Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale · 2026
Deep Learning-Assisted Prediction of Hearing Outcomes After Anatomically Successful Type I Tympanoplasty.
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Liu Te-Yi, Chang Hsiang-Chih, Wang Pa-Chun, Hsu Su-Yi, Fang Te-Yung, Pham Van-Truong, Tran Thi-Thao, Lin Chen, Lo Men-Tzung
Аннотация
IMPORTANCE: Type I tympanoplasty restores hearing in patients with simple tympanic membrane (TM) perforations, but reliable tools to predict postoperative outcomes remain limited.
OBJECTIVE: 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.
DESIGN: Diagnostic and prognostic model development and validation study.
SETTING: A tertiary referral medical center in northern Taiwan.
PARTICIPANTS: 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.Intervention or Exposures:Automated image features extracted by Mask R-CNN, combined with clinical and audiometric variables, were used to develop prognostic models.
MAIN OUTCOME MEASURES: Segmentation performance was evaluated using class pixel accuracy (CPA). Prognostic model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error, and predictive accuracy, defined as a predicted ABG within 10 and 5 dB of the measured value.
RESULTS: The segmentation model achieved a CPA of 0.884 for TM detection and 0.901 for perforation detection. The prognostic models yielded R2 values of 0.418 for ABG closure and 0.363 for residual ABG, with corresponding RMSEs of 4.39 and 4.36 dB. Prediction accuracy reached 97% within 10 dB and 74% within 5 dB, significantly outperforming baseline mean-value prediction (P < .05).
CONCLUSIONS: Deep learning-assisted analysis of TM images showed modest predictive ability for hearing outcomes after anatomically successful type I tympanoplasty.
RELEVANCE: This image-based approach may modestly assist preoperative counseling in otologic practice.
OBJECTIVE: 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.
DESIGN: Diagnostic and prognostic model development and validation study.
SETTING: A tertiary referral medical center in northern Taiwan.
PARTICIPANTS: 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.Intervention or Exposures:Automated image features extracted by Mask R-CNN, combined with clinical and audiometric variables, were used to develop prognostic models.
MAIN OUTCOME MEASURES: Segmentation performance was evaluated using class pixel accuracy (CPA). Prognostic model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error, and predictive accuracy, defined as a predicted ABG within 10 and 5 dB of the measured value.
RESULTS: The segmentation model achieved a CPA of 0.884 for TM detection and 0.901 for perforation detection. The prognostic models yielded R2 values of 0.418 for ABG closure and 0.363 for residual ABG, with corresponding RMSEs of 4.39 and 4.36 dB. Prediction accuracy reached 97% within 10 dB and 74% within 5 dB, significantly outperforming baseline mean-value prediction (P < .05).
CONCLUSIONS: Deep learning-assisted analysis of TM images showed modest predictive ability for hearing outcomes after anatomically successful type I tympanoplasty.
RELEVANCE: This image-based approach may modestly assist preoperative counseling in otologic practice.