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

In this study, we present a deep learning model for fracture classification on shoulder radiographs using a convolutional neural network (CNN). The primary aim was to evaluate the classification performance of the CNN for proximal humeral fractures (PHF) based on the AO/OTA classification system. Secondary objectives included evaluating the model's performance for diaphyseal humerus, clavicle, and scapula fractures. The training dataset consisted of 6,172 examinations, including 2-7 radiographs per examination. The overall area under the curve (AUC) for fracture classification was 0.89, indicating good performance. For PHF classification, 12 out of 16 classes achieved an AUC of 0.90 or greater. Additionally, the CNN model had excellent overall AUC for diaphyseal humerus fractures (0.97), clavicle fractures (0.96), and good AUC for scapula fractures (0.87). Despite the limitations of the study, such as the reliance on ground truth labels provided by students with limited radiographic assessment experience, our findings are in concordance with previous studies, further consolidating CNN as potent fracture classifiers in plain radiographs. The inclusion of multiple radiographs with different views from each examination, as well as the generally unselected nature of the sample, contributed to the overall generalizability of the study. This is the fifth study published by our group on AI in orthopaedic radiographs, which has consistently shown promising results. The next challenge for the orthopaedic research community will be to transfer these results from the research setting into clinical practice. External validation of the CNN model should be conducted in the future before it is considered for use in a clinical setting.

Microcirculation in healing and healthy Achilles tendon assessed with invasive laser doppler flowmetry.

INTRODUCTION: Achilles tendon (AT) rupture exhibits a prolonged healing process with varying clinical outcome. Reduced blood flow to the AT has been considered an underlying factor to AT rupture (ATR) and impaired healing. In vivo measurements using laser Doppler flowmetry (LDF) may be a viable method to assess blood flow in healthy and healing AT. METHODS: 29 persons were included in the study; 9 being ATR patients and 20 healthy subjects without any prior symptoms from the AT. Invasive LDF was used to determine the post-occlusive reactive hyperemia (PORH) in the paratenon after 15 minutes of occlusion of the lower extremities. ATR patients were examined two weeks post-operatively. RESULTS: LDF-assessments demonstrated a significantly different (p < 0.001) PORH response in the healing- versus intact- and control AT. In the healing AT, a slow, flattened PORH was observed compared to a fast, high peak PORH in intact, healthy AT. CONCLUSION: in vivo LDF appears to be a feasible method to assess alterations in blood flow in healing and intact AT. The healing ATs capability to react to an ischemic period is clearly impaired, which may be due to the trauma at injury and/or surgery or degenerative changes in the tendon.