Development and Validation of a Dual-Path Intelligent Auxiliary Diagnostic System for Colorectal Cancer (Preprint)

<sec> <title>BACKGROUND</title> Colorectal cancer (CRC) is a major global health concern, emphasizing the importance of early detection. This study aims to develop a dual-path AI strategy combining molecular analysis and image segmentation to enhance intelligent CRC diagnosis. </sec> <sec> <title>OBJECTIVE</title> To address existing screening limitations, including operator-dependency and missed detections, by integrating molecular and imaging approaches for improved CRC diagnosis. </sec> <sec> <title>METHODS</title> The study independently modeled two pathways: molecular analysis using transcriptomic data and machine learning models, and image segmentation employing the EMT-Net model with state-of-the-art metrics across various colonoscopy datasets. </sec> <sec> <title>RESULTS</title> Core diagnostic genes (e.g., CDC25B, TEAD4, MMP7) were identified with high stability and interpretability, achieving an AUROC of 0.987 and F1-score of 0.976. The EMT-Net model displayed superior segmentation performance compared to mainstream models on multiple datasets. </sec> <sec> <title>CONCLUSIONS</title> The fusion of molecular and imaging pathways in the AI-based CRC diagnosis strategy offers methodological innovation and potential clinical applications. The molecular pathway enhances interpretability, while the imaging pathway enhances precision, collectively forming a foundation for advanced and generalizable early CRC screening platforms. </sec>