Clinical trial NCT04307030: Study on AI Recognition System Of Heart Sound In Congenital Heart Disease Screening
Official title: Multi-center Study on Exploration and Application of Artificial Intelligence Technology-Assisted Heart Sound Recognition System in Children's Congenital Heart Disease Screening
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Congenital Heart Disease in Children
Keywords: Congenital Heart Disease
Artificial Intelligence
Heart Sound
Lead sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2020-03-13
Enrollment: 5000
Sex: ALL
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Pediatric population during outpatient service or hospitalization in ten pediatric medical centers in CHINA

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Receiver operating characteristic (ROC) of sensitivity
timeFrame: July 2020 to December 2021
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Beijing Anzhen Hospital
city: Chongqing
country: China
facility: Children's Hospital Affiliated to Chongqing Medical University
city: Guangxi
country: China
facility: The First Affiliated Hospital of Guangxi Medical University
city: Guangzhou
country: China
facility: Guangzhou Women and Children's Medical Center
city: Hangzhou
country: China
facility: Children's Hospital Affiliated to Zhejiang Medical University
city: Hunan
country: China
facility: Hunan Children's Hospital
city: Jinan
country: China
facility: Shandong Provincial Hospital
city: Kunming
country: China
facility: Kunming Children's Hospital
city: Lanzhou
country: China
facility: Lanzhou University Second Hospital
city: Linyi
country: China
facility: Linyi Hospital for Women and Children
city: Shanghai
country: China
facility: Children's Hospital of Shanghai
city: Shanghai
country: China
facility: Shanghai Children's Medical Center
city: Shiyan
country: China
facility: Shiyan Taihe Hospital
city: Suzhou
country: China
facility: Children's Hospital of Soochow University
city: Wenzhou
country: China
facility: The Second Hospital Affiliated to Wenzhou Medical University
city: Wuhan
country: China
facility: Wuhan Children's Hospital
Study officials:
affiliation: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
name: KUN SUN, MD
role: PRINCIPAL_INVESTIGATOR
References: