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Târcoveanu F, Leon F, Lisa C, Curteanu S, Feraru A, Ali K, Anton N. The use of artificial neural networks in studying the progression of glaucoma. Sci Rep 2024; 14:19597. [PMID: 39179625 PMCID: PMC11344130 DOI: 10.1038/s41598-024-70748-1] [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: 05/10/2024] [Accepted: 08/20/2024] [Indexed: 08/26/2024] Open
Abstract
In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilities and support physicians in diagnosing and treating patients. This paper focuses on modelling glaucoma evolution because it requires early diagnosis, individualized treatment, and lifelong monitoring. Glaucoma is a chronic, progressive, irreversible, multifactorial optic neuropathy that primarily affects elderly individuals. It is important to emphasize that the processed data are taken from medical records, unlike other studies in the literature that rely on image acquisition and processing. Although more challenging to handle, this approach has the advantage of including a wide range of parameters in large numbers, which can highlight their potential influence. Artificial neural networks are used to study glaucoma progression, designed through successive trials for near-optimal configurations using the NeuroSolutions and PyTorch frameworks. Furthermore, different problems are formulated to demonstrate the influence of various structural and functional parameters on the study of glaucoma progression. Optimal neural networks were obtained using a program written in Python using the PyTorch deep learning framework. For various tasks, very small errors in training and validation, under 5%, were obtained. It has been demonstrated that very good results can be achieved, making them credible and useful for medical practice.
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Affiliation(s)
- Filip Târcoveanu
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy "Gr. T. Popa" Iasi, University Street No 16, 700115, Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, 27 Mangeron Street, 700050, Iasi, Romania
| | - Cătălin Lisa
- Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection "Cristofor Simionescu", "Gheorghe Asachi" Technical University of Iasi, 73 Mangeron Street, 700050, Iasi, Romania
| | - Silvia Curteanu
- Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection "Cristofor Simionescu", "Gheorghe Asachi" Technical University of Iasi, 73 Mangeron Street, 700050, Iasi, Romania.
| | - Andreea Feraru
- Faculty of Economic Science, "Vasile Alecsandri" University of Bacau, Calea Marasesti 156, 600115, Bacau, Romania
| | - Kashif Ali
- Countess of Chester Hospital, Liverpool Rd, Chester, CH21UL, UK
| | - Nicoleta Anton
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy "Gr. T. Popa" Iasi, University Street No 16, 700115, Iasi, Romania.
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Almidani L, Bradley C, Herbert P, Ramulu P, Yohannan J. The Impact of Social Vulnerability on Structural and Functional Glaucoma Severity, Worsening, and Variability. Ophthalmol Glaucoma 2024; 7:380-390. [PMID: 38636704 DOI: 10.1016/j.ogla.2024.03.008] [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: 02/15/2024] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE To determine the associations between social vulnerability index (SVI) and baseline severity, worsening, and variability of glaucoma, as assessed by visual field (VF) and OCT. DESIGN Retrospective longitudinal cohort study. PARTICIPANTS Adults with glaucoma or glaucoma suspect status in 1 or both eyes. Visual fields were derived from 7897 eyes from 4482 patients, while OCTs were derived from 6271 eyes from 3976 patients. All eyes had a minimum of 5 tests over follow-up using either the Humphrey Field Analyzer or the Cirrus HD-OCT. METHODS Social vulnerability index, which measures neighborhood-level environmental factors, was linked to patients' addresses at the census tract level. Rates of change in mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness were computed using linear regression. The slope of the regression line was used to assess worsening, while the standard deviation of residuals was used as a measure of variability. Multivariable linear mixed-effects models were used to investigate the impact of SVI on baseline, worsening, and variability in both MD and RNFL. We further explored the interaction effect of mean intraocular pressure (IOP) and SVI on worsening in MD and RNFL. MAIN OUTCOME MEASURES Glaucoma severity defined based on baseline MD and RNFL thickness. Worsening defined as MD and RNFL slope. Variability defined as the standard deviation of the residuals obtained from MD and RNFL slopes. RESULTS Increased (worse) SVI was significantly associated with worse baseline MD (β = -1.07 dB, 95% confidence interval [CI]: [-1.54, -0.60]), thicker baseline RNFL (β = 2.46 μm, 95% CI: [0.75, 4.17]), greater rates of RNFL loss (β = -0.12 μm, 95% CI: [-0.23, -0.02]), and greater VF variability (β = 0.16 dB, 95% CI: [0.07, 0.24]). Having worse SVI was associated with worse RNFL loss with increases in IOP (βinteraction = -0.07, 95% CI: [-0.12, -0.02]). CONCLUSIONS Increased SVI score is associated with worse functional (VF) loss at baseline, higher rates of structural (OCT) worsening over time, higher VF variability, and a greater effect of IOP on RNFL loss. Further studies are needed to enhance our understanding of these relationships and establish their cause. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Louay Almidani
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick Herbert
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jithin Yohannan
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland; Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Karimi A, Stanik A, Kozitza C, Chen A. Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach. Bioengineering (Basel) 2024; 11:577. [PMID: 38927813 PMCID: PMC11200568 DOI: 10.3390/bioengineering11060577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. METHODS We extracted structural data from control and glaucoma patients' electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. RESULTS The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. CONCLUSIONS This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.
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Affiliation(s)
- Alireza Karimi
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA; (A.S.); (C.K.); (A.C.)
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Ansel Stanik
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA; (A.S.); (C.K.); (A.C.)
| | - Cooper Kozitza
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA; (A.S.); (C.K.); (A.C.)
| | - Aiyin Chen
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA; (A.S.); (C.K.); (A.C.)
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Wang R, Bradley C, Herbert P, Hou K, Hager GD, Breininger K, Unberath M, Ramulu P, Yohannan J. Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability. Ophthalmol Glaucoma 2024; 7:222-231. [PMID: 38296108 DOI: 10.1016/j.ogla.2024.01.005] [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: 09/04/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials. DESIGN Retrospective cohort and simulation study. METHODS We included 1 eye per patient with baseline reliable VFs, OCT, clinical measures (demographics, intraocular pressure, and visual acuity), and 5 subsequent reliable VFs to forecast VF variability using DLMs and perform sample size estimates. We estimated sample size for 3 groups of eyes: all eyes (AE), low variability eyes (LVE: the subset of AE with a standard deviation of mean deviation [MD] slope residuals in the bottom 25th percentile), and DLM-predicted low variability eyes (DLPE: the subset of AE predicted to be low variability by the DLM). Deep learning models using only baseline VF/OCT/clinical data as input (DLM1), or also using a second VF (DLM2) were constructed to predict low VF variability (DLPE1 and DLPE2, respectively). Data were split 60/10/30 into train/val/test. Clinical trial simulations were performed only on the test set. We estimated the sample size necessary to detect treatment effects of 20% to 50% in MD slope with 80% power. Power was defined as the percentage of simulated clinical trials where the MD slope was significantly worse from the control. Clinical trials were simulated with visits every 3 months with a total of 10 visits. RESULTS A total of 2817 eyes were included in the analysis. Deep learning models 1 and 2 achieved an area under the receiver operating characteristic curve of 0.73 (95% confidence interval [CI]: 0.68, 0.76) and 0.82 (95% CI: 0.78, 0.85) in forecasting low VF variability. When compared with including AE, using DLPE1 and DLPE2 reduced sample size to achieve 80% power by 30% and 38% for 30% treatment effect, and 31% and 38% for 50% treatment effect. CONCLUSIONS Deep learning models can forecast eyes with low VF variability using data from a single baseline clinical visit. This can reduce sample size requirements, and potentially reduce the burden of future glaucoma clinical trials. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Ruolin Wang
- Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick Herbert
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kaihua Hou
- Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Gregory D Hager
- Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Katharina Breininger
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mathias Unberath
- Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jithin Yohannan
- Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland; Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Parakkel RR, Wong D, Li C, Cheong J, Nongpiur ME, Chong RS, Aung T, Schmetterer L, Liu X, Chua J. Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography. Transl Vis Sci Technol 2024; 13:9. [PMID: 38743409 PMCID: PMC11103739 DOI: 10.1167/tvst.13.5.9] [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: 06/27/2023] [Accepted: 03/20/2024] [Indexed: 05/16/2024] Open
Abstract
Purpose To assess the diagnostic performance and structure-function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma. Methods This cross-sectional pilot study analyzed 170 eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity. Results The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure-function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure-function association was similar when using RNFLT and RR. Conclusions RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice. Translational Relevance We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.
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Affiliation(s)
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Chi Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
| | - Jocelyn Cheong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Monisha Esther Nongpiur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Rachel Shujuan Chong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Xinyu Liu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
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Gupta V, Birla S, Varshney T, Somarajan BI, Gupta S, Gupta M, Panigrahi A, Singh A, Gupta D. In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence. Indian J Ophthalmol 2024; 72:339-346. [PMID: 38146977 PMCID: PMC11001234 DOI: 10.4103/ijo.ijo_1456_23] [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: 06/04/2023] [Revised: 08/18/2023] [Accepted: 10/06/2023] [Indexed: 12/27/2023] Open
Abstract
PURPOSE To predict the presence of angle dysgenesis on anterior-segment optical coherence tomography (ADoA) by using deep learning (DL) and to correlate ADoA with mutations in known glaucoma genes. PARTICIPANTS In total, 800 high-definition anterior-segment optical coherence tomography (AS-OCT) images were included, of which 340 images were used to build the machine learning (ML) model. Images used to build the ML model included 170 scans of primary congenital glaucoma (16 patients), juvenile-onset open-angle glaucoma (62 patients), and adult-onset primary open-angle glaucoma eyes (37 patients); the rest were controls (n = 85). The genetic validation dataset consisted of another 393 images of patients with known mutations that were compared with 320 images of healthy controls. METHODS ADoA was defined as the absence of Schlemm's canal, the presence of hyperreflectivity over the region of the trabecular meshwork, or a hyperreflective membrane. DL was used to classify a given AS-OCT image as either having angle dysgenesis or not. ADoA was then specifically looked for on AS-OCT images of patients with mutations in the known genes for glaucoma. RESULTS The final prediction, which was a consensus-based outcome from the three optimized DL models, had an accuracy of >95%, a specificity of >97%, and a sensitivity of >96% in detecting ADoA in the internal test dataset. Among the patients with known gene mutations, ( MYOC, CYP1B1, FOXC1, and LTBP2 ) ADoA was observed among all the patients in the majority of the images, compared to only 5% of the healthy controls. CONCLUSION ADoA can be objectively identified using models built with DL.
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Affiliation(s)
- Viney Gupta
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Shweta Birla
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Jawaharlal Nehru University, New Delhi, India
| | - Toshit Varshney
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Bindu I Somarajan
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Shikha Gupta
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Mrinalini Gupta
- Department of Biomedical Engineering Technical University of Munich, Munich, Germany
| | - Arnav Panigrahi
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Abhishek Singh
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Jawaharlal Nehru University, New Delhi, India
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Wang R, Bradley C, Herbert P, Hou K, Ramulu P, Breininger K, Unberath M, Yohannan J. Deep learning-based identification of eyes at risk for glaucoma surgery. Sci Rep 2024; 14:599. [PMID: 38182701 PMCID: PMC10770345 DOI: 10.1038/s41598-023-50597-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024] Open
Abstract
To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retrospective study. 4898 unique eyes from 4038 adult glaucoma or glaucoma-suspect patients who underwent surgery for uncontrolled glaucoma (trabeculectomy, tube shunt, xen, or diode surgery) between 2013 and 2021, or did not undergo glaucoma surgery but had 3 or more ophthalmology visits. We constructed a DLM to predict the occurrence of glaucoma surgery within various time horizons from a baseline visit. Model inputs included spatially oriented visual field (VF) and optical coherence tomography (OCT) data as well as clinical and demographic features. Separate DLMs with the same architecture were trained to predict the occurrence of surgery within 3 months, within 3-6 months, within 6 months-1 year, within 1-2 years, within 2-3 years, within 3-4 years, and within 4-5 years from the baseline visit. Included eyes were randomly split into 60%, 20%, and 20% for training, validation, and testing. DLM performance was measured using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC). Shapley additive explanations (SHAP) were utilized to assess the importance of different features. Model prediction of surgery for uncontrolled glaucoma within 3 months had the best AUC of 0.92 (95% CI 0.88, 0.96). DLMs achieved clinically useful AUC values (> 0.8) for all models that predicted the occurrence of surgery within 3 years. According to SHAP analysis, all 7 models placed intraocular pressure (IOP) within the five most important features in predicting the occurrence of glaucoma surgery. Mean deviation (MD) and average retinal nerve fiber layer (RNFL) thickness were listed among the top 5 most important features by 6 of the 7 models. DLMs can successfully identify eyes requiring surgery for uncontrolled glaucoma within specific time horizons. Predictive performance decreases as the time horizon for forecasting surgery increases. Implementing prediction models in a clinical setting may help identify patients that should be referred to a glaucoma specialist for surgical evaluation.
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Affiliation(s)
- Ruolin Wang
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, 600 N Wolfe Street, Baltimore, MD, 21287, USA
| | - Patrick Herbert
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, 600 N Wolfe Street, Baltimore, MD, 21287, USA
| | - Kaihua Hou
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, 600 N Wolfe Street, Baltimore, MD, 21287, USA
| | - Katharina Breininger
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mathias Unberath
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jithin Yohannan
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, 600 N Wolfe Street, Baltimore, MD, 21287, USA.
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Asaoka R, Sugisaki K, Inoue T, Yoshikawa K, Kanamori A, Yamazaki Y, Ishikawa S, Uchida K, Iwase A, Araie M. Identifying central 10° visual subfield associated with future worsening of visual acuity in eyes with advanced glaucoma. Br J Ophthalmol 2023; 108:71-77. [PMID: 36418145 DOI: 10.1136/bjo-2022-321481] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND/AIMS To determine a cluster of test points: visual subfield (VSF) of Humphrey Field Analyzer 10-2 test (HFA 10-2) of which baseline sensitivities were associated with future worsening of visual acuity (VA) in eyes with advanced glaucoma. METHODS A total of 175 advanced glaucoma eyes of 175 advanced glaucoma patients with well controlled intraocular pressure (IOP), a mean deviation of the Humphrey Field Analyzer 24-2 (HFA 24-2) test ≤ -20 decibels and best corrected VA ≥20/40, were included. At baseline, HFA 24-2 and HFA 10-2 tests were performed along with VA measurements. All patients underwent prospective follow-up of 5 years, and VA was measured every 6 months. The Cox proportional hazards model was used to identify visual field sensitivities associated with deterioration of VA and also blindness. RESULTS Deterioration of VA and blindness were observed in 15.4% and 3.4% of the eyes, respectively. More negative total deviation (TD) values in the temporal papillomacular bundle VSF were significantly associated with deterioration in VA. Averages of the TD values in this area of the HFA 10-2 test had the most predictive power of future VA deterioration (OR: 0.92, p<0.001). A very similar tendency was observed for blindness. CONCLUSION In advanced glaucoma eyes with well-controlled IOP, careful attention is needed when the mean TD values in the temporal papillomacular bundle VSF, measured with a HFA 10-2 test is deteriorated. TD values of this VSF indicate higher risks for future deterioration of VA and also blindness.
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Affiliation(s)
- Ryo Asaoka
- Department of Ophthalmology, Seirei Hamamatsu Byoin, Hamamatsu, Shizuoka, Japan
- Seirei Christopher University, Hamamatsu, Japan
- Nanovision Research Division, Research Institute of Electronics, Shizuoka University, Shizuoka, Japan
- The Graduate School for the Creation of New Photonics Industries, Shizuoka, Japan
| | - Kenji Sugisaki
- Department of Ophthalmology, International University of Health and Welfare, Mita Hospital, Tokyo, Japan
- Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Toshihiro Inoue
- Department of Ophthalmology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | | | - Akiyasu Kanamori
- Department of Surgery, Division of Ophthalmology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | - Shinichiro Ishikawa
- Department of Ophthalmology, Saga University Faculty of Medicine, Saga, Japan
| | | | | | - Makoto Araie
- Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
- Sekikawa Hospital, Tokyo, Japan
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [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: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
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Hussain S, Chua J, Wong D, Lo J, Kadziauskiene A, Asoklis R, Barbastathis G, Schmetterer L, Yong L. Predicting glaucoma progression using deep learning framework guided by generative algorithm. Sci Rep 2023; 13:19960. [PMID: 37968437 PMCID: PMC10651936 DOI: 10.1038/s41598-023-46253-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/30/2023] [Indexed: 11/17/2023] Open
Abstract
Glaucoma is a slowly progressing optic neuropathy that may eventually lead to blindness. To help patients receive customized treatment, predicting how quickly the disease will progress is important. Structural assessment using optical coherence tomography (OCT) can be used to visualize glaucomatous optic nerve and retinal damage, while functional visual field (VF) tests can be used to measure the extent of vision loss. However, VF testing is patient-dependent and highly inconsistent, making it difficult to track glaucoma progression. In this work, we developed a multimodal deep learning model comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network, for glaucoma progression prediction. We used OCT images, VF values, demographic and clinical data of 86 glaucoma patients with five visits over 12 months. The proposed method was used to predict VF changes 12 months after the first visit by combining past multimodal inputs with synthesized future images generated using generative adversarial network (GAN). The patients were classified into two classes based on their VF mean deviation (MD) decline: slow progressors (< 3 dB) and fast progressors (> 3 dB). We showed that our generative model-based novel approach can achieve the best AUC of 0.83 for predicting the progression 6 months earlier. Further, the use of synthetic future images enabled the model to accurately predict the vision loss even earlier (9 months earlier) with an AUC of 0.81, compared to using only structural (AUC = 0.68) or only functional measures (AUC = 0.72). This study provides valuable insights into the potential of using synthetic follow-up OCT images for early detection of glaucoma progression.
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Affiliation(s)
- Shaista Hussain
- Institute of High Performance Computing, A*STAR, Singapore, Singapore.
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | | | - Aiste Kadziauskiene
- Clinic of Ears, Nose, Throat and Eye Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Department of Eye Diseases, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Rimvydas Asoklis
- Clinic of Ears, Nose, Throat and Eye Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Department of Eye Diseases, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - George Barbastathis
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore.
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.
- Department of Ophthalmology, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria.
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
| | - Liu Yong
- Institute of High Performance Computing, A*STAR, Singapore, Singapore
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Herbert P, Hou K, Bradley C, Hager G, Boland MV, Ramulu P, Unberath M, Yohannan J. Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data. Ophthalmol Glaucoma 2023; 6:466-473. [PMID: 36944385 PMCID: PMC10509314 DOI: 10.1016/j.ogla.2023.03.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/20/2023] [Accepted: 03/10/2023] [Indexed: 03/22/2023]
Abstract
PURPOSE To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN A retrospective cohort study. SUBJECTS In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). METHODS We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1 + VF2; (6) V2OC: VF1 + VF2 + Clinical1 + OCT1; (7) V3: VF1 + VF2 + VF3; and (8) V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES The AUC of DLMs when forecasting rapidly worsening eyes. RESULTS Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). CONCLUSIONS Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Patrick Herbert
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Kaihua Hou
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
| | - Greg Hager
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Michael V Boland
- Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
| | - Mathias Unberath
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Jithin Yohannan
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland; Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland.
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Thakur S, Dinh LL, Lavanya R, Quek TC, Liu Y, Cheng CY. Use of artificial intelligence in forecasting glaucoma progression. Taiwan J Ophthalmol 2023; 13:168-183. [PMID: 37484617 PMCID: PMC10361424 DOI: 10.4103/tjo.tjo-d-23-00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/03/2023] [Indexed: 07/25/2023] Open
Abstract
Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.
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Affiliation(s)
- Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Linh Le Dinh
- Institute of High Performance Computing, The Agency for Science, Technology and Research, Singapore
| | - Raghavan Lavanya
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yong Liu
- Institute of High Performance Computing, The Agency for Science, Technology and Research, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
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13
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Detecting disease progression in mild, moderate and severe glaucoma. Curr Opin Ophthalmol 2023; 34:168-175. [PMID: 36730773 DOI: 10.1097/icu.0000000000000925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to examine contemporary techniques for detecting the progression of glaucoma. We provide a general overview of detection principles and review evidence-based diagnostic strategies and specific considerations for detecting glaucomatous progression in patients with mild, moderate and severe disease. RECENT FINDINGS Diagnostic techniques and technologies for glaucoma have dramatically evolved in recent years, affording clinicians an expansive toolkit with which to detect glaucoma progression. Each stage of glaucoma, however, presents unique diagnostic challenges. In mild disease, either structural or functional changes can develop first in disease progression. In moderate disease, structural or functional changes can occur either in tandem or in isolation. In severe disease, standard techniques may fail to detect further disease progression, but such detection can still be measured using other modalities. SUMMARY Detecting disease progression is central to the management of glaucoma. Glaucomatous progression has both structural and functional elements, both of which must be carefully monitored at all disease stages to determine when interventions are warranted.
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A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach. Sci Rep 2023; 13:1041. [PMID: 36658309 PMCID: PMC9852268 DOI: 10.1038/s41598-023-28003-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Abstract
Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8-68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93-0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72-0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63-0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care.
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Ma D, Pasquale LR, Girard MJA, Leung CKS, Jia Y, Sarunic MV, Sappington RM, Chan KC. Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications. FRONTIERS IN OPHTHALMOLOGY 2023; 2:1057896. [PMID: 36866233 PMCID: PMC9976697 DOI: 10.3389/fopht.2022.1057896] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 04/16/2023]
Abstract
Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.
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Affiliation(s)
- Da Ma
- School of Medicine, Wake Forest University, Winston-Salem, NC, United States
- Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Michaël J. A. Girard
- Ophthalmic Engineering & Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Institute for Molecular and Clinical Ophthalmology, Basel, Switzerland
| | | | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, United States
| | - Marinko V. Sarunic
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Rebecca M. Sappington
- School of Medicine, Wake Forest University, Winston-Salem, NC, United States
- Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Kevin C. Chan
- Departments of Ophthalmology and Radiology, Neuroscience Institute, NYU Grossman School of Medicine, NYU Langone Health, New York University, New York, NY, United States
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY, United States
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Perkins SW, Wu AK, Singh RP. Predictors of limited early response to anti-vascular endothelial growth factor therapy in neovascular age-related macular degeneration with machine learning feature importance. Saudi J Ophthalmol 2022; 36:315-321. [PMID: 36276255 PMCID: PMC9583356 DOI: 10.4103/sjopt.sjopt_73_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/02/2022] [Accepted: 06/02/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Patients with neovascular age-related macular degeneration (nAMD) have varying responses to anti-vascular endothelial growth factor injections. Limited early response (LER) after three monthly loading doses is associated with poor long-term vision outcomes. This study predicts LER in nAMD and uses feature importance analysis to explain how baseline variables influence predicted LER risk. METHODS Baseline age, best visual acuity (BVA), central subfield thickness (CST), and baseline and 3 months intraretinal fluid (IRF) and subretinal fluid (SRF) for 286 eyes were collected in a retrospective clinical chart review. At month 3, LER was defined as the presence of fluid, while early response (ER) was the absence thereof. Decision tree classification and feature importance methods determined the influence of baseline age, BVA, CST, IRF, and SRF, on predicted LER risk. RESULTS One hundred and sixty-seven eyes were LERs and 119 were ERs. The algorithm achieved area under the curve = 0.66 in predicting LER. Baseline SRF was most important for predicting LER while age, BVA, CST, and IRF were somewhat less important. Nonlinear trends were observed between baseline variables and predicted LER risk. Zones of increased predicted LER risk were identified, including age <74 years, and CST <290 or >350 μm, IRF >750 nL, and SRF >150 nL. CONCLUSION These findings explain baseline variable importance for predicting LER and show SRF to be the most important. The nonlinear impact of baseline variables on predicted risk is shown, increasing understanding of LER and aiding clinicians in assessing personalized LER risk.
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Affiliation(s)
- Scott W. Perkins
- Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA
| | - Anna K. Wu
- Case Western Reserve University School of Medicine, Cleveland, Ohio, USA,Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Rishi P. Singh
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, USA,Address for correspondence: Dr. Rishi P. Singh, 9500 Euclid Avenue, Desk I32, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA. E-mail:
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Li F, Su Y, Lin F, Li Z, Song Y, Nie S, Xu J, Chen L, Chen S, Li H, Xue K, Che H, Chen Z, Yang B, Zhang H, Ge M, Zhong W, Yang C, Chen L, Wang F, Jia Y, Li W, Wu Y, Li Y, Gao Y, Zhou Y, Zhang K, Zhang X. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest 2022; 132:157968. [PMID: 35642636 PMCID: PMC9151694 DOI: 10.1172/jci157968] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/12/2022] [Indexed: 02/05/2023] Open
Abstract
BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.
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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, China
| | - Yuandong Su
- State Key Laboratory of Biotherapy and Center for Translational Innovations, West China Hospital and Sichuan University, Chengdu, China.,PKU-MUST Center for Future Technology, Faculty of Medicine, Macao University of Science and Technology, Macau, China
| | - Fengbin Lin
- 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, China
| | - Zhihuan Li
- PKU-MUST Center for Future Technology, Faculty of Medicine, Macao University of Science and Technology, Macau, China
| | - Yunhe Song
- 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, China
| | - Sheng Nie
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease and Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jie Xu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China
| | - Linjiang Chen
- Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shiyan Chen
- Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Hao Li
- Department of Ophthalmology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Kanmin Xue
- Nuffield Laboratory of Ophthalmology, Department of Clinical Neurosciences, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Huixin Che
- He Eye Specialist Hospital, Shenyang, Liaoning Province, China
| | - Zhengui Chen
- Jiangmen Xinhui Aier New Hope Eye Hospital, Jiangmen, Guangdong, China
| | - Bin Yang
- Department of Ophthalmology, Zigong Third People's Hospital, Zigong, China
| | - Huiying Zhang
- Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou, China
| | - Ming Ge
- Department of Ophthalmology and Optometry, Guizhou Nursing Vocational College, Guiyang, China
| | - Weihui Zhong
- Department of Ophthalmology, Guangzhou Development District Hospital, Guangzhou, China
| | - Chunman Yang
- Department of Ophthalmology, The Second Affiliated Hospital of Guizhou Medical University, Kaili, China
| | - Lina Chen
- Department of Ophthalmology, The Third People's Hospital of Dalian, Dalian, Liaoning Province, China
| | - Fanyin Wang
- Department of Ophthalmology, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China
| | - Yunqin Jia
- Department of Ophthalmology, Dali Bai Autonomous Prefecture People's Hospital, Dali, China
| | - Wanlin Li
- Department of Ophthalmology, Wuwei People's Hospital, Wuwei, Gansu Province, China
| | - Yuqing Wu
- Department of Ophthalmology, Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, China
| | - Yingjie Li
- Department of Ophthalmology, The First Hospital of Nanchang City, Nanchang, China
| | - Yuanxu Gao
- PKU-MUST Center for Future Technology, Faculty of Medicine, Macao University of Science and Technology, Macau, China.,State Key Laboratory of Lunar and Planetary Sciences, Macao University of Science and Technology, Taipa, Macau, China
| | - Yong Zhou
- Clinical Research Institute, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Kang Zhang
- PKU-MUST Center for Future Technology, Faculty of Medicine, Macao University of Science and Technology, Macau, 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, China
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