High-Speed Videoendoscopy and Stiffness Mapping for AI-Assisted Glottic Lesion Differentiation

Objectives: This study evaluates the potential of high-speed videoendoscopy (HSV) in differentiating between benign and malignant glottic lesions, offering a non-invasive diagnostic tool for clinicians. Moreover, a new parameter derived from high-speed videoendoscopy (HSV) had been proposed and implemented in the analysis for an objective assessment of the vocal fold stiffness. Methods: High-speed videoendoscopy (HSV) was conducted on 102 participants, including 21 normophonic individuals, 39 patients with benign vocal fold lesions, and 42 with glottic cancer. Laryngotopographic parameter describing the stiffness of vocal fold (SAI) and kymographic parameters describing amplitude, symmetry, and glottal dynamics were quantified. Statistical differences between groups were assessed using receiver operating characteristic (ROC) analysis and lesion classification was performed using a machine learning model. Results: Univariate receiver operating characteristic (ROC) analysis revealed that SAI (AUC = 0.91, 95% CI: 0.839-0.962) and weighted amplitude asymmetry (AUC = 0.92, 95% CI: 0.85-0.974) were highly effective in distinguishing between normophonic and organic lesions (p < 0.01). Further multivariate analysis using machine learning models demonstrated improved accuracy, with the SVM classifier achieving an AUC of 0.93 for detecting organic lesions and 0.83 for distinguishing benign from malignant lesions. Conclusions: The study demonstrates the potential value of parameter describing the pliability of infiltrated vocal fold (SAI) as a non-invasive tool to support histopathological evaluation in laryngeal lesions, with machine learning models enhancing diagnostic performance.