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AI-Enhanced Prognostic Model for Predicting Polyp Recurrence and Guiding Post-Polypectomy Surveillance Intervals

Sri Harsha Boppana¹, Sachin Sravan Kumar Komati², Ritwik Raj³

¹Department of Internal Medicine, Nassau University Medical Center, East Meadow, NY 11554, USA.

Journal of Clinical Medicine

|May 13, 2026

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This summary is machine-generated.

A new AI model accurately predicts colorectal polyp recurrence after removal by combining endoscopic images with patient data, enabling personalized surveillance plans. This AI approach improves risk stratification for better colorectal cancer prevention.

Area of Science:

Gastroenterology and AI
Medical Imaging Analysis
Oncology

Background:

Colorectal cancer (CRC) is a significant health concern, with adenomatous polyps as precursors.
Post-polypectomy recurrence poses a substantial risk for CRC development.
Current surveillance strategies require optimization for personalized patient management.

Purpose of the Study:

To develop and evaluate a multimodal AI model for predicting colorectal polyp recurrence post-polypectomy.
To integrate endoscopic imaging with clinical and pathology data for enhanced risk prediction.
To support individualized colonoscopy surveillance planning based on recurrence risk.

Main Methods:

A multimodal AI model was developed using the ERCPMP-v5 dataset, incorporating endoscopic images and structured clinical/pathology data.

Keywords:

artificial intelligence colorectal cancer multilayer perceptron recurrence prediction vision transformers

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A late fusion framework combined features from a Vision Transformer (ViT) backbone and a multilayer perceptron for metadata.

Model performance was assessed using accuracy, precision, recall, F1-score, and AUC, compared against image-only and metadata-only models.

Main Results:

The multimodal fusion model achieved high performance: 90.4% accuracy, 86.7% precision, 83.1% recall, 84.9% F1-score, and 0.920 AUC.
Multimodal fusion outperformed image-only (0.880 AUC) and metadata-only (0.850 AUC) models.
Risk stratification generated tailored surveillance intervals: 1-3 years (low), 6-12 months (moderate), and 3-6 months (high risk).

Conclusions:

A multimodal AI model integrating endoscopic imaging and clinical data effectively predicts post-polypectomy recurrence.
The model provides actionable, risk-based surveillance recommendations for individualized patient follow-up.
This approach enhances surveillance efficiency and resource allocation while prioritizing high-risk patients.