Cureus · 2025
Evaluating Partial Middle Ear Filling for the Diagnosis of Cholesteatoma Using Machine Learning: A Radiological Approach.
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Ouattassi Naouar, Zoizou Abdelhay, Maaroufi Mustapha, Labiyad El Mehdi, Benatiya Andaloussi Taha, Benmansour Najib, Ridal Mohammed, Zarghili Arsalane, El Amine El Alami Mohamed Nouredine
Аннотация
OBJECTIVES: To evaluate the diagnostic accuracy of middle ear partial filling on temporal bone computed tomography (CT) scan for middle ear cholesteatoma identification using supervised machine learning models.
METHODS: We conducted an observational case-control study that retrospectively analyzed temporal bone CT scans from 212 patients from a single tertiary healthcare institution using supervised machine learning models, including k-Nearest Neighbors (kNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The study assessed the diagnostic value of partial middle ear filling for cholesteatoma. Limitations such as dataset imbalance and data complexity were addressed. Results. In internal validation, kNN and Neural Networks achieved the highest performance (area under the receiver operating characteristic curve [AUC]: 1.000, classification accuracy [CA]: 99.6-99.7%, F1: 0.996-0.997), followed by Logistic Regression (AUC: 0.998, CA: 98.3%, F1: 0.983) and SVM (AUC: 0.997, CA: 97.5%, F1: 0.975). Random Forest performed the weakest (AUC: 0.980, CA: 92.0%, F1: 0.919). External validation (125 cases) revealed Neural Networks' superior generalizability (four errors), outperforming Logistic Regression (five), SVM (seven), Random Forest (28), and kNN (45). kNN demonstrated notably lower generalizability, suggesting limited robustness for unseen data. Discussion. The study highlights the effectiveness of machine learning in diagnosing cholesteatoma. Addressing data imbalance and variability in CT scans was crucial for model performance. Further research is needed to refine these models and explore their integration into clinical practice.
METHODS: We conducted an observational case-control study that retrospectively analyzed temporal bone CT scans from 212 patients from a single tertiary healthcare institution using supervised machine learning models, including k-Nearest Neighbors (kNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The study assessed the diagnostic value of partial middle ear filling for cholesteatoma. Limitations such as dataset imbalance and data complexity were addressed. Results. In internal validation, kNN and Neural Networks achieved the highest performance (area under the receiver operating characteristic curve [AUC]: 1.000, classification accuracy [CA]: 99.6-99.7%, F1: 0.996-0.997), followed by Logistic Regression (AUC: 0.998, CA: 98.3%, F1: 0.983) and SVM (AUC: 0.997, CA: 97.5%, F1: 0.975). Random Forest performed the weakest (AUC: 0.980, CA: 92.0%, F1: 0.919). External validation (125 cases) revealed Neural Networks' superior generalizability (four errors), outperforming Logistic Regression (five), SVM (seven), Random Forest (28), and kNN (45). kNN demonstrated notably lower generalizability, suggesting limited robustness for unseen data. Discussion. The study highlights the effectiveness of machine learning in diagnosing cholesteatoma. Addressing data imbalance and variability in CT scans was crucial for model performance. Further research is needed to refine these models and explore their integration into clinical practice.