Machine Learning Innovations in Ophthalmology
DOI:
https://doi.org/10.70356/jafotik.v3i1.51Keywords:
Artificial Intelligence, Machine Learning, OphthalmologyAbstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in ophthalmology has significantly advanced diagnostic precision and patient care. Leveraging diverse datasets such as Electronic Health Records (EHRs), Optical Coherence Tomography (OCT) images, and genomic data, AI-driven approaches have proven effective in diagnosing eye conditions and systemic diseases with ocular manifestations. This study reviews various applications of AI in ophthalmology, including fungal keratitis, diabetic retinopathy, glaucoma, and rare genetic disorders. Techniques such as Lasso regression, deep transfer learning, and Random Forest analysis have been employed to enhance diagnostic models and improve prediction accuracy. For example, deep transfer learning models like VGG19 and DenseNet have demonstrated superior performance in identifying diabetic retinopathy from OCT scans. Additionally, AI’s application in genomic studies has shown promising results in detecting genetic markers for rare diseases. The contributions of these studies extend beyond clinical applications, emphasizing AI’s role in personalized medicine, early disease detection, and improved treatment planning. By validating models across multiple centers, the scalability and consistency of AI solutions in real-world clinical environments are reinforced. This review underscores the transformative potential of AI and ML in shaping the future of ophthalmology, fostering more accurate diagnoses and personalized treatment strategies.
Downloads
References
Z. Wei et al., “Development and multi-center validation of machine learning model for early detection of fungal keratitis,” eBioMedicine, vol. 88, p. 104438, 2023, doi: https://doi.org/10.1016/j.ebiom.2023.104438.
M. M. Carlà, F. Giannuzzi, F. Boselli, and S. Rizzo, “Testing the power of Google DeepMind: Gemini versus ChatGPT 4 facing a European ophthalmology examination,” AJO Int., vol. 1, no. 3, p. 100063, 2024, doi: https://doi.org/10.1016/j.ajoint.2024.100063.
L. Wang, B. Li, J. Pan, C. Zhang, and T. Wang, “Optical coherence tomography image recognition of diabetic retinopathy based on deep transfer learning,” J. Radiat. Res. Appl. Sci., vol. 17, no. 3, p. 101026, 2024, doi: https://doi.org/10.1016/j.jrras.2024.101026.
A. A. Alzahrani and F. S. Alharithi, “Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing,” Alexandria Eng. J., vol. 106, no. June, pp. 582–593, 2024, doi: https://doi.org/10.1016/j.aej.2024.08.056.
A. Maunz et al., “Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration,” Ophthalmol. Sci., vol. 3, no. 4, p. 100319, 2023, doi: https://doi.org/10.1016/j.xops.2023.100319.
J. Joseph, S. Senith, A. A. Kirubaraj, and J. S. R. Ramson, “Machine Learning for Prediction of Wait Times in Outpatient Clinic,” Procedia Comput. Sci., vol. 215, no. 2022, pp. 230–239, 2022, doi: https://doi.org/10.1016/j.procs.2022.12.026.
Y. Xu et al., “Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma,” Heliyon, vol. 10, no. 13, p. e33813, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e33813.
F. J. Dongil, M. Ortiz, D. Jimeno, V. Mayen, and E. Garcia-Martin, “Contribution to Early Diagnosis of Multiple Sclerosis using Optical Coherence Tomography and Support Vector Machine,” Procedia Comput. Sci., vol. 225, pp. 4922–4929, 2023, doi: https://doi.org/10.1016/j.procs.2023.10.492.
L. Álvarez-rodríguez, A. Pueyo, J. De Moura, and E. Vilades, “Fully automatic deep convolutional approaches for the screening of neurodegeneratives diseases using multi-view OCT images,” Artif. Intell. Med., vol. 158, no. October, p. 103006, 2024, doi: https://doi.org/10.1016/j.artmed.2024.103006.
K. Sangeetha, K. Valarmathi, T. Kalaichelvi, and S. Subburaj, “Measurement : Sensors A broad study of machine learning and deep learning techniques for diabetic retinopathy based on feature extraction , detection and classification,” Meas. Sensors, vol. 30, no. April, p. 100951, 2023, doi: https://doi.org/10.1016/j.measen.2023.100951.
A. Ran and C. Y. Cheung, “Deep Learning-Based Optical Coherence Tomography and Optical An Updated Summary,” Asia-Pacific J. Ophthalmol., vol. 10, no. 3, pp. 253–260, 2021, doi: https://doi.org/10.1097/APO.0000000000000405.
L. Ferro et al., “The upcoming role of Arti fi cial Intelligence ( AI ) for retinal and glaucomatous diseases,” J. Optom., vol. 15, pp. S50–S57, 2022, doi: https://doi.org/10.1016/j.optom.2022.08.001.
B. P. Makala and D. M. Kumar, “Highlights Earlier glaucoma prediction and prevents irreversible damage . Cup and nerves are perspective enhanced with optimized DnCNN algorithm Nerve damages are analyzed for the both the eye of a patient Predicted the glaucoma stages using optimized machine learning,” Results Eng., p. 104220, 2025, doi: https://doi.org/10.1016/j.rineng.2025.104220.
M. J. Menten et al., “Exploring Healthy Retinal Aging with Deep Learning,” pp. 1–10, 2023, doi: 10.1016/j.xops.2023.100294.
K. Nowomiejska et al., “Residual Attention Network for distinction between visible optic disc drusen and healthy optic discs,” vol. 176, no. August 2023, 2024, doi: https://doi.org/10.1016/j.optlaseng.2024.108056.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Amirah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
