Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-75
SponsorThe First Affiliated Hospital of Guangzhou Medical University
About this trial
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.
Eligibility criteria
Qualifiers
(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;
Disqualifiers
(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.);
Trial design
Treatments tested in this trial
- Single inspiratory phase computed tomography.
- Respiratory dual-phase computed tomography.
Treatment groups
2,000 Participants
are divided into 2 treatment groupsSponsors and collaborators
The First Affiliated Hospital of Guangzhou Medical University
Lead sponsor
GE Healthcare
Collaborator
Source ClinicalTrials.gov