Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.

ABSTRACT:

Objectives

Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligence (AI) algorithms have been employed for imaging diagnoses. In this study, we examined the sensitivity of neoplastic lesions in CT colonography images.

Methods

Lesion location and size were evaluated during colonoscopy and a large-scale database including a dataset for AI learning and external validation was created. The DICOM data used as training data and internal validation data (total 453 patients) for this study were colorectal cancer screening test data from two multicenter joint trial conducted in Japan and data from two in

SUBMITTER: Endo S 

PROVIDER: S-EPMC11889859 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature