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

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.