Developing Echocardiography Image Quality Management System Based on Deep Learning

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

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.

Study Overview

• Status: Recruiting
• Conditions: Echocardiography

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 Type: Observational
• Enrollment (Anticipated): 2000

Contacts and Locations

• Study Contact: Name: Jing Yao, Phd; Phone Number: +8618905188727; Email: w1835199709@163.com

Study Locations

• China
  • Jiangsu
    • Nanjing, Jiangsu, China, 210008
      • Recruiting
      • Affiliated Drum Tower Hospital of Nanjing University Medical School
      • Contact: Jing Yao, Phd; Phone Number: +18905188727; Email: w18351992709@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

patients with standardized TTE views

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Standardized View Group; The echocardiography view images of patients in this group are standardized.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
the score of PSAX view	the score of PSAX view by the echocardiography image quality management system	12 months
the score of apical view	the score of apical view by the echocardiography image quality management system	12 months

Collaborators and Investigators

• Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
• Collaborators: Southeast University, China

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): December 30, 2022
• Primary Completion (Anticipated): December 31, 2024
• Study Completion (Anticipated): December 31, 2025

Study Registration Dates

• First Submitted: November 21, 2022
• First Submitted That Met QC Criteria: November 21, 2022
• First Posted (Actual): December 1, 2022

Study Record Updates

• Last Update Posted (Estimate): February 23, 2023
• Last Update Submitted That Met QC Criteria: February 21, 2023
• Last Verified: September 1, 2022

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Echocardiography; Deep learning; Quality Management System
• Other Study ID Numbers: 2022-337-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No