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Surgical Induction of Endolymphatic Hydrops by Obliteration of the Endolymphatic Duct
Published on: January 22, 2010
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Interpretable machine learning with multimodal hearing data for diagnosing endolymphatic hydrops
Xu Liu1,2, Qin Sun1,2, Suming Shi1,2
1ENT Institute, Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
Head & Face Medicine
|December 23, 2025
Summary
Integrating electrocochleography (ECochG) and pure-tone audiometry (PTA) data into machine learning models significantly improves endolymphatic hydrops (EH) diagnosis. This multimodal approach enhances accuracy for early clinical detection and management.
Area of Science:
- Audiology
- Medical Machine Learning
- Otolaryngology
Background:
- Endolymphatic hydrops (EH) diagnosis relies on clinical assessment, often lacking objective measures.
- Electrocochleography (ECochG) and pure-tone audiometry (PTA) provide auditory data but have limitations in diagnosing EH alone.
Purpose of the Study:
- To evaluate the diagnostic utility of ECochG for identifying EH.
- To enhance diagnostic accuracy by integrating ECochG and PTA data into machine learning (ML) models.
- To improve the clinical interpretability of ML models for EH diagnosis.
Main Methods:
- Prospective cohort study of 78 patients (156 ears) with suspected EH.
- ECochG and PTA examinations were conducted.
- Multimodal auditory data were used to develop and validate ML models, including Light Gradient Boosting Machine (LightGBM).
Main Results:
- ECochG-alone models showed limited diagnostic performance (AUC max 0.61).
- Integrating multimodal data into ML models substantially improved performance.
- The LightGBM model achieved an AUC of 0.92 and accuracy of 0.73 for predicting EH distribution.
Conclusions:
- Multimodal auditory data integrated into interpretable ML models significantly improve EH diagnostic accuracy.
- This approach offers an objective framework for early EH diagnosis and management.
- Further validation with larger, multi-center cohorts is warranted due to the study's limitations.
