Rapid Abdominal Diagnosis With AI & Radiology

NCT07040358 | Active Not Recruiting

• 1 other identifier: RADAR
• Study Type: observational
• Target: 2,000,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Abdominal Diseases

Keywords

Artificial Intelligence; abdominal diseases; contrast-enhanced CT; RADAR

Outcome Measures

Primary Outcomes (1)

• Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT

  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.

  After internal and external validation datasets are processed (estimated 6-12 months)

Study Arms (3)

Internal Training Set

Internal Validation Set

External Test Set

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• First Affiliated Hospital of Zhejiang University: lead
• the First Division Hospital of Xinjiang Production and Construction Corps: collaborator
• The First People's Hospital of Yuhang District: collaborator
• Affiliated Hospital of Jiaxing University: collaborator
• Jixi County People's Hospital: collaborator
• Anji County People's Hospital: collaborator
• Zhejiang University: collaborator
• People's Hospital of Beilun District, Ningbo City: collaborator
• Haining People's Hospital: collaborator
• Jingning County People's Hospital: collaborator

Study Sites (1)

the First Affliated Hospital, Zhejiang University School of Medicine

Hangzhou, Zhejiang, 310003, China

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 19, 2025
• First Posted: June 27, 2025
• Study Start: December 1, 2023
• Primary Completion: September 30, 2025
• Study Completion (Estimated): June 1, 2026
• Last Updated: March 5, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)