Development and Application of an AI Model for Accurate Interpretation of Abdominal Enhanced CT Images
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 2,000,000
- 试验地点
- 1
- 主要终点
- Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •multiphase contrast-enhanced abdominal CT covering the full abdominal region and corresponding radiology reports matched to the CT images
排除标准
- •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.
研究组 & 干预措施
Internal Training Set
Internal Validation Set
External Test Set
结局指标
主要结局
Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT
时间窗: 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.
次要结局
未报告次要终点
研究者
Qi Zhang
Professor
First Affiliated Hospital of Zhejiang University