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Chiang YY, Chen CL, Chen YH. Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations. Biomedicines 2024; 12:1394. [PMID: 39061968 PMCID: PMC11274657 DOI: 10.3390/biomedicines12071394] [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: 04/28/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
OBJECTIVES This study aimed to use deep learning to identify glaucoma and normal eyes in groups with high myopia using fundus photographs. METHODS Patients who visited Tri-Services General Hospital from 1 November 2018 to 31 October 2022 were retrospectively reviewed. Patients with high myopia (spherical equivalent refraction of ≤-6.0 D) were included in the current analysis. Meanwhile, patients with pathological myopia were excluded. The participants were then divided into the high myopia group and high myopia glaucoma group. We used two classification models with the convolutional block attention module (CBAM), an attention mechanism module that enhances the performance of convolutional neural networks (CNNs), to investigate glaucoma cases. The learning data of this experiment were evaluated through fivefold cross-validation. The images were categorized into training, validation, and test sets in a ratio of 6:2:2. Grad-CAM visual visualization improved the interpretability of the CNN results. The performance indicators for evaluating the model include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS A total of 3088 fundus photographs were used for the deep-learning model, including 1540 and 1548 fundus photographs for the high myopia glaucoma and high myopia groups, respectively. The average refractive power of the high myopia glaucoma group and the high myopia group were -8.83 ± 2.9 D and -8.73 ± 2.6 D, respectively (p = 0.30). Based on a fivefold cross-validation assessment, the ConvNeXt_Base+CBAM architecture had the best performance, with an AUC of 0.894, accuracy of 82.16%, sensitivity of 81.04%, specificity of 83.27%, and F1 score of 81.92%. CONCLUSIONS Glaucoma in individuals with high myopia was identified from their fundus photographs.
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Affiliation(s)
- Yen-Ying Chiang
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan;
| | - Ching-Long Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
| | - Yi-Hao Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
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Swaminathan U, Daigavane S. Unveiling the Potential: A Comprehensive Review of Artificial Intelligence Applications in Ophthalmology and Future Prospects. Cureus 2024; 16:e61826. [PMID: 38975538 PMCID: PMC11227442 DOI: 10.7759/cureus.61826] [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: 05/25/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the field of ophthalmology. This comprehensive review examines the current applications of AI in ophthalmology, highlighting its significant contributions to diagnostic accuracy, treatment efficacy, and patient care. AI technologies, such as deep learning algorithms, have demonstrated exceptional performance in the early detection and diagnosis of various eye conditions, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Additionally, AI has enhanced the analysis of ophthalmic imaging techniques like optical coherence tomography (OCT) and fundus photography, facilitating more precise disease monitoring and management. The review also explores AI's role in surgical assistance, predictive analytics, and personalized treatment plans, showcasing its potential to revolutionize clinical practice and improve patient outcomes. Despite these advancements, challenges such as data privacy, regulatory hurdles, and ethical considerations remain. The review underscores the need for continued research and collaboration among clinicians, researchers, technology developers, and policymakers to address these challenges and fully harness the potential of AI in improving eye health worldwide. By integrating AI with teleophthalmology and developing AI-driven wearable devices, the future of ophthalmic care promises enhanced accessibility, efficiency, and efficacy, ultimately reducing the global burden of visual impairment and blindness.
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Affiliation(s)
- Uma Swaminathan
- Ophthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sachin Daigavane
- Ophthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Harris A, Verticchio Vercellin A, Weinreb RN, Khawaja A, MacGregor S, Pasquale LR. Lessons From The Glaucoma Foundation Think Tank 2023: A Patient-Centric Approach to Glaucoma. J Glaucoma 2024; 33:e1-e14. [PMID: 38129952 DOI: 10.1097/ijg.0000000000002353] [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: 09/11/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
PRCIS The main takeaways also included that BIG DATA repositories and AI are important combinatory tools to foster novel strategies to prevent and stabilize glaucoma and, in the future, recover vision loss from the disease. PURPOSE To summarize the main topics discussed during the 28th Annual Glaucoma Foundation Think Tank Meeting "A Patient-Centric Approach to Glaucoma" held in New York on June 9 and 10, 2023. METHODS The highlights of the sessions on BIG DATA, genetics, modifiable lifestyle risk factors, female sex hormones, and neuroprotection in the field of primary open angle glaucoma (POAG) were summarized. RESULTS The researchers discussed the importance of BIG DATA repositories available at national and international levels for POAG research, including the United Kingdom Biobank. Combining genotyped large cohorts worldwide, facilitated by artificial intelligence (AI) and machine-learning approaches, led to the milestone discovery of 312 genome-wide significant disease loci for POAG. While these loci could be combined into a polygenic risk score with clinical utility, Think Tank meeting participants also provided analytical epidemiological evidence that behavioral risk factors modify POAG polygenetic risk, citing specific examples related to caffeine and alcohol use. The impact of female sex hormones on POAG pathophysiology was discussed, as was neuroprotection and the potential use of AI to help mitigate specific challenges faced in clinical trials and speed approval of neuroprotective agents. CONCLUSIONS The experts agreed on the importance of genetics in defining individual POAG risk and highlighted the additional crucial role of lifestyle, gender, blood pressure, and vascular risk factors. The main takeaways also included that BIG DATA repositories and AI are important combinatory tools to foster novel strategies to prevent and stabilize glaucoma and, in the future, recover vision loss from the disease.
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Affiliation(s)
- Alon Harris
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY
| | | | - Robert N Weinreb
- Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, UC San Diego, La Jolla, CA
| | - Anthony Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Stuart MacGregor
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY
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Bekollari M, Dettoraki M, Stavrou V, Glotsos D, Liaparinos P. Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device. Diagnostics (Basel) 2024; 14:349. [PMID: 38396388 PMCID: PMC10888400 DOI: 10.3390/diagnostics14040349] [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: 01/10/2024] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the "Elpis" General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.
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Affiliation(s)
- Marsida Bekollari
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece; (M.B.); (D.G.)
| | - Maria Dettoraki
- Department of Ophthalmology, “Elpis” General Hospital, 11522 Athens, Greece
| | - Valentina Stavrou
- Department of Ophthalmology, “Elpis” General Hospital, 11522 Athens, Greece
| | - Dimitris Glotsos
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece; (M.B.); (D.G.)
| | - Panagiotis Liaparinos
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece; (M.B.); (D.G.)
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Sala L, Prud’homme C, Guidoboni G, Szopos M, Harris A. The ocular mathematical virtual simulator: A validated multiscale model for hemodynamics and biomechanics in the human eye. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3791. [PMID: 37991116 PMCID: PMC10922164 DOI: 10.1002/cnm.3791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 10/22/2023] [Indexed: 11/23/2023]
Abstract
We present our continuous efforts from a modeling and numerical viewpoint to develop a powerful and flexible mathematical and computational framework called Ocular Mathematical Virtual Simulator (OMVS). The OMVS aims to solve problems arising in biomechanics and hemodynamics within the human eye. We discuss our contribution towards improving the reliability and reproducibility of computational studies by performing a thorough validation of the numerical predictions against experimental data. The OMVS proved capable of simulating complex multiphysics and multiscale scenarios motivated by the study of glaucoma. Furthermore, its modular design allows the continuous integration of new models and methods as the research moves forward, and supports the utilization of the OMVS as a promising non-invasive clinical investigation tool for personalized research in ophthalmology.
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Affiliation(s)
- Lorenzo Sala
- Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France
| | | | | | - Marcela Szopos
- Université Paris Cité, CNRS, MAP5, F-75006 Paris, France
| | - Alon Harris
- Icahn School of Medicine at Mount Sinai, New York (NY), USA
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Kurysheva NI, Rodionova OY, Pomerantsev AL, Sharova GA, Golubnitschaja O. Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy. EPMA J 2023; 14:527-538. [PMID: 37605656 PMCID: PMC10439872 DOI: 10.1007/s13167-023-00337-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/23/2023]
Abstract
Background Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression. Aim To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE). Methods The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC. Results and data interpretation in the framework of 3P medicine Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00337-1.
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Affiliation(s)
- Natalia I. Kurysheva
- The Ophthalmological Center of the Federal Medical and Biological Agency of the Russian Federation, 15 Gamalei Street, Moscow, Russian Federation 123098
| | - Oxana Y. Rodionova
- Federal Research Center for Chemical Physics RAS, 4, Kosygin Street, Moscow, Russian Federation 119991
| | - Alexey L. Pomerantsev
- Federal Research Center for Chemical Physics RAS, 4, Kosygin Street, Moscow, Russian Federation 119991
| | - Galina A. Sharova
- Ophthalmology Clinic of Dr. Belikova, 26/2, Budenny Avenue, Moscow, Russian Federation 105118
| | - Olga Golubnitschaja
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
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