Using deep learning to assess lung function before thoracic surgery

Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function

This study is testing a new way to use advanced computer technology with CT scans to better predict lung function in patients getting ready for thoracic surgery.

Quick facts

• Study type: Observational
• Enrollment: 2000 (estimated)
• Ages: 18 Years to 75 Years
• Sex: All
• Sponsor: The First Affiliated Hospital of Guangzhou Medical University (Academic / other)
• Locations: 1 site (Guangzhou, Guangdong)
• Trial ID: NCT06477458 on ClinicalTrials.gov

What this trial studies

This observational study aims to leverage deep learning technology alongside computed tomography (CT) images to accurately predict pulmonary function indicators in patients scheduled for elective thoracic surgery. By optimizing a model based on a large dataset of CT scans, the study seeks to address the limitations of traditional pulmonary function tests, which can be time-consuming and prone to inaccuracies. The approach involves using single inspiratory phase CT scans and transferring model parameters from dual-phase respiratory CTs to enhance predictive capabilities, ultimately providing a more efficient and personalized assessment for patients. This innovative methodology could streamline preoperative evaluations and improve surgical outcomes.

Who should consider this trial

Good fit: Ideal candidates are adults aged 18-75 who are undergoing elective thoracic surgery and can cooperate with preoperative pulmonary function tests.

Not a fit: Patients with severe respiratory disorders or significant artefacts in their CT scans may not benefit from this study.

Why it matters

Potential benefit: If successful, this approach could lead to more accurate preoperative assessments, reducing the risk of complications during thoracic surgery.

How similar studies have performed: While the use of deep learning in medical imaging is gaining traction, this specific application for preoperative pulmonary assessment is relatively novel and has not been extensively tested in prior studies.

Eligibility criteria

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.

Where this trial is running

Guangzhou, Guangdong

• Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College — Guangzhou, Guangdong, China (Recruiting)

Study contacts

• Principal investigator: Jianxing He, MD — Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
• Study coordinator: Jianxing He, MD

Conditions Elective Thoracic Surgery; Pulmonary Function; Deep Learning