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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

    • Guangdong
      • Guangzhou, Guangdong, China, 510120
        • Recruiting
        • Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Elective Thoracic Surgery Patients

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / 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

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

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

This is where you will find people and organizations involved with this study.

Sponsor

Collaborators

Investigators

  • Principal Investigator: Jianxing He, MD, Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2023

Primary Completion (Estimated)

September 30, 2024

Study Completion (Estimated)

December 30, 2024

Study Registration Dates

First Submitted

June 21, 2024

First Submitted That Met QC Criteria

June 26, 2024

First Posted (Actual)

June 27, 2024

Study Record Updates

Last Update Posted (Actual)

June 27, 2024

Last Update Submitted That Met QC Criteria

June 26, 2024

Last Verified

June 1, 2024

More Information

Terms related to this study

Other Study ID Numbers

  • ES-2024-091-02

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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