Study on AI Recognition System Of Heart Sound In Congenital Heart Disease Screening

NCT ID: NCT04307030

Last Updated: 2023-04-21

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

5000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-07-01

Study Completion Date

2023-06-30

Brief Summary

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The objective of this study is to establish AI algorithm based on the deep learning to strengthen the ability to classify the heart murmurs of healthy people and different major or other subdivided congenital heart diseases(CHDs) and to evaluate the effectiveness of artificial intelligence technology-assisted heart sound recognition system (referred to as: Heart sound AI recognition system) for multi-center CHD screening.

Detailed Description

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This is a multi-center cluster cross-sectional study in CHINA. Heart sounds will be collected by auscultation using an electronic stethoscope in children (0 \~ 18 years old) confirmed with or without CHDs by echocardiography during outpatient or hospitalization in 10 pediatric medical centers. Heart sounds will be visualized as phonocardiogram, and feature extraction will be done after classification of normal and abnormal heart sounds and labeling the characteristics of heart murmurs by pediatric cardiovascular specialists. Artificial intelligence algorithm (machine learning, deep learning, etc.) will be trained to build a heart sounds recognition system with the data mentioned above.We will use the receiver operating characteristic (ROC) curve to compare the ability of recognition and classification of abnormal heart sounds between different artificial intelligence algorithm. Taken the results of echocardiography as the gold standard, we will use the evaluation indexes,such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity of CHD screening between the AI recognition system and human cardiovascular pediatricians. Our target is to use artificial intelligence technology to assist heart auscultation for CHD screening.

Conditions

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Congenital Heart Disease in Children

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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0 ~ 18 years old children

Children During Outpatient or Hospitalization

Heart Auscultation and Echocardiography

Intervention Type DIAGNOSTIC_TEST

Heart auscultation will be done by cardiovascular pediatrician and echocardiography by echocardiologist

Interventions

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Heart Auscultation and Echocardiography

Heart auscultation will be done by cardiovascular pediatrician and echocardiography by echocardiologist

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

1. 0 \~ 18 years of age, regardless of gender ;
2. Children with or without congenital heart disease confirmed by echocardiography;
3. On the basis of informed consent,willing to cooperate with our group.

Exclusion Criteria

1. ≥ 18 years of age;
2. Children who can not undergo echocardiography or other related tests;
3. Subjects who refuse to join in, or who are unwilling to cooperate with the provision of diagnostic and therapeutic data for further analysis and research.
Maximum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

OTHER

Sponsor Role lead

Responsible Party

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Kun Sun

Professor of Department of Pediatric Cardiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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KUN SUN, MD

Role: PRINCIPAL_INVESTIGATOR

Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

Locations

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Beijing Anzhen Hospital

Beijing, , China

Site Status RECRUITING

Children's Hospital Affiliated to Chongqing Medical University

Chongqing, , China

Site Status RECRUITING

The First Affiliated Hospital of Guangxi Medical University

Guangxi, , China

Site Status RECRUITING

Guangzhou Women and Children's Medical Center

Guangzhou, , China

Site Status RECRUITING

Children's Hospital Affiliated to Zhejiang Medical University

Hangzhou, , China

Site Status RECRUITING

Hunan Children's Hospital

Hunan, , China

Site Status RECRUITING

Shandong Provincial Hospital

Jinan, , China

Site Status RECRUITING

Kunming Children's Hospital

Kunming, , China

Site Status RECRUITING

Lanzhou University Second Hospital

Lanzhou, , China

Site Status RECRUITING

Linyi Hospital for Women and Children

Linyi, , China

Site Status RECRUITING

Children's Hospital of Shanghai

Shanghai, , China

Site Status RECRUITING

Shanghai Children's Medical Center

Shanghai, , China

Site Status RECRUITING

Shiyan Taihe Hospital

Shiyan, , China

Site Status RECRUITING

Children's Hospital of Soochow University

Suzhou, , China

Site Status RECRUITING

The Second Hospital Affiliated to Wenzhou Medical University

Wenzhou, , China

Site Status RECRUITING

Wuhan Children's Hospital

Wuhan, , China

Site Status RECRUITING

Countries

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China

Central Contacts

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KUN SUN, MD

Role: CONTACT

8621-25076045

Facility Contacts

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Wei Li

Role: primary

0086-13436312289

Jia Liu

Role: primary

0086-13438054141

Suyuan Tan

Role: primary

0086-13768510719

Wei Li

Role: primary

0086-18100202077

Yujia Wang

Role: primary

0086-13588747101

Zhi Chen, MD

Role: primary

0086-13787028209

Jianli Lv, MD

Role: primary

0086-15562565679

Nan Zheng

Role: primary

0086-17387744720

Jin Wang

Role: primary

0086-13993129500

Shiqiang Wu

Role: primary

0539-3216251

Wei Liu

Role: primary

0086-17702159185

Tianyi Ji, MD

Role: primary

0086-15317026159

Shibing Xi

Role: primary

0086-13397298282

Qiuqin Xu

Role: primary

0086-13915538909

Xing Rong, MD

Role: primary

0577-88002178

Jia Fu

Role: primary

0086-15827103182

Other Identifiers

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XH-20-003

Identifier Type: -

Identifier Source: org_study_id

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