European journal of radiology · 2026
Deep learning for subtype classification of inner ear malformations on temporal bone HRCT: Development and multicenter validation.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Li Xiaoge, Zhao Xing, Hao Qianyu, Gao Kun, Feng Haifeng, Sun Shuping, Liu Shuo, Sun Dance, Cheng Liuquan, Shen Zhecheng, Shao Hang, Dai Pu, Yuan Yongyi
Аннотация
PURPOSE: Accurate subtype classification of inner ear malformations (IEMs) on temporal bone high-resolution computed tomography (HRCT) is important for preoperative risk assessment and surgical planning, but depends heavily on subspecialty expertise. This study aimed to develop and externally validate a deep learning model for multiclass IEM diagnosis on temporal bone HRCT.
METHODS: We developed and externally validated a weakly supervised deep learning framework for one-stage classification of automatically extracted temporal bone regions of interest from HRCT in a multicenter cohort of 9,182 ears (3,161 IEMs and 6,021 normal ears). The framework was based on an improved Transformer-based multiple instance learning architecture. Performance was evaluated on internal and independent external datasets. An independent reader study compared the model with five senior and five junior otolaryngologists. Explainability was assessed using attention-based slice ranking and Grad-CAM. The study was approved by an institutional review board, and informed consent was waived.
RESULTS: On the independent external test set, the proposed model demonstrated strong multiclass performance, with an accuracy of 93.9 % (95 % CI, 92.4 %-95.3 %), a macro-F1 score of 88.2 % (95 % CI, 84.3 %-91.4 %), and a multiclass AUC of 0.990 (95 % CI, 0.985-0.995). Performance was consistent on the internal validation set, with an accuracy of 96.5 % (95 % CI, 95.6 %-97.4 %), a macro-F1 score of 87.5 % (95 % CI, 83.0 %-91.0 %), and a multiclass AUC of 0.994 (95 % CI, 0.989-0.998). In the reader study, the model obtained an accuracy of 94.0 % and a macro-F1 score of 89.9 %, outperforming both senior otolaryngologists (mean macro-F1, 75.7 %, Holm-adjusted p = 0.0046) and junior otolaryngologists (mean macro-F1, 62.9 %, Holm-adjusted p < 0.001). The model also provided interpretable outputs by identifying the top three highest-contributing slices and generating Grad-CAM heatmaps.
CONCLUSION: This weakly supervised framework enabled accurate and generalizable subtype-level diagnosis of IEMs on temporal bone HRCT in a large multicenter cohort and may serve as a practical decision-support tool for standardized preoperative assessment, particularly in settings with limited subspecialty expertise.
METHODS: We developed and externally validated a weakly supervised deep learning framework for one-stage classification of automatically extracted temporal bone regions of interest from HRCT in a multicenter cohort of 9,182 ears (3,161 IEMs and 6,021 normal ears). The framework was based on an improved Transformer-based multiple instance learning architecture. Performance was evaluated on internal and independent external datasets. An independent reader study compared the model with five senior and five junior otolaryngologists. Explainability was assessed using attention-based slice ranking and Grad-CAM. The study was approved by an institutional review board, and informed consent was waived.
RESULTS: On the independent external test set, the proposed model demonstrated strong multiclass performance, with an accuracy of 93.9 % (95 % CI, 92.4 %-95.3 %), a macro-F1 score of 88.2 % (95 % CI, 84.3 %-91.4 %), and a multiclass AUC of 0.990 (95 % CI, 0.985-0.995). Performance was consistent on the internal validation set, with an accuracy of 96.5 % (95 % CI, 95.6 %-97.4 %), a macro-F1 score of 87.5 % (95 % CI, 83.0 %-91.0 %), and a multiclass AUC of 0.994 (95 % CI, 0.989-0.998). In the reader study, the model obtained an accuracy of 94.0 % and a macro-F1 score of 89.9 %, outperforming both senior otolaryngologists (mean macro-F1, 75.7 %, Holm-adjusted p = 0.0046) and junior otolaryngologists (mean macro-F1, 62.9 %, Holm-adjusted p < 0.001). The model also provided interpretable outputs by identifying the top three highest-contributing slices and generating Grad-CAM heatmaps.
CONCLUSION: This weakly supervised framework enabled accurate and generalizable subtype-level diagnosis of IEMs on temporal bone HRCT in a large multicenter cohort and may serve as a practical decision-support tool for standardized preoperative assessment, particularly in settings with limited subspecialty expertise.