Overview of global publications on machine learning in diabetic retinopathy from 2011 to 2021: Bibliometric analysis

PurposeTo comprehensively analyze and discuss the publications on machine learning (ML) in diabetic retinopathy (DR) following a bibliometric approach. MethodsThe global publications on ML in DR from 2011 to 2021 were retrieved from the Web of Science Core Collection (WoSCC) database. We analyzed the publication and citation trend over time and identified highly-cited articles, prolific countries, institutions, journals and the most relevant research domains. VOSviewer and Wordcloud are used to visualize the mainstream research topics and evolution of subtopics in the form of co-occurrence maps of keywords. ResultsBy analyzing a total of 1147 relevant publications, this study found a rapid increase in the number of annual publications, with an average growth rate of 42.68%. India and China were the most productive countries. IEEE Access was the most productive journal in this field. In addition, some notable common points were found in the highly-cited articles. The keywords analysis showed that "diabetic retinopathy", "classification", and "fundus images" were the most frequent keywords for the entire period, as automatic diagnosis of DR was always the mainstream topic in the relevant field. The evolution of keywords highlighted some breakthroughs, including "deep learning" and "optical coherence tomography", indicating the advance in technologies and changes in the research attention. ConclusionsAs new research topics have emerged and evolved, studies are becoming increasingly diverse and extensive. Multiple modalities of medical data, new ML techniques and constantly optimized algorithms are the future trends in this multidisciplinary field.