{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T10:38:17Z","timestamp":1769596697955,"version":"3.49.0"},"reference-count":44,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T00:00:00Z","timestamp":1693008000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1311801"],"award-info":[{"award-number":["2019YFB1311801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Robotics Computer Surgery"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Artificial intelligence (AI) techniques, especially deep learning (DL) techniques, have shown promising results for various computer vision tasks in the field of surgery. However, AI\u2010guided navigation during microscopic surgery for real\u2010time surgical guidance and decision support is much more complex, and its efficacy has yet to be demonstrated. We propose a model dedicated to the evaluation of DL\u2010based semantic segmentation of chorda tympani (CT) during microscopic surgery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Various convolutional neural networks were constructed, trained, and validated for semantic segmentation of CT. Our dataset has 5817 images annotated from 36 patients, which were further randomly split into the training set (90%, 5236 images) and validation set (10%, 581 images). In addition, 1500 raw images from 3 patients (500 images randomly selected per patient) were used to evaluate the network performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>When evaluated on a validation set (581 images), our proposed CT detection networks achieved great performance, and the modified U\u2010net performed best (mIOU\u00a0=\u00a00.892, mPA\u00a0=\u00a00.9427). Moreover, when applying U\u2010net to predict the test set (1500 raw images from 3 patients), our methods also showed great overall\u00a0performance (Accuracy\u00a0=\u00a00.976, Precision\u00a0=\u00a00.996, Sensitivity\u00a0=\u00a00.979, Specificity\u00a0=\u00a00.902).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study suggests that DL can be used for the automated detection and segmentation of CT in patients with otosclerosis during microscopic surgery with a high degree of performance. Our research validated the potential feasibility for future vision\u2010based navigation surgical assistance and autonomous surgery using\u00a0AI.<\/jats:p><\/jats:sec>","DOI":"10.1002\/rcs.2567","type":"journal-article","created":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T04:44:57Z","timestamp":1693111497000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic detection and segmentation of chorda tympani under microscopic vision in otosclerosis patients via convolutional neural networks"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4801-2687","authenticated-orcid":false,"given":"Yu","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology Head and Neck Surgery the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 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