Clinical trial NCT06791096: Efficacy Comparison Between Primary Care Physicians' Independent Auscultation and AI-assisted Auscultation for Congenital Heart Disease Screening in Patient-enriched Populations: a Randomized Controlled Trial
Official title: Efficacy Comparison Between Primary Care Physicians' Independent Auscultation and AI-assisted Auscultation for Congenital Heart Disease Screening in Patient-enriched Populations: a Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Congenital Heart Disease (CHD)
Screening Tool
Artifical Intelligence
Randomised Controlled Trial
Auscultation for Clinical Evaluation
Lead sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
Collaborators: name: Bill and Melinda Gates Foundation
First posted: 2025-01-24
Enrollment: 420
Sex: ALL
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
timeFrame: From enrollment to the end of treatment at 3 months
Secondary outcomes:
measure: Specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
timeFrame: From enrollment to the end of treatment at 3 months
measure: Specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & AI-Assisted Auscultation By Primary Care Physician
timeFrame: From enrollment to the end of treatment at 3 months
measure: Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI Model
timeFrame: From enrollment to the end of treatment at 3 months
measure: Specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & Primary Care Physicians' Independent Auscultation
timeFrame: From enrollment to the end of treatment at 3 months
measure: Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & AI Model
timeFrame: From enrollment to the end of treatment at 3 months
measure: The rate of diagnostic revisions by physicians, the proportions of correct and incorrect changes
timeFrame: From enrollment to the end of treatment at 3 months
Other outcomes:
Locations:
city: Qinghai
country: China
facility: Qinghai Provincial Women and Children's Hospital
city: Shanghai
country: China
facility: Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Study officials:
References: