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Developing Echocardiography Image Quality Management System Based on Deep Learning

NCT05633732 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2023-02-23

No results posted yet for this study

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.

Conditions

  • Echocardiography

Sponsors & Collaborators

  • Southeast University, China

    collaborator OTHER

  • The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-12-30
Primary Completion
2024-12-31
Completion
2025-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05633732 on ClinicalTrials.gov