Congenital Heart Disease in Children
Conditions
Keywords
Congenital Heart Disease, Artificial Intelligence, Heart Sound
Brief summary
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
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.
Interventions
DIAGNOSTIC_TESTHeart Auscultation and Echocardiography
Heart auscultation will be done by cardiovascular pediatrician and echocardiography by echocardiologist
Sponsors
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
No minimum to 18 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Receiver operating characteristic (ROC) of sensitivity | July 2020 to December 2021 | ROC of sensitivity in CHD screening by different artificial intelligence algorithm and auscultation |
Countries
China
Contacts
PRINCIPAL_INVESTIGATORKUN SUN, MD
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Outcome results
None listed