Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery

Anthony Bozzo, James M.G. Tsui, Sahir Bhatnagar, Jonathan Forsberg

Research output: Contribution to journalReview articlepeer-review

Abstract

This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits. We first review standard deep neural networks which can analyze numerical clinical variables, then describe convolutional neural networks which can analyze image data, and then introduce multimodal AI models which analyze various types of different data. Then, we contrast these deep learning techniques with related but more limited techniques such as radiomics, describe how to interpret deep learning studies, and how to initiate such studies at your institution. Ultimately, by empowering orthopaedic surgeons with the knowledge and know-how of deep learning, this review aspires to facilitate the translation of research into clinical practice, thereby enhancing the efficacy and precision of real-world orthopaedic care for patients.

Original languageEnglish
Pages (from-to)E523-E532
JournalThe Journal of the American Academy of Orthopaedic Surgeons
Volume32
Issue number11
DOIs
StatePublished - 1 Jun 2024
Externally publishedYes

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