Mujeeb Ur Rehman Parrey Muhammad Omer Afzal Bhatti Maha M. Abdul-Latif Saima Rehman Mohammed M. Ismail Omaima A. Hamid

Keywords: Diagnostic Accuracy, Retinal Disease Detection, Fundus Photography, Meta-Analysis, Deep Learning, Algorithms, Artificial Intelligence

Abstract

Artificial intelligence (AI) is revolutionizing retinal diagnostics by enhancing the accuracy and efficiency of disease detection in fundus photography. Manual interpretation is time-consuming and subject to interobserver variability, highlighting the need for AI-driven solutions. This study assesses the diagnostic performance of AI algorithms in detecting retinal diseases using fundus photography. A systematic search was conducted in PubMed, Embase, Cochrane Library, IEEE Xplore, and Scopus, including studies reporting AI-based retinal disease detection with sensitivity, specificity, and area under the curve (AUC) metrics. The QUADAS-2 tool was used for quality assessment, and a random-effects model was applied for meta-analysis. AI algorithms demonstrated high diagnostic accuracy, with an overall sensitivity of 89% and specificity of 92%. Performance improved with larger datasets (>500 samples), achieving a sensitivity of 92%, specificity of 95%, and an AUC of 0.96. These findings suggest that AI has strong potential for clinical implementation in retinal disease screening, offering high accuracy for early diagnosis and improved patient outcomes.

Abbreviations: AI: Artificial intelligence, AUC: area under the curve,

Keywords: Artificial Intelligence; Algorithms; Deep Learning; Meta-Analysis; Fundus Photography; Retinal Disease Detection; Diagnostic Accuracy

Citation: Parrey MUR, Bhatti MOA, Abdul-Latif MM, Rehman S, Ismail MM, Hamid OA. Meta-analysis of AI algorithm performance in detecting retinal diseases. Anaesth. pain intensive care 2025;29(4):400-406. DOI:  10.35975/apic.v29i4.2828

Received: February 17, 2025; Revised: April 25, 2025; Accepted: April 25, 2025