This study aims to develop an AI-assisted diagnostic system for abdominal contrast-enhanced CT images using data from multiple inpatient centers. In collaboration with Alibaba DAMO Academy, the project will address key mathematical challenges limiting current automated image interpretation, including feature space alignment, hybrid reasoning, and multimodal report generation. The study includes the following components: (1) construction of a dual-modality foundation model to align abdominal CT features with corresponding radiology reports; (2) development of a model to standardize CT phase variation among patients; and (3) creation of an automated image interpretation and reporting system that integrates multi-source clinical data. The effectiveness of the system will be evaluated through a report quality assessment framework and clinical validation. This project aims to improve the accuracy and clinical applicability of automated abdominal disease interpretation and promote intelligent innovation in healthcare delivery.

Inclusion Criteria:

• multiphase contrast-enhanced abdominal CT covering the full abdominal region and corresponding radiology reports matched to the CT images

Exclusion Criteria:

• CT images with poor diagnostic quality due to artifacts, including but not limited to: Convolution artifacts caused by improper arm positioning (e.g., arms placed alongside the body instead of above the head),Respiratory motion artifacts due to inadequate breath-holding.

This study uses a retrospective multicenter cohort comprising approximately 2 million cases of multiphase contrast-enhanced abdominal CT scans. All included imaging data are paired with corresponding radiology reports. The dataset reflects real-world imaging scenarios of various abdominal diseases.