Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT
Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Jianxing He, MD
- Phone Number: 86-20-83337792
- Email: drjianxing.he@gmail.com
Study Locations
-
-
Guangdong
-
Guangzhou, Guangdong, China, 510120
- Recruiting
- Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
-
Contact:
- Jianxing He, MD
- Phone Number: 86-20-83337792
- Email: drjianxing.he@gmail.com
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- (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;
- (5) Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging;
- (6) The interval between preoperative pulmonary function and single/dual phase CT scans does not exceed one month.
Exclusion Criteria:
- (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.);
- (5) Coexisting with other severe functional impairments;
- (6) Patients with obstructive lesions such as airway or esophageal stenosis;
- (7) Height beyond the predicted equation range (Female < 1.45m; Male < 1.55m);
- (8) Medication use before pulmonary function testing that does not meet the cessation guidelines;
- (9) Pulmonary function report quality graded D-F.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Single inspiratory phase cohort
Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively.
|
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 cohort
Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Mean Absolute Error(MAE)
Time Frame: 2 years
|
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
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Concordance Correlation Coefficient(CCC)
Time Frame: 2 years
|
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
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Jianxing He, MD, Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- ES-2024-091-02
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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