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Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT

Recruiting
Conditions
Elective Thoracic Surgery
Pulmonary Function
Deep Learning
Registration Number
NCT06477458
Lead Sponsor
The First Affiliated Hospital of Guangzhou Medical University
Brief 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.

Detailed Description

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.

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
2000
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 & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Mean Absolute Error(MAE)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).

Secondary Outcome Measures
NameTimeMethod
Concordance Correlation Coefficient(CCC)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).

Trial Locations

Locations (1)

Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College

🇨🇳

Guangzhou, Guangdong, China

Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
🇨🇳Guangzhou, Guangdong, China
Jianxing He, MD
Contact
86-20-83337792
drjianxing.he@gmail.com

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