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Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT
Sponsor: The First Affiliated Hospital of Guangzhou Medical University
Summary
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.
Official title: Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function
Key Details
Gender
All
Age Range
18 Years - 75 Years
Study Type
OBSERVATIONAL
Enrollment
2000
Start Date
2023-10-01
Completion Date
2024-12-30
Last Updated
2024-06-27
Healthy Volunteers
No
Interventions
Single inspiratory 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.
Respiratory dual-phase computed tomography.
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.
Locations (1)
Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
Guangzhou, Guangdong, China