Artificial intelligence (AI) in medicine has been widely explored, Specifically, deep learning has been reported to gain excellent achievement on computed tomography (CT) and magnetic resonance (MR) images. Deep learning models can learn the most predictive features directly from raw image pixels and avoid the subjective impressions. 1 At present, there are many AI algorithms, including feed-forward neural networks, regularized regression, gradient-boostered trees, 2 and random forests are one of the most used machine learning methods.In this study, the authors sought to optimize random forest prediction performance using combinations of clinical and medical imaging data, and constructed a nomogram. 3 This is the first article creating a new contrast-enhanced sonography (CEUS)-related nomogram for differentiating benign from malignant lesions in the periampullary area. 3 The periampullary region is a complex area composed of three histologically and physiologically different anatomical structures, namely, ampulla of Vater, pancreatic duct, and common bile duct. 4 Theoretically, clinical and biological behaviors of the diseases depend on their primary pathological structure. However, determining the disease nature arising in this narrow and complex area is usu-