Mapping the Intelligence: A Bibliometric Analysis of Artificial Intelligence and Machine Learning Applications in Retinal OCT for Neurodegenerative Diseases

Background Retinal optical coherence tomography (OCT) combined with artificial intelligence (AI) is increasingly proposed as a scalable window onto neurodegenerative disease, yet the intellectual architecture of this field remains unmapped. Methods Following the BIBLIO guideline, 1,036 documents (1976-2026) were retrieved from the Web of Science Core Collection and analysed with bibliometrix (v5.4.0), integrating performance analysis, collaboration and co-citation networks, and science mapping. Results Output grew exponentially after 2010, peaking at 139 articles in 2025. Productivity and impact concentrated in a transatlantic core (USA, Germany, UK; Charité Berlin, University College London, Johns Hopkins; Calabresi, Paul, Saidha), with a 19-journal Bradford core led by Investigative Ophthalmology & Visual Science. Thematic mapping documented a decisive post-2022 reorientation toward deep learning, optical coherence tomography angiography (OCT-A) and oculomics, with AI themes consolidating as motor themes. Conclusions The field is transitioning from structural biomarker research to computational phenotyping; externally validated, globally inclusive collaborations will determine whether retinal AI fulfils its screening potential.