Review Article
Author Details :
Volume : 10, Issue : 4, Year : 2024
Article Page : 196-207
https://doi.org/10.18231/j.ijooo.2024.036
Abstract
In the domains of ocular oncology and oculoplasty, machine learning (ML) has become a game-changing technology, providing previously unheard-of levels of precision in diagnosis, treatment planning, and outcome prediction. Using imaging modalities, genomic data, and clinical characteristics, this chapter investigates the integration of machine learning algorithms in the detection and treatment of ocular tumours, including retinoblastoma and uveal melanoma. Through predictive modelling and real-time decision-making, it also emphasises how ML might improve surgical outcomes in oculoplasty, including orbital reconstruction and eyelid correction. Automated examination of fundus photographs, histological slides, and 3D imaging has been made possible by methods like deep learning and natural language processing, which have improved individualised therapeutic approaches and decreased diagnostic errors. Additionally, the use of augmented reality and machine learning in robotics and surgery is a significant development in precision oculoplasty. Notwithstanding its potential, issues including data heterogeneity, algorithm interpretability, and ethical considerations are significant roadblocks that need to be addressed. This chapter explores cutting-edge developments, real-world uses, and potential future paths, offering researchers and doctors a thorough resource.Dipali Vikas Mane, Associate Professor, Shriram Shikshan Sanstha’s College of Pharmacy, Paniv-413113
Keywords: Machine learning, Ocularoncology, Oculoplasty, Deep learning, Personalized medicine
How to cite : Mane D V, Khuspe P R, Machine learning in ocular oncology and oculoplasty: Transforming diagnosis and treatment. IP Int J Ocul Oncol Oculoplasty 2024;10(4):196-207
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Received : 03-11-2024
Accepted : 23-12-2024
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