Development and validation of an endoscopic diagnostic model for sessile serrated lesions based on machine learning algorithms.

ABSTRACT:

Background and aims

Sessile serrated lesions (SSLs) are morphologically subtle and often misclassified as hyperplastic polyps (HPs), increasing colorectal cancer risks. We developed a machine learning (ML) model to improve endoscopic SSL diagnosis.

Methods

Three hundred and eighty-six colorectal polyps (135 SSLs, 251 HPs) with histologically confirmed were retrospective analyzed and divided into a training set and a test set. Multiple ML classification models were applied for a comprehensive analysis. SHapley Additive exPlanations (SHAP) for model contribution were plotted, and the model results were interpreted by calculating the contribution of each feature to the prediction results.

Results

Comparative analysis revealed that the shrinkage method based on penalisati

SUBMITTER: Yu X 

PROVIDER: S-EPMC12568591 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature