{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T19:11:23Z","timestamp":1793301083714,"version":"4.5.0"},"reference-count":36,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["arthroscopyjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthroscopy"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To assess the feasibility of a deep learning model for predicting early structural integrity after arthroscopic rotator cuff repair (ARCR) using intraoperative arthroscopic images obtained after the double\u2010row bridging repair technique.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included patients who underwent ARCR between January 2016 and June 2024, provided that postoperative tendon integrity was assessed using ultrasonography at a minimum follow\u2010up of 6\u2009months. Intraoperative arthroscopic images of the postrepaired tendon were collected. The dataset was randomly divided into training, validation, and test sets in a 7:1.5:1.5 ratio at the patient level. Transfer learning was performed using the pretrained ConvNeXt\u2010Tiny architecture integrated with a Convolutional Block Attention Module. Model performance was evaluated using the area under the receiver operating characteristic curve, precision\u2010recall curve, sensitivity, specificity, and F1 score. Clinical utility was evaluated using decision curve analysis and a reliability curve.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1047 intraoperative arthroscopic images were collected from 642 patients in the healed group, and 151 images were obtained from 85 patients in the retear group with a mean follow\u2010up of 6\u2009months. The performance of the model on the test dataset (81 patients [71 healed and 10 retear], corresponding to 170 images [146 healed and 24 retear]) achieved a sensitivity of 100% (95% confidence interval [CI], 1.0\u20101.0) and specificity of 83.3% (95% CI, 0.66\u20100.96), with an overall classification accuracy of 97.6%. Model performance yielded an area under the receiver operating characteristic curve of 0.95 (95% CI, 0.84\u20100.99), precision\u2010Recall Area under the curve\u00a0of 0.98 (95% CI, 0.96\u20100.99) and F1 score of 0.98 (95% CI, 0.97\u20100.99). Eigen\u2010Class Activation Map visualizations revealed that the model predominantly focused on the repaired tendon edges and footprint zones during prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The ConvNeXt\u2010Convolutional Block Attention Module deep learning model can predict early tendon structural integrity with high internal accuracy after ARCR using only intraoperative arthroscopic images of the repaired tendon from a single\u2010center dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>The image\u2010based deep learning model may assist surgeons in screening patients at high risk of retear after ARCR, thereby supporting delayed postoperative rehabilitation, using a decision threshold of 0.40 healing probability.<\/jats:p>\n       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