Comparison of artificial intelligence algorithm for the diagnosis of hip fracture on plain radiography with decision-making physicians: a validation study.

Objective: This study aimed to compare an algorithm developed for diagnosing hip fractures on plain radiographs with the physicians involved in diagnosing hip fractures.

Methods: Radiographs labeled as fractured (n=182) and non-fractured (n=542) by an expert on proximal femur fractures were included in the study. General practitioners in the emergency department (n=3), emergency medicine (n=3), radiologists (n=3), orthopedic residents (n=3), and orthopedic surgeons (n=3) were included in the study as the labelers, who labeled the presence of fractures on the right and left sides of the proximal femoral region on each anteroposterior (AP) plain pelvis radiograph as fractured or non-fractured.

The ensemble artificial intelligence (AI) method: Detection of hip fractures in AP pelvis plain radiographs by majority voting using a multi-center dataset.

Introduction: This article was undertaken to explore the potential of AI in enhancing the diagnostic accuracy and efficiency in identifying hip fractures using X-ray radiographs. In the study, we trained three distinct deep learning models, and we utilized majority voting to evaluate their outcomes, aiming to yield the most reliable and precise diagnoses of hip fractures from X-ray radiographs.

Methods: An initial study was conducted of 10,849 AP pelvis X-rays obtained from five hospitals affiliated with Başkent University.