The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures

Overview

Journal Bioengineering (Basel)

Date 2024 Apr 27

PMID 38671760

Authors

Andrew Tieu

Ezriel Kroen

Yonaton Kadish

Zelong Liu

Nikhil Patel

Alexander Zhou

Alara Yilmaz

Stephanie Lee

Timothy Deyer

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field.

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