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Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery.

Anthony Bozzo¹, James M G Tsui, Sahir Bhatnagar

¹From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg).

The Journal of the American Academy of Orthopaedic Surgeons

|April 23, 2024

Summary

This summary is machine-generated.

This review explores deep learning applications in orthopaedics, detailing neural networks and multimodal AI for surgical imaging and clinical data. It empowers surgeons to evaluate and implement artificial intelligence in their practice.

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Area of Science:

Orthopaedic Surgery
Medical Imaging
Artificial Intelligence

Background:

Deep learning, including neural networks and multimodal AI, is increasingly applied in orthopaedics.
AI integration in orthopaedic surgery offers potential for enhanced diagnostics and treatment planning.

Purpose of the Study:

To review deep learning applications in orthopaedics, focusing on neural networks and multimodal AI.
To provide orthopaedic surgeons with tools to evaluate AI literature and consider its clinical or research potential.

Main Methods:

Review of standard deep neural networks for clinical variables.
Description of convolutional neural networks for image data analysis.
Introduction to multimodal AI models for diverse data integration.

Main Results:

Practical examples of AI in orthopaedic surgery, particularly with imaging and clinical data.
Comparison of deep learning techniques with radiomics.
Guidance on interpreting and initiating deep learning studies.

Conclusions:

Empowering orthopaedic surgeons with deep learning knowledge facilitates research translation.
AI has the potential to enhance the efficacy and precision of orthopaedic patient care.