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Artificial Intelligence in Choroid Through Optical Coherence Tomography: A Comprehensive Review
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  • Amrish Selvam ,
  • Joshua Ong ,
  • Sandeep Chandra Bollepalli ,
  • Jay Chhablani ,
  • Kiran Kumar Vupparaboina ,
  • Matthew Driban
Amrish Selvam
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Joshua Ong
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Sandeep Chandra Bollepalli
University of Pittsburgh School of Medicine, University of Pittsburgh School of Medicine, University of Pittsburgh School of Medicine

Corresponding Author:[email protected]

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Jay Chhablani
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Kiran Kumar Vupparaboina
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Matthew Driban
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Abstract

Vision-threatening conditions, such as age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR), arise from dysfunctions in the highly vascular choroid layer in the eye’s posterior segment. Optical coherence tomography (OCT) images play a crucial role in diagnosing choroidal structural changes in clinical practice. This review emphasizes the significant efforts in developing precise detection, quantification, and automated disease classification of choroidal biomarkers. The rapid progress of artificial intelligence (AI) has triggered transformative breakthroughs across sectors including medical image analysis. Recently, the integration of AI within the diagnosis and treatment of choroidal diseases has captured significant attention. Multiple studies highlight AI’s potential to enhance diagnostic precision and optimize clinical outcomes in this context. The review provides an extensive overview of AI’s current applications in choroidal analysis using OCT imaging. It encompasses a diverse array of algorithms and techniques employed for biomarker detection, such as thickness and vascularity index, and for identifying diseases like AMD and CSCR. The overarching goal of this review is to provide an updated and comprehensive exploration of AI’s impact on the choroid, highlighting its potential, challenges, and role in driving innovation in the field.