Automated Detection of Retinal Diseases Using Artificial Intelligence-Enhanced Ocular Coherence Tomography Imaging: Bibliometric Analysis - Tarazi, Alaa; AlSawaftah, Yazan; Basheti, Omar; Abu-Yaghi, Nakhleh (Jordan Medical Journal, 2025)
Artificial intelligence (AI) has revolutionized optical coherence tomography (OCT) imaging for the better detection of retinal diseases like diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Here, bibliometric analysis of AI in OCT is presented. We conducted a systematic search in the Web of Science Core Collection (WoSCC) database up to January 22, 2025 with the help of AI and OCT-related terms. The journal articles and reviews on the topic were examined based on publication trends, author productivity, institution affiliation, and keyword co-occurrence. Visualization was performed through VOSviewer software (1.6.20). A total of 765 articles were analyzed with an increasing trend in publications, led by the USA (30.7%), China (23.3%), and the UK (11.1%) in research production. The most cited institution was the Medical University of Vienna (9.0%), and the most productive journal was Scientific Reports (7.3%). The most common keywords were "Optical Coherence Tomography", "Deep Learning", and "Diabetic Retinopathy" with frequency of 469, 297, 221, respectively. Our review of AI-enhanced OCT for retinal diseases was both comprehensive and systematic, identifying key trends and current research hotspots. As research productivity in this field continues to grow, the focus is increasingly shifting toward developing more accurate AI-driven imaging techniques to improve the diagnosis of various retinal diseases. Future work should prioritize algorithm validation and clinical implementation to facilitate widespread adoption