Clinical Research Directory

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

Sponsor: Peking Union Medical College Hospital

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.

Official title: Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound: A Prospective Observational Study

Key Details

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Interventions

Locations (1)