Rapid Abdominal Diagnosis With AI & Radiology

Active, not recruiting

• Conditions: Abdominal Diseases

• Registration Number: NCT07040358
• Lead Sponsor: First Affiliated Hospital of Zhejiang University

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-12-01
• Study First Submitted: 2025-06-19
• Study First Submitted QC: 2025-06-19
• Study First Posted: 2025-06-27
• Status Verified: 2025-09
• Last Update Submitted: 2025-09-14
• Last Update Posted: 2025-09-18
• Primary Completion: 2025-12
• Study Completion: 2026-06-01

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 2000000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT	After internal and external validation datasets are processed (estimated 6-12 months)	The primary outcome is the overall performance of the AI model in detecting and characterizing lesions in abdominal organs using multiphase contrast-enhanced CT scans. Performance will be measured using area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity, with expert radiologist consensus reports as the reference standard.

Trial Locations

Locations (1)

the First Affliated Hospital, Zhejiang University School of Medicine; 🇨🇳 Hangzhou, Zhejiang, China