Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle

Application of Artificial Intelligence in Shoulder Pathology

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

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

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.

Screw Stress Distribution in a Clavicle Fracture with Plate Fixation: A Finite Element Analysis

Clavicle midshaft fractures are mostly treated surgically by open internal reduction with a superior or anteroinferior plate and screws or by intramedullary nailing. Screw positioning plays a critical role in determining the stress distribution. There is a lack of data on the screw position and the appropriate number of cortices required for plate fixation. The aim of this study is to evaluate the mechanical behavior of an anterior plate implanted in a fractured bone subjected to 120° of lateral elevation compared to a healthy clavicle using numerical simulations. Contact forces and moments used were obtained from literature data and applied to the healthy and fractured finite element models. Stresses of about 9 MPa were found on the healthy clavicle, while values of about 15 MPa were calculated on the plate of the fractured one; these stress peaks were reached at about 30° and 70° of elevation when the stress shielding on the clavicle sums all the three components of the solicitation: compression, flexion, and torsion. The stress distribution in a clavicle fracture stabilized with plates and screws is influenced by several factors, including the plate's position and design, the type of screw, and the biomechanical forces applied during movements.