An observational study in Elective Thoracic Surgery, Pulmonary Function and Deep Learning, sponsored by The First Affiliated Hospital of Guangzhou Medical University. Recruiting at 1 site in China. Open to participants aged 18 Years to 75 Years. Per ClinicalTrials.gov, last updated 2024-06-27.
Sponsored by The First Affiliated Hospital of Guangzhou Medical University · Observational
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.
386 studies on the registry are indexed under Aortic Dissection; 117 are open to participants now.
This study's planned enrollment of 2,000 is above the median of 183 across 180 observational studies indexed under Aortic Dissection.
Browse Aortic Dissection studies →The First Affiliated Hospital of Guangzhou Medical University is the lead sponsor of 158 studies on the registry; 65 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Elective Thoracic Surgery Patients
Exclusion Criteria:
Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively.
Other: Single inspiratory phase computed tomography.
Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively.
Other: Respiratory dual-phase computed tomography.
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.
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.
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).
Time frame: 2 years
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).
Time frame: 2 years
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The First Affiliated Hospital of Guangzhou Medical University