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Updated: Sep 28, 2026
Using Retinal Imaging to Study Dementia
Published on: November 6, 2017
Mapping the Intelligence: A Bibliometric Analysis of Artificial Intelligence and Machine Learning Applications in
Mohammad Danesh-Doust1, Farzane Nikparast1, Hoda Zare2
1Student research committee, Mashhad University of medical sciences, Mashhad, Iran; Medical Physics Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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.
