Abdominal Diseases
Conditions
Keywords
Artificial Intelligence, abdominal diseases, contrast-enhanced CT, RADAR
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.
Interventions
None listed
Sponsors
First Affiliated Hospital of Zhejiang University
the First Division Hospital of Xinjiang Production and Construction Corps
The First People's Hospital of Yuhang District
Affiliated Hospital of Jiaxing University
Jixi County People's Hospital
Anji County People's Hospital
Zhejiang University
People's Hospital of Beilun District, Ningbo City
Haining People's Hospital
Jingning County People's Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Outcome results
None listed