OBJECTIVES: To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs.
Clinical orthopaedics and related research
36881548
BACKGROUND: Occult scaphoid fractures on initial radiographs of an injury are a diagnostic challenge to physicians. Although artificial intelligence models based on the principles of deep convolutional neural networks (CNN) offer a potential method o...
OBJECTIVE: Perilunate injuries are rare but devastating carpal injuries. The treatment of perilunate injuries remains challenging and contentious. This study aims to describe a novel mini-invasive surgical technique of arthroscopic-assisted reduction...
PURPOSE: Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians...
OBJECTIVES: Scaphoid nonunion remains a challenging problem to manage with no general consensus on its treatment recommendations. We propose a novel minimally invasive (MIS) technique of arthroscopic bone grafting (ABG) with robot-assisted fixation f...
OBJECTIVE: Scaphoid nonunion advanced collapse (SNAC) is a relatively common and debilitating wrist disorder yet its treatment remains challenging and controversial. We aim to describe a novel technique of a dual arthroscopic and robotic assisted fou...
OBJECTIVES: Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this syst...
PURPOSE: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts.
PURPOSE: The aim of the study is to perform a systematic review and meta-analysis comparing the diagnostic performance of artificial intelligence (AI) and human readers in the detection of wrist fractures.