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Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound

NCT06423066 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2024-05-21

No results posted yet for this study

Summary

This study aims to investigate the accuracy of using pleural ultrasound (USP) to identify pleural adhesions in patients who plan to receive video-assisted thoracoscopic surgery. It employs three-dimensional convolutional neural network (3D-CNN) technology to process USP-related images and video data for machine learning, and to establish a diagnostic model for identifying pleural adhesions using 3D-CNN-USP. The study will determine the sensitivity, specificity, positive predictive value, and negative predictive value of 3D-CNN-USP in identifying pleural adhesions. Additionally, it will explore the feasibility and effectiveness of using 3D-CNN-USP for preoperative identification of pleural adhesions in VATS, thereby supporting the implementation of day surgery in thoracic surgery and ultimately serving clinical practice.

Conditions

Interventions

DIAGNOSTIC_TEST

Pleural ultrasound

Patients who examine pleural ultrasound preoperatively.

Sponsors & Collaborators

  • Peking Union Medical College Hospital

    lead OTHER

Principal Investigators

  • Shanqing Li, PhD, and MD · Peking Union Medical College Hospital

  • Qingli Zhu, PhD, and MD · Peking Union Medical College Hospital

Eligibility

Min Age
12 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-06-01
Primary Completion
2025-12-30
Completion
2026-03-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06423066 on ClinicalTrials.gov