Artificial Intelligence-Optimized Image Biomarker Identification for Sinonasal Inverted Papilloma Diagnosis Using Image-Enhanced Versus White-Light Endoscopy.

Objective: This study aims to compare image feature identification and diagnostic performance between artificial intelligence-aided image-enhanced endoscopy (using the Storz Professional Image Enhancement System, SPIES) and conventional white-light endoscopy (WLE) for sinonasal inverted papilloma (SIP). Methods: A total of 163 patients were investigated using Storz Professional Image Enhancement System (SPIES) endoscopy (SPIES Spectral A[SA]/Spectral B[SB]) and WLE. From each patient, one or two images (only in the presence of bilateral lesions) were obtained, and a large number of potential features were extracted from WLE, SA, and SB. Independent t-tests and LASSO methods were used to feature selection. Machine learning (ML) models were trained and evaluated for each channel of images. Results: The Random Forest (RF) algorithm on SPIES SB mode achieved superior performance: accuracy = 0.86, sensitivity = 0.89, specificity = 0.83, AUC = 0.86, and Kappa = 0.71. SB significantly outperformed SA (accuracy = 0.77) and WLE (accuracy=0.75). ROC analysis confirmed SB's diagnostic superiority (area under curve [AUC]: SB = 0.86 vs. SA = 0.77, WLE = 0.75). Conclusions: SPIES SB mode demonstrated the highest diagnostic accuracy and AUC for SIP identification, highlighting its potential as a superior imaging modality. SPIES endoscopy exhibits superior diagnostic accuracy and predictive performance compared to conventional WLE in the diagnosis of SIP. However, the single-center design and limited sample size necessitate validation through multicenter studies with larger cohorts.