Developing Echocardiography Image Quality Management System Based on Deep Learning

Echocardiography Clinical Trial

Official title:

Echocardiography Image Quality Management System Based on Deep Learning: A Single-center Prospective Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05633732
• Other study ID #: 2022-337-01
• Status: Recruiting
• Start date: December 30, 2022
• Est. completion date: December 31, 2025

Study information

• Verified date: September 2022
• Source: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
• Contact: Jing Yao, Phd
• Phone: +8618905188727
• Email: w1835199709[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

To develop an echocardiography image quality management system based on deep learning to achieve objective and accurate automatic echocardiography image quality control. A total of 2000 patients performing transthoracic echocardiography were prospectively enrolled in the Department of Ultrasound Medicine of the Affiliated Drum Tower Hospital with Medical School of Nanjing University. The data of 8 TTE view segmentations were collected, including the views of the parasternal long axis of the left ventricle (PLAX_LV), parasternal short axis of the large vessel level (PSAX_GV), parasternal short axis of the mitral valve level (PSAX_MV), parasternal short axis of the papillary muscle level (PSAX_PM), parasternal short axis of the apical level (PSAX_AP), apical four cavity (A4C), apical three cavity (A3C), apical two cavity (A2C). The data of 1500 patients were used as the training set, and the rest were used as the validation set. These video data were classified into corresponding view segmentations and analyzed by the Video Swin Transformed Model. Then, the scoring module of different view segmentations combined key frame extraction, image segmentation, video target recognition and video classification model were established. At the same time, the scores achieved by the automatic echocardiography image assessment system were compared with the artificial score. By constantly correcting and learning and eventually building an primary automated grading system. At last, the automatic echocardiography image assessment system was constructed and performed on the rest 500 patients.

Description:

To develop an echocardiography image quality management system based on deep learning to achieve objective and accurate automatic echocardiography image quality control. A total of 2000 patients performing transthoracic echocardiography were prospectively enrolled in the Department of Ultrasound Medicine of the Affiliated Drum Tower Hospital with Medical School of Nanjing University. The inclusion criteria: Patients with standardized TTE view segmentation; The exclusion criteria: Patients with incomplete standard segmentations. The data of 8 TTE view segmentations were collected, including the views of the parasternal long axis of the left ventricle (PLAX_LV), parasternal short axis of the large vessel level (PSAX_GV), parasternal short axis of the mitral valve level (PSAX_MV), parasternal short axis of the papillary muscle level (PSAX_PM), parasternal short axis of the apical level (PSAX_AP), apical four cavity (A4C), apical three cavity (A3C), apical two cavity (A2C). The data of 1500 patients were used as the training set, and the rest were used as the validation set. These video data were classified into corresponding view segmentations and analyzed by the Video Swin Transformed Model. Then, the scoring module of different view segmentations combined key frame extraction, image segmentation, video target recognition and video classification model were established. At the same time, the scores achieved by the automatic echocardiography image assessment system were compared with the artificial score. By constantly correcting and learning and eventually building an primary automated grading system. At last, the echocardiography image quality management system was performed on the rest 500 patients and improved.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2000
• Est. completion date: December 31, 2025
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. aged =18years, gender unlimited;

2. Patients with standardized TTE views;

3. Subjects participated in the study voluntarily and signed informed consent;

Exclusion Criteria:

1. patients wirh incomplete standard TTE views;

2. patients with poor sound transmission conditions.

Related Conditions & MeSH terms

• Echocardiography

Locations

Country	Name	City	State
China	Affiliated Drum Tower Hospital of Nanjing University Medical School	Nanjing	Jiangsu

Sponsors (2)

Lead Sponsor	Collaborator
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School	Southeast University, China

Country where clinical trial is conducted

China, 

References & Publications (4)

Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1:6. doi: 10.1038/s41746-017-0013-1. Epub 2018 Mar 21. — View Citation

Sengupta PP, Shrestha S. Machine Learning for Data-Driven Discovery: The Rise and Relevance. JACC Cardiovasc Imaging. 2019 Apr;12(4):690-692. doi: 10.1016/j.jcmg.2018.06.030. Epub 2018 Dec 12. No abstract available. — View Citation

Thiebaut R, Thiessard F; Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology Informatics. Artificial Intelligence in Public Health and Epidemiology. Yearb Med Inform. 2018 Aug;27(1):207-210. doi: 10.1055/s-0038-1667082. Epub 2018 Aug 29. — View Citation

Ueda D, Shimazaki A, Miki Y. Technical and clinical overview of deep learning in radiology. Jpn J Radiol. 2019 Jan;37(1):15-33. doi: 10.1007/s11604-018-0795-3. Epub 2018 Dec 1. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	the score of PSAX view	the score of PSAX view by the echocardiography image quality management system	12 months
Primary	the score of apical view	the score of apical view by the echocardiography image quality management system	12 months