RT Journal Article
T1 Artificial intelligence applied to ophthalmology and optometry: A citation network analysis
A1 Martínez Pérez, Clara
A1 Álvarez Peregrina, Cristina
A1 Villa Collar, César
A1 Sánchez Tena, Miguel Ángel
AB Purpose: The objective of this study is to analyse co-authorship and co-citation networks of publications in the field of artificial intelligence in ophthalmology and optometry. As well as, identify the different areas of research and the most cited publication.Method: A search of publications was performed in the Web of Science database for the period from 1977 to December 2021, using the term “Artificial Intelligence AND (Ophthalmol* OR optometry)”. The analysis of the publication was carried out using the Citation Network Explorer, VOSviewer and CiteSpace software.Results: 1086 publications and 2348 citation networks were found. 2020 was the year with the highest number of publications, a total of 351 publications and 115 citation networks. The most cited publication was “Clinically applicable deep learning for diagnosis and referral in retinal disease” published by De Fauw et al. in 2018, with a citation index of 723. Through the clustering function, three groups were found that cover the main research areas in this field: retinal pathology, anterior segment and glaucoma.Conclusions: The citation network analysis offers an in-depth analysis of scientific publications and the adoption of new topics and fields of research. The results of an exhaustive analysis of citation networks in artificial intelligence in the field of ophthalmology and optometry are presented since the publication of the first article in 1977.
PB Spanish General Council of Optometrists
SN 1888-4296
YR 2022
FD 2022-09-21
LK https://hdl.handle.net/20.500.14352/73282
UL https://hdl.handle.net/20.500.14352/73282
LA eng
DS Docta Complutense
RD 7 abr 2025