Bone mineral density, as well as FI of the infraspinatus and amount of retraction, was an independent determining factor affecting postoperative rotator cuff healing. Further studies with prospective, randomized, and controlled design are needed to confirm the relationship between BMD and postoperative rotator cuff healing.
Background and purpose — We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs.Patients and methods — 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated.Results — The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65–86% top-1 accuracy, 0.90–0.98 AUC, 0.88/0.83–0.97/0.94 sensitivity/specificity, and 0.71–0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures.Interpretation — The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments.
Early passive motion exercise after arthroscopic cuff repair did not guarantee early gain of ROM or pain relief but also did not negatively affect cuff healing. We suggest that early passive motion exercise is not mandatory after arthroscopic repair of small to medium-sized full-thickness rotator cuff tears, and postoperative rehabilitation can be modified to ensure patient compliance.
Despite a high rate of healing failures, arthroscopic repair can be recommended in patients with massive rotator cuff tears because of the functional gain at midterm follow-up. Higher FI of the infraspinatus was the single most important factor negatively affecting cuff healing. In cases of failed massive rotator cuff repair, no preoperative factor was able to predict poor functional outcome; reduced postoperative AHD was the only relevant functional determinant in the patients' eventual functional outcome and should be considered when ascertaining a prognosis and planning further treatment strategies.
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