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| Name | Class |
|---|---|
| GE Healthcare | INDUSTRY |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Single inspiratory phase cohort | Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively. |
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| Respiratory dual-phase cohort | Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Single inspiratory phase computed tomography. | Other | Utilizing deep learning technology in conjunction with single inspiratory phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients. |
| Measure | Description | Time Frame |
|---|---|---|
| Mean Absolute Error(MAE) | 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). | 2 years |
| Measure | Description | Time Frame |
|---|---|---|
| Concordance Correlation Coefficient(CCC) | 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). | 2 years |
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Inclusion Criteria:
Exclusion Criteria:
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Elective Thoracic Surgery Patients
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jianxing He, MD | Contact | 86-20-83337792 | drjianxing.he@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Jianxing He, MD | Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College | Recruiting | Guangzhou | Guangdong | 510120 | China |
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| Respiratory dual-phase computed tomography. | Other | Utilizing deep learning technology in conjunction with respiratory dual-phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients. |
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