EchocardiographyNCT05633732
Developing Echocardiography Image Quality Management System Based on Deep Learning
An observational study of Echocardiography, run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School at 1 site in China.
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Echocardiography
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 30 Dec 2022to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: patients with standardized TTE views
Who may be able to join
- aged ≥18years, gender unlimited;
- Patients with standardized TTE views;
- Subjects participated in the study voluntarily and signed informed consent;
Who cannot join
- patients wirh incomplete standard TTE views;
- patients with poor sound transmission conditions.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Affiliated Drum Tower Hospital of Nanjing University Medical School
Nanjing, Jiangsu, 210008
Jing Yao, Phd, +18905188727, w18351992709@163.com
About this study
Official title: 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.
Read the 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
- Keywords
- Echocardiography, Quality Management System, Deep learning, Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Standardized View Group | The echocardiography view images of patients in this group are standardized. |
What is being measured
The primary outcome is the main question the study is designed to answer.
the score of PSAX view
the score of PSAX view by the echocardiography image quality management system
Measured: 12 months
the score of apical view
the score of apical view by the echocardiography image quality management system
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Dec 2022
- Study start30 Dec 2022
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for EchocardiographyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
- Sponsor type
- University, hospital or other
- Collaborators
- Southeast University, China
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05633732
- Sponsor's protocol ID
- 2022-337-01
- Status last verified
- Sep 2022
- First submitted
- 21 Nov 2022
- Last updated
- 23 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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.
PubMed 30157525Sengupta 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.
PubMed 30553684Ueda 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.
PubMed 30506448Madani 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.
PubMed 30828647
Study at a glance
From the registry record, last updated by the sponsor on 23 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Echocardiography Image Quality Management System Based on Deep Learning: A Single-center Prospective Study
- ClinicalTrials.gov ID
- NCT05633732
- Acronym / sponsor ID
- 2022-337-01
- Conditions
- Echocardiography
- Keywords
- Echocardiography, Quality Management System, Deep learning, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical SchoolUniversity, hospital or other
- Collaborators
- Southeast University, China
- Study start
- 30 Dec 2022 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 1 Dec 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Standardized View Group | – | –The echocardiography view images of patients in this group are standardized. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- patients with standardized TTE views
Key inclusion criteria (3 listed)
- aged ≥18years, gender unlimited;
- Patients with standardized TTE views;
- Subjects participated in the study voluntarily and signed informed consent;
Key exclusion criteria (2 listed)
- patients wirh incomplete standard TTE views;
- patients with poor sound transmission conditions.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
the score of PSAX view
Time frame: 12 months
the score of PSAX view by the echocardiography image quality management system
the score of apical view
Time frame: 12 months
the score of apical view by the echocardiography image quality management system
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Feb 2023.
- Background: PubMed 30157525Thiebaut 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.
- Background: PubMed 30553684Sengupta 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.
- Background: PubMed 30506448Ueda 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.
- Background: PubMed 30828647Madani 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Echocardiography, run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 23 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jing Yao, Phd (+8618905188727, w1835199709@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the score of PSAX view (time frame: 12 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05633732, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Feb 2023.