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.