Ignite Creation Date:
2025-12-24 @ 9:44 PM
Ignite Modification Date:
2025-12-25 @ 7:24 PM
Study NCT ID:
NCT05633732
Status:
RECRUITING
Last Update Posted:
2023-02-23
First Post:
2022-11-21
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Developing Echocardiography Image Quality Management System Based on Deep Learning
Sponsor:
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Organization:
Study Overview
Official Title:
Echocardiography Image Quality Management System Based on Deep Learning: A Single-center Prospective Study
Status:
RECRUITING
Status Verified Date:
2022-09
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:
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.
Study Oversight
Has Oversight DMC:
False
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?:
None
Is an FDA AA801 Violation?: