{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:13:12Z","timestamp":1783584792879,"version":"3.55.0"},"reference-count":35,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Otolaryngology - Head &amp; Neck Surgery"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Importance:<\/jats:title>\n                    <jats:p>Type I tympanoplasty restores hearing in patients with simple tympanic membrane (TM) perforations, but reliable tools to predict postoperative outcomes remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To develop and evaluate a deep learning-assisted model integrating automated TM image features and clinical data to predict postoperative air-bone gap (ABG) closure and residual ABG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>Diagnostic and prognostic model development and validation study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting:<\/jats:title>\n                    <jats:p>A tertiary referral medical center in northern Taiwan.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants:<\/jats:title>\n                    <jats:p>A total of 1285 otoendoscopic images were collected, of which 1014 intact and 150 perforated TMs were used to train the mask region-based convolutional neural network (Mask R-CNN) segmentation model. Prognostic analysis included 121 patients with simple perforations and anatomically successful type I tympanoplasty (complete TM closure), with 83 preoperative images for training and 38 for independent internal testing. Demographic, clinical, and audiometric data were recorded.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Intervention or Exposures:<\/jats:title>\n                    <jats:p>Automated image features extracted by Mask R-CNN, combined with clinical and audiometric variables, were used to develop prognostic models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measures:<\/jats:title>\n                    <jats:p>\n                      Segmentation performance was evaluated using class pixel accuracy (CPA). Prognostic model performance was assessed using the coefficient of determination (R\n                      <jats:sup>2<\/jats:sup>\n                      ), root mean square error (RMSE), mean absolute error, and predictive accuracy, defined as a predicted ABG within 10 and 5\u2009dB of the measured value.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The segmentation model achieved a CPA of 0.884 for TM detection and 0.901 for perforation detection. The prognostic models yielded R\n                      <jats:sup>2<\/jats:sup>\n                      values of 0.418 for ABG closure and 0.363 for residual ABG, with corresponding RMSEs of 4.39 and 4.36\u2009dB. Prediction accuracy reached 97% within 10\u2009dB and 74% within 5\u2009dB, significantly outperforming baseline mean-value prediction (P\u2009&lt;\u2009.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Deep learning-assisted analysis of TM images showed modest predictive ability for hearing outcomes after anatomically successful type I tympanoplasty.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance:<\/jats:title>\n                    <jats:p>This image-based approach may modestly assist preoperative counseling in otologic practice.<\/jats:p>\n                  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