Ignite Creation Date:
2024-06-16 @ 11:48 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06423066
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-05-21
First Post:
2024-05-15
Brief Title:
Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound
Sponsor:
Peking Union Medical College Hospital
Organization:
Peking Union Medical College Hospital
Study Overview
Official Title:
Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound a Prospective Observational Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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
Detailed Description:
Lung cancer is currently the leading cause of death from malignant tumors worldwide Surgery is the primary treatment method for lung cancer and breakthroughs in thoracic surgical techniques play a crucial role in the diagnosis and treatment of lung cancer Medical ultrasound technology due to its non-invasive flexible convenient and economical characteristics is widely used in clinical practice With the advancement of ultrasound technology and the need to address clinical challenges ultrasound imaging technology has seen new developments in the field of thoracoscopic surgery
In the era of minimally invasive surgery intraoperative pleural adhesions are one of the main factors affecting the implementation of video-assisted thoracoscopic surgery VATS Especially under the concept of enhanced recovery after surgery ERAS the day surgery model for VATS has gradually taken shape However pleural adhesions significantly increase intraoperative trauma and prolong hospital stays Additionally pleural adhesions increase the risk of lung injury during VATS and in severe cases may hinder access to the pleural space necessitating conversion to open thoracotomy Pleural adhesions increase intraoperative time and morbidity in thoracic surgery due to poor visibility bleeding and lung and vascular injuries The presence location and degree of pleural adhesions are useful for determining the initial port placement or choosing between open or VATS approaches Therefore accurately predicting the presence and specific location of pleural adhesions preoperatively is crucial for the development of day surgery under thoracic ERAS ensuring the safety and efficiency of future VATS day surgeries
Previous studies have shown that chest CT is difficult to predict pleural adhesions with a sensitivity of only 72 and a sensitivity of only 46 for determining adhesions at specific locations In contrast ultrasonography of the pleura USP can dynamically display pleural sliding and adhesions with surrounding lung tissue and has real-time monitoring capabilities based on movement providing unique advantages for detecting pleural adhesions Preoperative prediction of pleural adhesions using USP has significant application value Studies have already demonstrated the advantages of using transthoracic pleural ultrasound to identify pleural adhesions Nicola et al conducted 1192 ultrasounds to predict pleural adhesions confirming 1124 positive cases and 68 negative cases with a sensitivity of 806 specificity of 961 positive predictive value of 732 and negative predictive value of 974 However there are still some issues with using USP to predict pleural adhesions Physicians who can identify pleural adhesions need to be trained in lung ultrasound and ultrasound examination and interpretation are skill-dependent techniques The burden of training thoracic ultrasound physicians remains a clinical challenge
Three-dimensional convolutional neural network 3D-CNN technology is an emerging technology in the field of artificial intelligence and machine learning Unlike traditional convolutional neural networks CNN 3D-CNN can process three-dimensional data that includes a time dimension making it suitable for analyzing the real-time dynamic image features of ultrasound images This technology holds promise for developing a machine learning model to interpret USP images potentially replacing physician interpretation and improving the accuracy of USP in identifying pleural adhesions
In summary this study intends to use USP for preoperative identification of pleural adhesions in patients scheduled for VATS surgery It aims to investigate the accuracy of USP in predicting intraoperative pleural adhesions and to develop a diagnostic model using 3D-CNN technology to process USP-related images and video data for machine learning The study will explore the sensitivity specificity positive predictive value and negative predictive value of the 3D-CNN-USP model in identifying pleural adhesions Additionally it will examine the feasibility and effectiveness of using 3D-CNN-USP for preoperative identification of pleural adhesions to support the implementation of day surgery in thoracic surgery
In the era of minimally invasive surgery intraoperative pleural adhesions are one of the main factors affecting the implementation of video-assisted thoracoscopic surgery VATS Especially under the concept of enhanced recovery after surgery ERAS the day surgery model for VATS has gradually taken shape However pleural adhesions significantly increase intraoperative trauma and prolong hospital stays Additionally pleural adhesions increase the risk of lung injury during VATS and in severe cases may hinder access to the pleural space necessitating conversion to open thoracotomy Pleural adhesions increase intraoperative time and morbidity in thoracic surgery due to poor visibility bleeding and lung and vascular injuries The presence location and degree of pleural adhesions are useful for determining the initial port placement or choosing between open or VATS approaches Therefore accurately predicting the presence and specific location of pleural adhesions preoperatively is crucial for the development of day surgery under thoracic ERAS ensuring the safety and efficiency of future VATS day surgeries
Previous studies have shown that chest CT is difficult to predict pleural adhesions with a sensitivity of only 72 and a sensitivity of only 46 for determining adhesions at specific locations In contrast ultrasonography of the pleura USP can dynamically display pleural sliding and adhesions with surrounding lung tissue and has real-time monitoring capabilities based on movement providing unique advantages for detecting pleural adhesions Preoperative prediction of pleural adhesions using USP has significant application value Studies have already demonstrated the advantages of using transthoracic pleural ultrasound to identify pleural adhesions Nicola et al conducted 1192 ultrasounds to predict pleural adhesions confirming 1124 positive cases and 68 negative cases with a sensitivity of 806 specificity of 961 positive predictive value of 732 and negative predictive value of 974 However there are still some issues with using USP to predict pleural adhesions Physicians who can identify pleural adhesions need to be trained in lung ultrasound and ultrasound examination and interpretation are skill-dependent techniques The burden of training thoracic ultrasound physicians remains a clinical challenge
Three-dimensional convolutional neural network 3D-CNN technology is an emerging technology in the field of artificial intelligence and machine learning Unlike traditional convolutional neural networks CNN 3D-CNN can process three-dimensional data that includes a time dimension making it suitable for analyzing the real-time dynamic image features of ultrasound images This technology holds promise for developing a machine learning model to interpret USP images potentially replacing physician interpretation and improving the accuracy of USP in identifying pleural adhesions
In summary this study intends to use USP for preoperative identification of pleural adhesions in patients scheduled for VATS surgery It aims to investigate the accuracy of USP in predicting intraoperative pleural adhesions and to develop a diagnostic model using 3D-CNN technology to process USP-related images and video data for machine learning The study will explore the sensitivity specificity positive predictive value and negative predictive value of the 3D-CNN-USP model in identifying pleural adhesions Additionally it will examine the feasibility and effectiveness of using 3D-CNN-USP for preoperative identification of pleural adhesions to support the implementation of day surgery in thoracic surgery
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None