA.
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Felipe Medeiros
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Ying Han
- University of California, San Francisco, Department of Ophthalmology, San Francisco, CA, USA; The Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, CA, USA.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, CA, USA.
| | - Dennis S C Lam
- The International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
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Guo PY, Zhang X, Li F, Lin C, Nguyen A, Sakata R, Higashita R, Okamoto K, Yu M, Aihara M, Aung T, Lin S, Leung CKS. Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure. Br J Ophthalmol 2024; 108:1130-1136. [PMID: 38594062 PMCID: PMC11287563 DOI: 10.1136/bjo-2023-323860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 11/27/2023] [Indexed: 04/11/2024]
Abstract
AIMS To compare the diagnostic performance of 360° anterior segment optical coherence tomography assessment by applying normative percentile cut-offs versus iris trabecular contact (ITC) for detecting gonioscopic angle closure. METHODS In this multicentre study, 394 healthy individuals were included in the normative dataset to derive the age-specific and angle location-specific normative percentiles of angle open distance (AOD500) and trabecular iris space area (TISA500) which were measured every 10° for 360°. 119 healthy participants and 170 patients with angle closure by gonioscopy were included in the test dataset to investigate the diagnostic performance of three sets of criteria for detection of gonioscopic angle closure: (1) the 10th and (2) the 5th percentiles of AOD500/TISA500, and (3) ITC (ie, AOD500/TISA500=0 mm/mm2). The number of angle locations with angle closure defined by each set of the criteria for each eye was used to generate the receiver operating characteristic (ROC) curve for the discrimination between gonioscopic angle closure and open angle. RESULTS Of the three sets of diagnostic criteria examined, the area under the ROC curve was greatest for the 10th percentile of AOD500 (0.933), whereas the ITC criterion AOD500=0 mm showed the smallest area under the ROC (0.852) and the difference was statistically significant with or without adjusting for age and axial length (p<0.001). The criterion ≥90° of AOD500 below the 10th percentile attained the best sensitivity 87.6% and specificity 84.9% combination for detecting gonioscopic angle closure. CONCLUSIONS Applying the normative percentiles of angle measurements yielded a higher diagnostic performance than ITC for detecting angle closure on gonioscopy.
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Affiliation(s)
- Philip Yawen Guo
- Department of Ophthalmology, The University of Hong Kong, Pok Fu Lam, People's Republic of China
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Chen Lin
- Shenzhen Aier Eye Hospital, Shenzhen, China
| | - Anwell Nguyen
- Department of Ophthalmology, University of California San Francisco, San Francisco, California, USA
| | - Rei Sakata
- Ophthalmology, The University of Tokyo, Bunkyo-ku, Japan
| | | | | | - Marco Yu
- Singapore Eye Research Institute, Singapore
| | - Makoto Aihara
- Ophthalmology, Tokyo Daigaku Daigakuin Igakukei Kenkyuka Igakubu, Tokyo, Japan
| | - Tin Aung
- Glaucoma, Singapore National Eye Centre, Singapore
| | - Shan Lin
- Department of Ophthalmology, University of California San Francisco, San Francisco, California, USA
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Kurysheva NI, Rodionova OY, Pomerantsev AL, Sharova GA. [Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis]. Vestn Oftalmol 2024; 140:82-87. [PMID: 38962983 DOI: 10.17116/oftalma202414003182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach. The results found in the reviewed literature are contradictory, indicating that improvement of the AI models requires further research and a standardized approach. The use of neural networks for timely detection of glaucoma based on multimodal imaging will reduce the risk of blindness associated with glaucoma.
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Affiliation(s)
- N I Kurysheva
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- Ophthalmological Center of the Federal Medical-Biological Agency at the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
| | - O Ye Rodionova
- N.N. Semenov Federal Research Center for Chemical Physics, Moscow, Russia
| | - A L Pomerantsev
- N.N. Semenov Federal Research Center for Chemical Physics, Moscow, Russia
| | - G A Sharova
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- OOO Glaznaya Klinika Doktora Belikovoy, Moscow, Russia
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Jiang W, Yan Y, Cheng S, Wan S, Huang L, Zheng H, Tian M, Zhu J, Pan Y, Li J, Huang L, Wu L, Gao Y, Mao J, Cong Y, Wang Y, Deng Q, Shi X, Yang Z, Liu S, Zheng B, Yang Y. Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2497-2509. [PMID: 37730479 DOI: 10.1016/j.ultrasmedbio.2023.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.
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Affiliation(s)
- Weiyan Jiang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yulin Yan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Simin Cheng
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Shanshan Wan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Linying Huang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Hongmei Zheng
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Miao Tian
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Jian Zhu
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yumiao Pan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yuelan Gao
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Jiewen Mao
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yuyu Cong
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yujin Wang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Qian Deng
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Xiaoshuo Shi
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Zixian Yang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Siqi Liu
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Biqing Zheng
- School of Resources and Environmental Sciences of Wuhan University, Wuhan, Hubei Province, China
| | - Yanning Yang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
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Espinoza G, Iglesias K, Parra JC, Rodriguez-Una I, Serrano-Gomez S, Prada AM, Galvis V. Agreement and Reproducibility of Anterior Chamber Angle Measurements between CASIA2 Built-In Software and Human Graders. J Clin Med 2023; 12:6381. [PMID: 37835024 PMCID: PMC10573880 DOI: 10.3390/jcm12196381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023] Open
Abstract
PURPOSE This study evaluated the agreement and reproducibility of ACA measurements obtained using the built-in software of the CASIA2 (Version 3G.1) and the measurements derived from expert clinicians. METHODS Healthy volunteers underwent ophthalmological evaluation and AS-OCT examination. ACA measurements derived from automated and manual SS location were obtained using the CASIA2 automated software and clinician identification, respectively. The intraobserver, interobserver reproducibility, CASIA2-human grader reproducibility and CASIA2 repeatability were assessed using intraclass correlation coefficients (ICCs). RESULTS The study examined 58 eyes of 30 participants. The CASIA2 software showed excellent repeatability for all ACA parameters (ICC > 0.84). Intraobserver, interobserver, and CASIA2-human grader reproducibility were also excellent (ICC > 0.87). Interobserver agreement was high, except for nasal TISA500, differing between observers 1 and 2 (p < 0.05). The agreement between CASIA2 measurements and human graders was high, except for nasal TISA500, where observer 1 values were smaller (p < 0.05). CONCLUSION The CASIA2 built-in software reliably measures ACA parameters in healthy individuals, demonstrating high consistency. Although a small difference was observed in nasal TISA500 measurements, interobserver and CASIA2-human grader reproducibility remained excellent. Automated SS detection has the potential to facilitate evaluation and monitoring of primary angle closure disease.
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Affiliation(s)
- Gustavo Espinoza
- Centro Oftalmológico Virgilio Galvis, Floridablanca 681004, Santander, Colombia
- Fundación Oftalmológica de Santander, Floridablanca 681004, Santander, Colombia
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680002, Santander, Colombia
| | - Katheriene Iglesias
- Fundación Oftalmológica de Santander, Floridablanca 681004, Santander, Colombia
| | - Juan C. Parra
- Fundación Oftalmológica de Santander, Floridablanca 681004, Santander, Colombia
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680002, Santander, Colombia
| | - Ignacio Rodriguez-Una
- Instituto Universitario Fernández-Vega, Fundación de Investigación Oftalmológica, Universidad de Oviedo, 33012 Oviedo, Spain;
| | - Sergio Serrano-Gomez
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680002, Santander, Colombia
| | - Angelica M. Prada
- Centro Oftalmológico Virgilio Galvis, Floridablanca 681004, Santander, Colombia
- Fundación Oftalmológica de Santander, Floridablanca 681004, Santander, Colombia
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680002, Santander, Colombia
| | - Virgilio Galvis
- Centro Oftalmológico Virgilio Galvis, Floridablanca 681004, Santander, Colombia
- Fundación Oftalmológica de Santander, Floridablanca 681004, Santander, Colombia
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680002, Santander, Colombia
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Kurysheva NI, Rodionova OY, Pomerantsev AL, Sharova GA, Golubnitschaja O. Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy. EPMA J 2023; 14:527-538. [PMID: 37605656 PMCID: PMC10439872 DOI: 10.1007/s13167-023-00337-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/23/2023]
Abstract
Background Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression. Aim To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE). Methods The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC. Results and data interpretation in the framework of 3P medicine Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00337-1.
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Affiliation(s)
- Natalia I. Kurysheva
- The Ophthalmological Center of the Federal Medical and Biological Agency of the Russian Federation, 15 Gamalei Street, Moscow, Russian Federation 123098
| | - Oxana Y. Rodionova
- Federal Research Center for Chemical Physics RAS, 4, Kosygin Street, Moscow, Russian Federation 119991
| | - Alexey L. Pomerantsev
- Federal Research Center for Chemical Physics RAS, 4, Kosygin Street, Moscow, Russian Federation 119991
| | - Galina A. Sharova
- Ophthalmology Clinic of Dr. Belikova, 26/2, Budenny Avenue, Moscow, Russian Federation 105118
| | - Olga Golubnitschaja
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
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Eslami Y, Mousavi Kouzahkanan Z, Farzinvash Z, Safizadeh M, Zarei R, Fakhraie G, Vahedian Z, Mahmoudi T, Fadakar K, Beikmarzehei A, Tabatabaei SM. Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography. J Glaucoma 2023; 32:540-547. [PMID: 36897658 DOI: 10.1097/ijg.0000000000002194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 02/08/2023] [Indexed: 03/11/2023]
Abstract
PRCIS We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy. PURPOSE To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes. MATERIALS AND METHODS Anterior segment optical coherence tomography images were used for analysis with 5 different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction. RESULTS A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean ± SD age was 51.76 ± 15.15 years and 48.3% were males. MobileNet had the best performance in the model, in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99 ± 0.00, 0.77 ± 0.02, and 0.77 ± 0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95 ± 0.03, 0.83 ± 0.06, and 0.81 ± 0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test data set. CONCLUSION The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images.
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Affiliation(s)
- Yadollah Eslami
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Zahra Farzinvash
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mona Safizadeh
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Zarei
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Fakhraie
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Zakieh Vahedian
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Tahereh Mahmoudi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Kaveh Fadakar
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Seyed Mehdi Tabatabaei
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
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