PURPOSE: To appraise the performances of an AI trained to detect and localize skeletal lesions and compare them to the routine radiological interpretation.
OBJECTIVE: Joint dislocations are orthopedic emergencies that require prompt intervention. Automatic identification of these injuries could help improve timely patient care because diagnostic delays increase the difficulty of reduction. In this study...
PURPOSE: This paper aims to provide an overview of the possibility regarding the artificial intelligence application in orthopaedics to predict dislocation with a calculator according to the type of implant (hemiarthroplasty, standard total hip arthr...
We propose a wrist joint subluxation/ankylosis classification model for an automatic radiographic scoring system for X-ray images. In managing rheumatoid arthritis, the evaluation of joint destruction is important. The modified total Sharp score (mTS...
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...
Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was used for detection ...
This study proposes a Deep Learning algorithm to automatically detect perilunate dislocation in anteroposterior wrist radiographs. A total of 374 annotated radiographs, 345 normal and 29 pathological, of skeletally mature adolescents and adults aged ...
BACKGROUND: Early and accurate diagnosis is critical to preserve function and reduce healthcare costs in patients with hand and wrist injury. As such, artificial intelligence (AI) models have been developed for the purpose of diagnosing fractures thr...
OBJECTIVES: The purpose of this study was to propose a machine learning model and assess its ability to classify temporomandibular joint (TMJ) disc displacements on MR T1-weighted and proton density-weighted images.
OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging (MRI) using artificial intelligence (AI). Using deep learning techniques, the model first automatica...