Artifical Intelligence, Auscultation for Clinical Evaluation, Congenital Heart Disease (CHD), Randomised Controlled Trial, Screening Tool
Conditions
Brief summary
In recent years, the application of artificial intelligence (AI) in the healthcare domain has witnessed a significant surge, with deep learning emerging as a potent force in the medical field. Deep learning algorithms possess the remarkable ability to automatically extract intricate features and patterns, thereby facilitating highly accurate heart sound recognition. Drawing on this technological advancement, Professor Sun Kun and his research team from Xinhua Hospital, in collaboration with numerous centers spanning across China, have been diligently investigating the development and application of AI-assisted heart sound recognition for congenital heart disease (CHD) screening. Utilizing electronic stethoscopes to meticulously collect heart sounds, and harnessing AI algorithms to analyze extensive datasets comprising heart sounds from both children diagnosed with CHD and those who are healthy, the system has been trained to adeptly differentiate between normal and pathological murmurs. The current iteration of the system boasts an impressive accuracy and sensitivity rate of 90%. This study is designed as a randomized controlled trial (RCT) to be conducted at Shanghai Xinhua Hospital and Qinghai Provincial Women and Children's Hospital. The primary objective is to demonstrate the superiority of AI-assisted primary care physicians in identifying CHD over primary care physicians working independently. This will be achieved by conducting a comparative analysis of the performance of AI-assisted physicians versus their unassisted counterparts, thereby substantiating the model's practical applicability. Through an ongoing process of refinement and widespread application, this pioneering research endeavors to empower a diverse range of medical professionals, including general practitioners, child health physicians, and non-cardiovascular specialists, with the transformative capabilities of AI-assisted electronic auscultation. The ultimate goal is to elevate the standard of pediatric care across the nation.
Interventions
DIAGNOSTIC_TESTAI-assisted auscultation
The study begins with non-blinded staff collecting medical histories and specialist physicians conducting face-to-face auscultations and assessments. Then, primary care doctors will conduct face-to-face auscultations and first assessments, and use AI-assisted stethoscopes to collect heart sounds following a set protocol. The AI model will analyze the data in real-time and provides an immediate diagnostic result, which is relayed back to the primary care physicians. Based on this, they will make a secondary assessment. All participants will undergo echocardiography.
DIAGNOSTIC_TESTIndependent auscultation
It includes medical history collection by non-blinded independent personnel, face-to-face auscultation and evaluations conducted by specialist physicians and primary care doctors separately. All participants will undergo echocardiography.
Sponsors
Kun Sun
Bill and Melinda Gates Foundation
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
No minimum to 18 Years
Healthy volunteers
Yes
Inclusion criteria
* Age between 0 to 18 years, with no gender restrictions. * Children who consent to undergo echocardiography to determine the presence or absence of congenital heart disease. * Voluntary participation in this study and signing of an informed consent form.
Exclusion criteria
* Age greater than 18 years. * Children who are unable to undergo echocardiography or who do not cooperate with auscultation. * Participants who cannot provide informed consent or are unwilling to comply with study requirements to provide medical data for further analysis and research.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation | From enrollment to the end of treatment at 3 months |
Secondary
| Measure | Time frame |
|---|---|
| Specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation | From enrollment to the end of treatment at 3 months |
| Specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & AI-Assisted Auscultation By Primary Care Physician | From enrollment to the end of treatment at 3 months |
| Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI Model | From enrollment to the end of treatment at 3 months |
| Specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & Primary Care Physicians' Independent Auscultation | From enrollment to the end of treatment at 3 months |
| Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & AI Model | From enrollment to the end of treatment at 3 months |
| The rate of diagnostic revisions by physicians, the proportions of correct and incorrect changes | From enrollment to the end of treatment at 3 months |
Countries
China
Outcome results
None listed