Machine Learning and Vision: Advancing the Frontiers of Diabetic Cataract Management

Abstract

This comprehensive review explores the integration of machine learning (ML) in managing diabetic cataracts. It discusses the potential application of ML to identify novel methodologies for early detection, diagnosis, and therapeutic interventions. The review also addresses clinical translation challenges, including pharmacokinetics properties and ethical considerations. The approach toward cataractogenesis, therefore, has to be from a holistic viewpoint, bringing oxidative stress and metabolic disturbances to the top of importance. It outlines the important requirements, including continued research, diversified datasets, and uses interdisciplinary collaborations in making improvements in ML models and thereafter bridging the gap between computational promise and clinical implication, with the aim to help in the maximization of patient care in the management of diabetic cataract. A literature search through databases like PubMed and Scopus focusing on understanding of current innovations, challenges, and future directions in employing ML in diabetic cataract management was undertaken. This review has explored both recent and foundational studies in order to explain the development and gaps of current research with an aim to enhance outcomes of patient care by promoting future investigation. Key findings revealed a wide application of ML in ophthalmology including treatment identification, cataract detection and grading, and improving the surgical outcomes. However, this is accompanied by some obstacles, including risk of bias, concerns regarding artificial intelligence application as a diagnostic tool, and legal regulations. ML promises extraordinary developments in the treatment of diabetic cataracts through betterment in diagnosis, treatment, and patient care. With this, it is full of clinical translation and ethical challenges, yet there is recognition in general that continuous model refinement and interdisciplinary collaboration, along with the expansion of the two identified key elements in enhancing patient outcomes, are essential for this to continue.