Deep Learning and Cnns in Ophthalmology: Toward Accurate and Explainable Diagnosis - Kamal, Anum; Fatma, Nausheen; Dubey, Swati; Isha Mansoori, Mohammad; Asim Jamil, Mohd; Haq, Imtiyazul; Tandon, Dheeraj; Jaishree, Jaishree (Journal of Neonatal Surgery, 2025)
Advancements in deep learning, particularly CNN, have significantly enhanced diagnostic capabilities in ophthalmology by enabling accurate detection and classification of retinal and ocular diseases. These models have shown promising results in identifying diabetic retinopathy, glaucoma, age-related macular degeneration, and other vision-threatening conditions using fundus and OCT images. However, despite their high performance, the "black-box" nature of CNNs presents challenges in clinical adoption due to limited interpretability. Recent research is now emphasizing explainable AI (XAI) techniques to bridge this gap, offering transparency in decision-making through saliency maps, heatmaps, and attention mechanisms. This abstract highlights the role of CNNs in improving ophthalmic diagnosis, while advocating for explainability to ensure trust, accountability, and effective integration into real-world clinical practice. Early detection and management of ocular diseases are essential for improving patient outcomes in ophthalmology. This study presents a deep learning-based framework for the automated prediction, classification, and severity assessment of multiple eye conditions using ocular images. Leveraging the power of artificial intelligence (AI) and machine learning (ML), particularly convolutional neural networks (CNNs), the proposed system analyzes fundus photographs, OCT scans, and retinal images to identify diseases such as diabetic retinopathy, glaucoma, age-related macular degeneration, and cataracts. The model is trained on a large dataset of labeled images, enabling it to learn critical visual features indicative of each condition. In addition to disease classification, the framework incorporates severity analysis through image segmentation and quantitative evaluation of lesion characteristics like size, shape, and location. This dual output—diagnosis and severity score—empowers clinicians to make informed decisions and prioritize treatment. Furthermore, the system supports remote diagnostics, expanding access to ophthalmic care. By offering an accurate, explainable, and non-invasive diagnostic solution, this approach enhances clinical workflow and patient care in modern ophthalmology..