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:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2022-12-30', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-09', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-02-21', 'studyFirstSubmitDate': '2022-11-21', 'studyFirstSubmitQcDate': '2022-11-21', 'lastUpdatePostDateStruct': {'date': '2023-02-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-12-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'the score of PSAX view', 'timeFrame': '12 months', 'description': 'the score of PSAX view by the echocardiography image quality management system'}, {'measure': 'the score of apical view', 'timeFrame': '12 months', 'description': 'the score of apical view by the echocardiography image quality management system'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Echocardiography', 'Quality Management System', 'Deep learning', 'Artificial Intelligence'], 'conditions': ['Echocardiography']}, 'referencesModule': {'references': [{'pmid': '30157525', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '30553684', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '30506448', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '30828647', 'type': 'BACKGROUND', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'patients with standardized TTE views', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. aged ≥18years, gender unlimited;\n2. Patients with standardized TTE views;\n3. Subjects participated in the study voluntarily and signed informed consent;\n\nExclusion Criteria:\n\n1. patients wirh incomplete standard TTE views;\n2. patients with poor sound transmission conditions.'}, 'identificationModule': {'nctId': 'NCT05633732', 'briefTitle': 'Developing Echocardiography Image Quality Management System Based on Deep Learning', 'organization': {'class': 'OTHER', 'fullName': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School'}, 'officialTitle': 'Echocardiography Image Quality Management System Based on Deep Learning: A Single-center Prospective Study', 'orgStudyIdInfo': {'id': '2022-337-01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Standardized View Group', 'description': 'The echocardiography view images of patients in this group are standardized.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '210008', 'city': 'Nanjing', 'state': 'Jiangsu', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jing Yao, Phd', 'role': 'CONTACT', 'email': 'w18351992709@163.com', 'phone': '+18905188727'}], 'facility': 'Affiliated Drum Tower Hospital of Nanjing University Medical School', 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}], 'centralContacts': [{'name': 'Jing Yao, Phd', 'role': 'CONTACT', 'email': 'w1835199709@163.com', 'phone': '+8618905188727'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School', 'class': 'OTHER'}, 'collaborators': [{'name': 'Southeast University, China', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}