ABSTRACT
OBJECTIVE: To evaluate the performance of a machine learning model and the effects of major prognostic factors on hearing outcomes following intact canal wall (ICW) mastoidectomy with tympanoplasty. STUDY DESIGN: Retrospective cross-sectional study. SETTING: Tertiary hospital. METHODS: A total of 484 patients with chronic otitis media who underwent ICW tympanomastoidectomy between January 2007 and December 2020 were included in this study. Successful hearing outcomes were defined by a postoperative air-bone gap (ABG) of ≤20 dB and preoperative air conduction (AC)-postoperative AC value of ≥15 dB according to the Korean Otological Society guidelines for outcome reporting after chronic otitis media surgery. The light gradient boosting machine (LightGBM) and multilayer perceptron (MLP) models were tested as artificial intelligence models and compared using logistic regression. The main outcome assessed was the successful hearing outcome after surgery, measured using the area under the receiver operating characteristic curve (AUROC). RESULTS: In the analysis using the postoperative ABG criterion, the LightGBM exhibited a significantly higher AUROC compared to those of the baseline model (mean, 0.811). According to the difference between preoperative and postoperative AC, the MLP showed a significantly higher AUROC than those of the baseline model (mean, 0.795). CONCLUSION: This study analyzed multiple factors that could affect the hearing outcome using different artificial intelligence models and found that preoperative hearing status was the most important factor. Our findings provide additional information regarding postoperative hearing for clinicians.
