Mapping the Research Landscape of Deep Learning in Knee Osteoarthritis

Abstract

A comprehensive bibliometric analysis of the research in deep learning applied to knee osteoarthritis (KOA) classification is presented. The Scopus database was analyzed from 2015 to 2025 for 3,199 articles. The purpose of this study has been to understand publication patterns and identify top contributors, thematic clusters, and emerging research areas in this area. Results indicate a surge in research activity, particularly over recent years as the number of publications rises dramatically. China, United States, and India were major leading countries in terms of the research output, while the University of California and University of Stanford were identified as major contributors. Co-word analysis highlighted four key thematic clusters. In this paper, the authors examine advancements in deep learning architectures, imaging modalities used for KOA diagnosis, machine learning interpretation techniques, and data preprocessing techniques.