Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 2,000
- 试验地点
- 1
- 主要终点
- Mean Absolute Error（MAE）
研究概览
简要总结
The trial was designed as a single-centre, non-interventional prospective observational study to utilize deep learning technology combined with computed tomography (CT) images to precisely predict the pulmonary function indicators of thoracic surgery preoperative patients.
详细描述
Preoperative pulmonary function tests are crucial in assessing perioperative complications or mortality risks and providing decision support for thoracic surgery. However, traditional pulmonary function assessment methods have significant limitations, including long testing durations, difficulties in patient cooperation, high false-negative rates, and numerous contraindications. Thus, our study optimized the final model based on 1500 single inspiratory phase CTs by transferring model parameters trained on 500 dual-phase respiratory CTs, enhancing its predictive capabilities for pulmonary function. This adjustment suits real-world application demands, offering more convenient, comprehensive, and personalized preoperative pulmonary function assessment support. Our study optimized the final model based on 1500 single inspiratory phase CTs by transferring model parameters trained on 500 dual-phase respiratory CTs, enhancing its predictive capabilities for pulmonary function. This adjustment suits real-world application demands, offering more convenient, comprehensive, and personalized preoperative pulmonary function assessment support.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •(1) Signing of the informed consent form;
- •(2) Male or female, aged 18-75 years;
- •(3) Undergoing elective thoracic surgery;
- •(4) Good preoperative pulmonary function cooperation and complete reporting;
- •(5) Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging;
- •(6) The interval between preoperative pulmonary function and single/dual phase CT scans does not exceed one month.
排除标准
- •(1) Poor preoperative pulmonary function cooperation or missing reports;
- •(2) Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission;
- •(3) The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month;
- •(4) Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);
- •(5) Coexisting with other severe functional impairments;
- •(6) Patients with obstructive lesions such as airway or esophageal stenosis;
- •(7) Height beyond the predicted equation range (Female < 1.45m; Male < 1.55m);
- •(8) Medication use before pulmonary function testing that does not meet the cessation guidelines;
- •(9) Pulmonary function report quality graded D-F.
结局指标
主要结局
Mean Absolute Error（MAE）
时间窗: 2 years
Used to assess the discrepancy between pulmonary function predictions made by the deep learning algorithm and actual results obtained from pulmonary function tests (measured with a spirometer).
次要结局
- Concordance Correlation Coefficient（CCC）(2 years)
研究者
Jianxing He
Director
The First Affiliated Hospital of Guangzhou Medical University