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NCT Number: NCT07040358

Rapid Abdominal Diagnosis With AI & Radiology

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

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Key information

Conditions

Abdominal Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

the First Affliated Hospital, Zhejiang University School of Medicine

Hangzhou, Zhejiang, 310003, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

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

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital of Zhejiang University

Other

Collaborators

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

Registry information

Official study title

Development and Application of an AI Model for Accurate Interpretation of Abdominal Enhanced CT Images

Acronym: RADAR

Important dates

Study start
2023
Primary completion
2025
Study completion
2026
First posted
Jun 27, 2025
Registry last updated
Mar 5, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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