Deep Learning-Assisted Prediction of Air-Bone Gap Using Tympanic Membrane Perforation Image Features.

Journal: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Published Date: Nov 18, 2025

Abstract

OBJECTIVE: To evaluate a deep learning (DL)-based approach for predicting air-bone gap (ABG) from tympanic membrane (TM) perforation images using automated segmentation and feature extraction, addressing the limitations of audiometry availability in certain populations or settings. STUDY DESIGN: Prospective, cross-sectional diagnostic study. SETTING: Tertiary academic medical center. METHODS: A total of 1239 otoscopic images were collected between January 2019 and May 2023. Of these, 1014 intact TM images and 150 perforated TM images were used for model development and validation, and 75 intraoperative perforated images were reserved for independent testing. A Mask region-based convolutional neural network (Mask R-CNN) model was trained to segment TM and perforation areas. Segmentation performance was evaluated using class pixel accuracy (CPA), intersection over union (IoU), and Dice coefficient. Quantitative features-including perforation ratio and spatial metrics-were extracted to predict ABG using theoretical and quadratic regression models. Model performance was assessed using R², root mean square error (RMSE), and the proportion of predictions within 10 dB of measured ABG. RESULTS: TM segmentation achieved CPA, IoU, and Dice scores of 0.794, 0.702, and 0.875; perforation segmentation yielded scores of 0.824, 0.729, and 0.894. ABG prediction showed R² values of 0.433 (theoretical) and 0.516 (quadratic), with RMSEs of 6.15 and 5.68 dB. Deep learning (DL)-assisted models achieved accuracy of 83% and 86%, comparable to manual annotation. CONCLUSIONS: DL-based analysis of TM images enables accurate ABG prediction and may provide a scalable tool to support assessment of conductive hearing loss in environments without access to audiometry.

Authors

  • Te-Yi Liu
    Department of Otolaryngology, Hsinchu Cathay General Hospital, Hsinchu, Taiwan.
  • Hsiang-Chih Chang
    Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan.
  • Pa-Chun Wang
    Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan.
  • Su-Yi Hsu
    Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan.
  • Te-Yung Fang
    Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan.
  • Van-Truong Pham
    School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam; Department of Biomedical Sciences and Engineering, National Central University, Chung-li, Taiwan.
  • Thi-Thao Tran
    School of Electrical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam; Department of Biomedical Sciences and Engineering, National Central University, Chung-li, Taiwan. Electronic address: [email protected].
  • Chen Lin
    Faculty of Business and Economics, University of Hong Kong, Hong Kong SAR 999077, China.
  • Men-Tzung Lo
    Department of Biomedical Sciences and Engineering, National Central University, Chung-li, Taiwan. Electronic address: [email protected].

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