Background Patients with fractures are a common emergency presentation and may be
misdiagnosed at radiologic imaging. An increasing number of studies apply artificial …

Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has
been the object of extensive research. Recent advances in machine learning (ML) have …

Background The interpretation of radiographs suffers from an ever-increasing workload in
emergency and radiology departments, while missed fractures represent up to 80% of …

Importance Artificial intelligence (AI) enables powerful models for establishment of clinical
diagnostic and prognostic tools for hip fractures; however the performance and potential …

Purpose Suspected fractures are among the most common reasons for patients to visit
emergency departments and often can be difficult to detect and analyze them on film scans …

In recent years, bone fracture detection and classification has been a widely discussed topic
and many researchers have proposed different methods to tackle this problem. Despite this …

Abstract Background AI/ML CAD tools can potentially improve outcomes in the high-stakes,
high-volume model of trauma radiology. No prior scoping review has been undertaken to …

Researchers have proposed several ways of diagnosing, classifying and categorizing bone
fractures. Nevertheless, no standardized classification has yet been established for all …

Introduction The increasing availability of data and computing power has made machine
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …

Purpose To calculate the diagnostic accuracy from the confusion matrix using deep learning
(DL) on ultrasound (US) images of Palmer 1B triangular fibrocartilage complex (TFCC) …