Deep Learning and Imaging for the Orthopaedic Surgeon: How Machines "Read" Radiographs

In the not-so-distant future, orthopaedic surgeons will be exposed to machines that begin to automatically "read" medical imaging studies using a technology called deep learning. Deep learning has demonstrated remarkable progress in the analysis of medical imaging across a range of modalities that are commonly used in orthopaedics, including radiographs, computed tomographic scans, and magnetic resonance imaging scans. There is a growing body of evidence showing clinical utility for deep learning in musculoskeletal radiography, as evidenced by studies that use deep learning to achieve an expert or near-expert level of performance for the identification and localization of fractures on radiographs. Deep learning is currently in the very early stages of entering the clinical setting, involving validation and proof-of-concept studies for automated medical image interpretation. The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before (not after) the technology ends up in front of us and our patients. That is the purpose of this article.