Abstract

Objective: This study was to investigate the machine-learning texture analysis of laryngoscope image for risk stratification of Vocal Cord Leukoplakia (VCL).

Design: Retrospective case-control study.

Materials and methods: The laryngoscopy images were divided into the training datasets and the testing datasets. Five machine-learning classifiers including Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Neural Network (NN) and XG Boost (XGB) were trained using the training dataset and tested using the testing dataset. In addition, two laryngologists performed the Clinical Visual Assessments (CVA) for both training and testing datasets. The performances among five machine-learning models and two laryngologists were evaluated by using the area under the Receiver Operating Characteristic (ROC) Curves (AUC).

Result: In the training dataset, all five machine-learning models achieved Area Under Curve (AUC) between 0.935-0.966, in which RF was superior to other models. The AUC of clinicians’ CVAs were between 0.612-0.722. In the testing dataset, all five machine-learning models achieved AUC between 0.949-0.988. The AUC of clinicians’ CVAs were between 0.631-0.752.

Conclusion: The five machine-learning classifiers achieved excellent performance in predicting the pathological grading of VCL, which outperformed the CVA by clinicians.

Keywords:Vocal cord leukoplakia; Laryngoscopy; Machine learning; Texture analysis; Pathological grading