Recent Advances in Iridology based Disease Detection: A Comprehensive Review

Alaa Abdulkareem Ahmed; Mohammad Tariq Yaseen

doi:10.58564/IJSER.4.1.2025.291

Authors

Alaa Abdulkareem Ahmed Department of Electrical Engineering, College of Engineering, University of Mosul, Mosul, Iraq
Mohammad Tariq Yaseen Department of Electrical Engineering, College of Engineering, University of Mosul, Mosul, Iraq

DOI:

https://doi.org/10.58564/IJSER.4.1.2025.291

Keywords:

Iridology, Disease, Region of Interest (ROI), Artificial Intelligence (AI), Machine Learning (ML)

Abstract

The increasing demand for non-invasive, rapid, and cost-effective disease diagnostics has driven advancements in integrating Iridology with computer vision and Artificial Intelligence (AI). This review examines research conducted from 2009 to 2024 on iris-based disease detection. The key findings showed the significant role of Machine Learning (ML) and Deep Learning (DL) in enhancing diagnostic accuracy and efficiency. Iridology-based intelligent systems show great promise for early detection of hidden diseases and organ dysfunctions, offering transformative potential for healthcare.

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