Developing Echocardiography Image Quality Management System Based on Deep Learning

NCT ID: NCT05633732

Last Updated: 2023-02-23

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-12-30
• Study Completion Date: 2025-12-31

Brief 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.

Detailed 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.

Conditions

Echocardiography

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

Standardized View Group

The echocardiography view images of patients in this group are standardized.

No interventions assigned to this group

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Southeast University, China

OTHER

Sponsor Role: collaborator

The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Affiliated Drum Tower Hospital of Nanjing University Medical School

Nanjing, Jiangsu, China

Site Status: RECRUITING

Countries

China

Central Contacts

Jing Yao, Phd

Role: CONTACT

w1835199709@163.com

+8618905188727

Facility Contacts

Jing Yao, Phd

Role: primary

w18351992709@163.com

+18905188727

References

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Reference Type: BACKGROUND

PMID: 30157525 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 30553684 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 30506448 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 30828647 (View on PubMed)

Other Identifiers

2022-337-01

Identifier Type: -

Identifier Source: org_study_id