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
This trial does not accept healthy volunteersQualifiers
(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 population
Elective Thoracic Surgery Patients
Trial design
Design model
Other
Time perspective
Prospective
Treatments tested in this trial
Single inspiratory phase computed tomography.
Other interventionUtilizing 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.
Other interventionUtilizing deep learning technology in conjunction with respiratory dual-phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients.
Treatment groups
2,000 Participants
are divided into 2 treatment groupsGroup A: Single inspiratory phase cohort1 intervention
Group B: Respiratory dual-phase cohort1 intervention
Trial outcomes
Primary outcomes
1
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 yearsSecondary outcomes
1
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 yearsSponsors and contacts
Click on the lead sponsor to view all of their trials.
The First Affiliated Hospital of Guangzhou Medical University
Lead sponsor
GE Healthcare
Collaborator
Source ClinicalTrials.gov