Deep Learning For Eye Disease Diagnosis: Fundus and Oct Review For Diabetic Retinopathy, Glaucoma, and Cataract
Sonal S. More, Altaf Osman MulaniIntroduction:
Deep learning is revolutionizing the field of ophthalmology, both in detecting and categorizing eye diseases and in managing them. It is noted that ultrasound has contributed to the accuracy and efficiency of clinical diagnosis.
Methods:
A literature review of deep learning in ophthalmology was conducted, and convolutional neural networks, transfer learning, ensemble models, and hybrid models were identified. The papers that mentioned fundus photography and optical coherence tomography were given special attention to aid in disease diagnosis.
Results:
These results suggested that deep learning models had higher diagnostic accuracy and could not only detect earlier but also yield fewer false positives than traditional methods. They have also been used as a valuable aid in screening for retinal characteristics to support automatic, scalable screening, particularly in resource-constrained settings. Explainable AI has also enhanced trust between clinicians and patients by introducing transparency into AI.
Discussion:
It is an encouraging finding, but there are privacy concerns, the need to generalize to a population, and the sheer size of the annotated data. Ethical and regulatory issues should also be reviewed and made safe for clinical use.
Conclusion:
Deep learning can transform eye care, making it more accessible, more accurate in diagnosis, and more beneficial to patients. However, prudent incorporation into clinical practice and research is required to extract the full potential.