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Shi C, Lee J, Shi D, Wang G, Yuan F, Zee BCY. Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy. BMJ Open Ophthalmol 2024; 9:e001594. [PMID: 39256168 PMCID: PMC11429265 DOI: 10.1136/bmjophth-2023-001594] [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: 11/27/2023] [Accepted: 08/25/2024] [Indexed: 09/12/2024] Open
Abstract
OBJECTIVES Train an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess different models for the GON classification. METHODS All the images were obtained from the hospital for training and 10-fold cross-validation. Two methods were used to improve the classification performance: (1) using images labelled with the additional results of OCT as the reference standard and (2) generating models using retinal features from the entire images, the region of interest (ROI) of the optic disc, and the ROI of the macula, and the combination of all the features. RESULTS Overall, we collected 1338 images with paired OCT scans. In 10-fold validation, ARIA achieved sensitivities of 92.2 %, 92.7% and 85.7%, specificities of 88.8%, 86.7% and 80.2% and accuracies of 90.6%, 89.9% and 83.1% using the retinal features from the entire images, the ROI of the optic disc and the ROI of the macula, respectively. We found the model combining all the features has the best classification performance and obtained a sensitivity of 92.5%, a specificity of 92.1% and an accuracy of 92.4%, which is significantly different from other models (p<0.001). CONCLUSION We used two methods to improve the classification performance and found the best model to detect glaucoma on colour fundus retinal images. It can become a cost-effective and relatively more accurate glaucoma screening tool than conventional methods.
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Affiliation(s)
- Chuying Shi
- Center for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jack Lee
- Center for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Di Shi
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Gechun Wang
- Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Ophthalmology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, People's Republic of China
| | - Fei Yuan
- Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Benny Chung-Ying Zee
- Center for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
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2
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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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3
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Ha A, Sun S, Kim YK, Jeoung JW, Kim HC, Park KH. Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects. Br J Ophthalmol 2024; 108:927-932. [PMID: 37918891 DOI: 10.1136/bjo-2022-323167] [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: 12/30/2022] [Accepted: 09/03/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND/AIMS To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients. METHODS Datasets of 12 458 GS eyes were reviewed. Two hundred and ten eyes (105 eyes showing NTG conversion and 105 without conversion), followed up for a minimum of 7 years during which intraocular pressure (IOP) was lower than 21 mm Hg, were included. The features of two fundus images (optic disc photography and red-free retinal nerve fibre layer (RNFL) photography) were extracted by convolutional auto encoder. The extracted features as well as 15 clinical features including age, sex, IOP, spherical equivalent, central corneal thickness, axial length, average circumpapillary RNFL thickness, systolic/diastolic blood pressure and body mass index were used to predict NTG conversion. Prediction was performed using three machine-learning classifiers (ie, XGBoost, Random Forest, Gradient Boosting) with different feature combinations. RESULTS All three algorithms achieved high diagnostic accuracy for NTG conversion prediction. The AUCs ranged from 0.987 (95% CI 0.978 to 1.000; Random Forest trained with both fundus images and clinical features) and 0.994 (95% CI 0.984 to 1.000; XGBoost trained with both fundus images and clinical features). XGBoost showed the best prediction performance for time to NTG conversion (mean squared error, 2.24). The top three important clinical features for time-to-conversion prediction were baseline IOP, diastolic blood pressure and average circumpapillary RNFL thickness. CONCLUSION DL models, trained with both fundus images and clinical data, showed the potential to predict whether and when normotensive GS patients will show conversion to NTG.
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Affiliation(s)
- Ahnul Ha
- Department of Ophthalmology, Jeju National University, Jeju, Korea (the Republic of)
| | - Sukkyu Sun
- Department of AI Software Convergence, Dongguk University, Seoul, Korea (the Republic of)
| | - Young Kook Kim
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Jin Wook Jeoung
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Hee Chan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Ki Ho Park
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
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Xie X, Wang F, Wang G, Zhu W, Du X, Wang H. Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs. Artif Intell Med 2024; 152:102864. [PMID: 38640702 DOI: 10.1016/j.artmed.2024.102864] [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: 01/20/2023] [Revised: 01/28/2024] [Accepted: 03/30/2024] [Indexed: 04/21/2024]
Abstract
Predicting the response of tumor cells to anti-tumor drugs is critical to realizing cancer precision medicine. Currently, most existing methods ignore the regulatory relationships between genes and thus have unsatisfactory predictive performance. In this paper, we propose to predict anti-tumor drug efficacy via learning the activity representation of tumor cells based on a priori knowledge of gene regulation networks (GRNs). Specifically, the method simulates the cellular biosystem by synthesizing a cell-gene activity network and then infers a new low-dimensional activity representation for tumor cells from the raw high-dimensional expression profile. The simulated cell-gene network mainly comprises known gene regulatory networks collected from multiple resources and fuses tumor cells by linking them to hotspot genes that are over- or under-expressed in them. The resulting activity representation could not only reflect the shallow expression profile (hotspot genes) but also mines in-depth information of gene regulation activity in tumor cells before treatment. Finally, we build deep learning models on the activity representation for predicting drug efficacy in tumor cells. Experimental results on the benchmark GDSC dataset demonstrate the superior performance of the proposed method over SOTA methods with the highest AUC of 0.954 in the efficacy label prediction and the best R2 of 0.834 in the regression of half maximal inhibitory concentration (IC50) values, suggesting the potential value of the proposed method in practice.
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Affiliation(s)
- Xinping Xie
- School of mathematics and physics, Anhui Jianzhu University, Hefei, China
| | - Fengting Wang
- School of mathematics and physics, Anhui Jianzhu University, Hefei, China; Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei, China
| | - Guanfu Wang
- School of mathematics and physics, Anhui Jianzhu University, Hefei, China
| | - Weiwei Zhu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei, China; Zhongqi AI Lab, Hefei, China
| | - Xiaodong Du
- Experimental Teaching Center, Hefei University, Hefei, China
| | - Hongqiang Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei, China; Zhongqi AI Lab, Hefei, China.
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5
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Mastropasqua L, Agnifili L, Brescia L, Figus M, Posarelli C, Oddone F, Giammaria S, Sacchi M, Pavan M, Innocenti DD, Olivotto V, Sensi SL, Mastropasqua R. A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study. Graefes Arch Clin Exp Ophthalmol 2024; 262:149-160. [PMID: 37530849 PMCID: PMC10805808 DOI: 10.1007/s00417-023-06170-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 06/30/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023] Open
Abstract
PURPOSE To distinguish functioning from failed filtration blebs (FBs) implementing a deep learning (DL) model on slit-lamp images. METHODS Retrospective, cross-sectional, multicenter study for development and validation of an artificial intelligence classification algorithm. The dataset consisted of 119 post-trabeculectomy FB images of whom we were aware of the surgical outcome. The ground truth labels were annotated and images splitted into three outcome classes: complete (C) or qualified success (Q), and failure (F). Images were prepared implementing various data cleaning and data transformations techniques. A set of DL models were trained using different ResNet architectures as the backbone. Transfer and ensemble learning were then applied to obtain a final combined model. Accuracy, sensitivity, specificity, area under the ROC curve, and area under the precision-recall curve were calculated to evaluate the final model. Kappa coefficient and P value on the accuracy measure were used to prove the statistical significance level. RESULTS The DL approach reached good results in unraveling FB functionality. Overall, the model accuracy reached a score of 74%, with a sensitivity of 74% and a specificity of 87%. The area under the ROC curve was 0.8, whereas the area under the precision-recall curve was 0.74. The P value was equal to 0.00307, and the Kappa coefficient was 0.58. CONCLUSIONS All considered metrics supported that the final DL model was able to discriminate functioning from failed FBs, with good accuracy. This approach could support clinicians in the patients' management after glaucoma surgery in absence of adjunctive clinical data.
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Affiliation(s)
- Leonardo Mastropasqua
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
| | - Luca Agnifili
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy.
| | - Lorenza Brescia
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
| | - Michele Figus
- Ophthalmology Unit, Department of Surgical, Medical, Molecular Pathology and Critical Care Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Chiara Posarelli
- Ophthalmology Unit, Department of Surgical, Medical, Molecular Pathology and Critical Care Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | | | - Sara Giammaria
- IRCCS Fondazione Bietti, Via Livenza, 3, 00198, Rome, Italy
| | - Matteo Sacchi
- University Eye Clinic, San Giuseppe Hospital, University of Milan, Milan, Italy
| | - Marco Pavan
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Dante Degli Innocenti
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Valentina Olivotto
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), "G. d'Annunzio" University of Chieti-Pescara, Via Dei Vestini 31, 66100, Chieti, Italy
| | - Rodolfo Mastropasqua
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), "G. d'Annunzio" University of Chieti-Pescara, Via Dei Vestini 31, 66100, Chieti, Italy
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Schmetterer L, Scholl H, Garhöfer G, Janeschitz-Kriegl L, Corvi F, Sadda SR, Medeiros FA. Endpoints for clinical trials in ophthalmology. Prog Retin Eye Res 2023; 97:101160. [PMID: 36599784 DOI: 10.1016/j.preteyeres.2022.101160] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 12/22/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023]
Abstract
With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.
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Affiliation(s)
- Leopold Schmetterer
- Singapore Eye Research Institute, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore; Academic Clinical Program, Duke-NUS Medical School, Singapore; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore; Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.
| | - Hendrik Scholl
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Gerhard Garhöfer
- Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria
| | - Lucas Janeschitz-Kriegl
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Federico Corvi
- Eye Clinic, Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Italy
| | - SriniVas R Sadda
- Doheny Eye Institute, Los Angeles, CA, USA; Department of Ophthalmology, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Felipe A Medeiros
- Vision, Imaging and Performance Laboratory, Department of Ophthalmology, Duke Eye Center, Duke University, Durham, NC, USA
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Bowd C, Belghith A, Rezapour J, Christopher M, Jonas JB, Hyman L, Fazio MA, Weinreb RN, Zangwill LM. Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia. J Glaucoma 2023; 32:841-847. [PMID: 37523623 DOI: 10.1097/ijg.0000000000002267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/18/2023] [Indexed: 08/02/2023]
Abstract
PRCIS An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia. BACKGROUND/AIMS To evaluate the diagnostic accuracy of a multimodal DL classifier using wide OCT optic nerve head cube scans in eyes with and without axial high myopia. MATERIALS AND METHODS Three hundred seventy-one primary open angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia [axial length (AL) ≤ 26 mm] and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL > 26 mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: (1) texture-based en face image, (2) retinal nerve fiber layer (RNFL) thickness map image, and (3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves were used to compare model accuracy. RESULTS Adjusted area under the receiver operating curve for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models [0.83 (0.79, 0.86) for texture-based en face image; 0.84 (0.81, 0.87) for RNFL thickness map; and 0.68 (0.61, 0.74) for cSLO image; all P ≤ 0.05]. Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy [0.89 (0.86, 0.92)] compared with texture en face image [0.83 (0.78, 0.85)], RNFL [0.85 (0.81, 0.86)] and cSLO image models [0.69 (0.63, 0.76)] (all P ≤ 0.05). CONCLUSIONS Combining OCT-based RNFL thickness maps with texture-based en face images showed a better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.
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Affiliation(s)
- Christopher Bowd
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center
| | - Akram Belghith
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center
| | - Jasmin Rezapour
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center
- Department of Ophthalmology, University Medical Center of the Johannes Gutenberg University Mainz
| | - Mark Christopher
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center
| | - Jost B Jonas
- Department of Ophthalmology, Heidelberg University, Mannheim, Germany
| | - Leslie Hyman
- Vickie and Jack Farber Vision Research Center, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA
| | - Massimo A Fazio
- Department of Ophthalmology and Visual Sciences, The University of Alabama at Birmingham, Birmingham, AL
| | - Robert N Weinreb
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center
| | - Linda M Zangwill
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center
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Pucchio A, Krance S, Pur DR, Bassi A, Miranda R, Felfeli T. The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review. Eur J Ophthalmol 2023; 33:1816-1833. [PMID: 36426575 PMCID: PMC10469503 DOI: 10.1177/11206721221140948] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 11/01/2022] [Indexed: 08/31/2023]
Abstract
PURPOSE This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. METHODS A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. RESULTS A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. CONCLUSION The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.
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Affiliation(s)
- Aidan Pucchio
- School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Saffire Krance
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Daiana R Pur
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Arshpreet Bassi
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Rafael Miranda
- Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Toronto, Ontario, Canada
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Tina Felfeli
- Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology and Visual Sciences, University of Toronto, Toronto, Ontario, Canada
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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9
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Gherghel D, De Moraes G. Barriers to IOP-independent treatments in glaucoma clinical trials. Eye (Lond) 2023; 37:1955-1957. [PMID: 36400853 PMCID: PMC10333330 DOI: 10.1038/s41433-022-02305-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022] Open
Affiliation(s)
- Doina Gherghel
- College of Health and Life Sciences, Vascular Research Laboratory, Aston University, Birmingham, UK.
- Division of Cardiovascular Sciences, Manchester University, Manchester, UK.
| | - Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York City, NY, USA
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10
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Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
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11
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Chen JS, Baxter SL, van den Brandt A, Lieu A, Camp AS, Do JL, Welsbie DS, Moghimi S, Christopher M, Weinreb RN, Zangwill LM. Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression. J Glaucoma 2023; 32:151-158. [PMID: 36877820 PMCID: PMC9996451 DOI: 10.1097/ijg.0000000000002163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/19/2023] [Indexed: 03/08/2023]
Abstract
PRCIS We updated a clinical decision support tool integrating predicted visual field (VF) metrics from an artificial intelligence model and assessed clinician perceptions of the predicted VF metric in this usability study. PURPOSE To evaluate clinician perceptions of a prototyped clinical decision support (CDS) tool that integrates visual field (VF) metric predictions from artificial intelligence (AI) models. METHODS Ten ophthalmologists and optometrists from the University of California San Diego participated in 6 cases from 6 patients, consisting of 11 eyes, uploaded to a CDS tool ("GLANCE", designed to help clinicians "at a glance"). For each case, clinicians answered questions about management recommendations and attitudes towards GLANCE, particularly regarding the utility and trustworthiness of the AI-predicted VF metrics and willingness to decrease VF testing frequency. MAIN OUTCOMES AND MEASURES Mean counts of management recommendations and mean Likert scale scores were calculated to assess overall management trends and attitudes towards the CDS tool for each case. In addition, system usability scale scores were calculated. RESULTS The mean Likert scores for trust in and utility of the predicted VF metric and clinician willingness to decrease VF testing frequency were 3.27, 3.42, and 2.64, respectively (1=strongly disagree, 5=strongly agree). When stratified by glaucoma severity, all mean Likert scores decreased as severity increased. The system usability scale score across all responders was 66.1±16.0 (43rd percentile). CONCLUSIONS A CDS tool can be designed to present AI model outputs in a useful, trustworthy manner that clinicians are generally willing to integrate into their clinical decision-making. Future work is needed to understand how to best develop explainable and trustworthy CDS tools integrating AI before clinical deployment.
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Affiliation(s)
- Jimmy S Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | | | - Alexander Lieu
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Andrew S Camp
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Jiun L Do
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Derek S Welsbie
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Sasan Moghimi
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute
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12
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Lemij HG, de Vente C, Sánchez CI, Vermeer KA. Characteristics of a large, labeled dataset for the training of artificial intelligence for glaucoma screening with fundus photographs. OPHTHALMOLOGY SCIENCE 2023; 3:100300. [PMID: 37113471 PMCID: PMC10127130 DOI: 10.1016/j.xops.2023.100300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 02/12/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023]
Abstract
Purpose Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. Objective To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG). Design Cross-sectional study. Subjects Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy. Methods Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as "RG," "no referable glaucoma (NRG)," or "ungradable (UG)." In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features. Main Outcome Measures Qualitative features in eyes with RG. Results The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet's AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2-86.7)% and 96.4 (96.3-96.5)%, respectively. Of all gradable eyes (n = 111 183; 97.62%) the prevalence of RG was 4.38%. The most common features of RG were the appearance of the neuroretinal rim (NRR) inferiorly and superiorly. Conclusions A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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13
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Wang J, Zhang Y, Meng X, Liu G. Application of diffusion tensor imaging technology in glaucoma diagnosis. Front Neurosci 2023; 17:1125638. [PMID: 36816120 PMCID: PMC9932933 DOI: 10.3389/fnins.2023.1125638] [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: 12/16/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023] Open
Abstract
Glaucoma is the first major category of irreversible blinding eye illnesses worldwide. Its leading cause is the death of retinal ganglion cells and their axons, which results in the loss of vision. Research indicates that glaucoma affects the optic nerve and the whole visual pathway. It also reveals that degenerative lesions caused by glaucoma can be found outside the visual pathway. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that can investigate the complete visual system, including alterations in the optic nerve, optic chiasm, optic tract, lateral geniculate nuclear, and optic radiation. In order to provide a more solid foundation for the degenerative characteristics of glaucoma, this paper will discuss the standard diagnostic techniques for glaucoma through a review of the literature, describe the use of DTI technology in glaucoma in humans and animal models, and introduce these techniques. With the advancement of DTI technology and its coupling with artificial intelligence, DTI represents a potential future for MRI technology in glaucoma research.
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Affiliation(s)
| | | | | | - Gang Liu
- Department of Ophthalmology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, Hubei, China
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14
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Bao XL, Sun YJ, Zhan X, Li GY. Orbital and eyelid diseases: The next breakthrough in artificial intelligence? Front Cell Dev Biol 2022; 10:1069248. [PMID: 36467418 PMCID: PMC9716028 DOI: 10.3389/fcell.2022.1069248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/08/2022] [Indexed: 12/07/2023] Open
Abstract
Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.
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Affiliation(s)
- Xiao-Li Bao
- Department of Ophthalmology, Second Hospital of Jilin University, Changchun, China
| | - Ying-Jian Sun
- Department of Ophthalmology, Second Hospital of Jilin University, Changchun, China
| | - Xi Zhan
- Department of Engineering, The Army Engineering University of PLA, Nanjing, China
| | - Guang-Yu Li
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, China
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15
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Use of Artificial Neural Networks to Predict the Progression of Glaucoma in Patients with Sleep Apnea. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126061] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Aim: To construct neural models to predict the progression of glaucoma in patients with sleep apnea. Materials and Methods: Modeling the use of neural networks was performed using the Neurosolutions commercial simulator. The built databases gather information on a group of patients with primitive open-angle glaucoma and normal-tension glaucoma, who have been associated with sleep apnea syndrome and various stages of disease severity. The data within the database were divided as follows: 65 were used in the neural network training stage and 8 were kept for the validation stage. In total, 21 parameters were selected as input parameters for neural models including: age of patients, BMI (body mass index), systolic and diastolic blood pressure, intraocular pressure, central corneal thickness, corneal biomechanical parameters (IOPcc, HC, CRF), AHI, desaturation index, nocturnal oxygen saturation, remaining AHI, type of apnea, and associated general conditions (diabetes, hypertension, obesity, COPD). The selected output parameters are: c/d ratio, modified visual field parameters (MD, PSD), ganglion cell layer thickness. Forward-propagation neural networks (multilayer perceptron) were constructed with a layer of hidden neurons. The constructed neural models generated the output values for these data. The obtained results were then compared with the experimental values. Results: The best results were obtained during the training stage with the ANN network (21:35:4). If we consider a 25% confidence interval, we find that very good results are obtained during the validation stage, except for the average GCL thickness, for which the errors are slightly higher. Conclusions: Excellent results were obtained during the validation stage, which support the results obtained in other studies in the literature that strengthen the connection between sleep apnea syndrome and glaucoma changes.
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Kaskar OG, Wells-Gray E, Fleischman D, Grace L. Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings. Sci Rep 2022; 12:8518. [PMID: 35595794 PMCID: PMC9122936 DOI: 10.1038/s41598-022-12270-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 04/18/2022] [Indexed: 11/09/2022] Open
Abstract
Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the eye. These algorithms require carefully curated datasets with access to ocular images. In the current study, we have modeled and evaluated classifiers to predict self-reported glaucoma using a single, easily obtained ocular feature (intraocular pressure (IOP)) and non-ocular features (age, gender, race, body mass index, systolic and diastolic blood pressure, and comorbidities). The classifiers were trained on publicly available data of 3015 subjects without a glaucoma diagnosis at the time of enrollment. 337 subjects subsequently self-reported a glaucoma diagnosis in a span of 1–12 years after enrollment. The classifiers were evaluated on the ability to identify these subjects by only using their features recorded at the time of enrollment. Support vector machine, logistic regression, and adaptive boosting performed similarly on the dataset with F1 scores of 0.31, 0.30, and 0.28, respectively. Logistic regression had the highest sensitivity at 60% with a specificity of 69%. Predictive classifiers using primarily non-ocular features have the potential to be used for identifying suspected glaucoma in non-eye care settings, including primary care. Further research into finding additional features that improve the performance of predictive classifiers is warranted.
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Affiliation(s)
- Omkar G Kaskar
- North Carolina State University, Raleigh, NC, 27695, USA
| | | | - David Fleischman
- University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Landon Grace
- North Carolina State University, Raleigh, NC, 27695, USA.
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Ittoop SM, Jaccard N, Lanouette G, Kahook MY. The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma. J Glaucoma 2022; 31:137-146. [PMID: 34930873 DOI: 10.1097/ijg.0000000000001972] [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: 05/20/2021] [Accepted: 12/10/2021] [Indexed: 11/26/2022]
Abstract
Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. Artificial intelligence (AI) and AI-enabled workflows have been proposed as a plausible solution. Applications derived from this field of computer science can improve the quality and robustness of insights obtained from clinical data that can enhance the clinician's approach to patient care. This review clarifies key terms and concepts used in AI literature, discusses the current advances of AI in glaucoma, elucidates the clinical advantages and challenges to implementing this technology, and highlights potential future applications.
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Affiliation(s)
- Sabita M Ittoop
- The George Washington University Medical Faculty Associates, Washington, DC
| | | | | | - Malik Y Kahook
- Sue Anschutz-Rodgers Eye Center, The University of Colorado School of Medicine, Aurora, CO
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18
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Tan Z, Zhu Z, He Z, He M. Artificial Intelligence in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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