efficacy comparison between independent auscultation by specialist physicians and ai-assisted auscultation by primary care physicians in large-scale screening for congenital heart disease: a cluster randomized controlled trial in china

Conditions: Congenital Heart Disease (CHD); Screening Tool; Artificial Intelligence (AI); Cluster Randomized Trial; Auscultation for Clinical Evaluation

Summary: in recent years, the integration of artificial intelligence (ai) into healthcare has accelerated, with deep learning emerging as a powerful tool in medicine. deep learning algorithms can automatically extract complex features and patterns, enabling highly accurate recognition of heart sounds. building on this foundation, professor sun kun and his team at xinhua hospital, affiliated with shanghai jiao tong university school of medicine, in collaboration with multiple centers across china, have been investigating the development and application of ai-assisted heart sound recognition for screening congenital heart disease (chd). by leveraging electronic stethoscopes to collect heart sounds and applying ai algorithms to extensive datasets from both children with chd and healthy children, the system has been trained to discern between normal and pathological murmurs, achieving an accuracy and sensitivity rate of 90%. this prospective cluster-randomized clinical trial is designed to assess the non-inferiority of ai-assisted primary healthcare physicians in identifying chd compared to specialist physicians conducting independent identification. through ongoing optimization and broad application, this research endeavors to equip general practitioners, child health physicians, and non-cardiovascular specialists with ai-assisted electronic auscultation capabilities, ultimately enhancing the standard of pediatric care nationwide. as the screening network expands, the project is expected to reduce missed diagnoses and increase detection rates in chd screening. moreover, the project aspires to offer feasible and cost-effective child health management solutions to other developing countries, contributing to global efforts to improve children's health.


Eligibility criteria:
inclusion criteria (schools): * the school is of the type: kindergarten, primary school, junior high school, or senior high school. * the school has medical screening facilities and conditions that can support ai-assisted screening. * the area where the school is located has at least one primary healthcare institution willing to participate in this trial. * the school's management and teaching staff are willing to participate in the study and can cooperate to complete the related screening and data collection work. exclusion criteria (schools): * more than half of the students in the school refuse to participate in the trial. * schools that are unable to complete the study due to severe limitations in geographical location and transportation conditions. * schools lacking medical screening facilities and conditions necessary for the implementation of screening. * areas where there are no primary healthcare institutions willing to participate in this trial. * schools whose management and teaching staff refuse to participate in the study or are unable to cooperate in completing the related screening and data collection work. inclusion criteria (individuals): * children aged between 0 and 18 years, regardless of gender. * children who agree to undergo echocardiography to determine the presence of congenital heart disease. * individuals who voluntarily participate in this study and sign the informed consent form. exclusion criteria (individuals): * individuals over the age of 18. * children who are unable to complete echocardiography or do not cooperate with auscultation. * participants who are unable to provide informed consent or are unwilling to comply with the study requirements to provide medical data for further analysis and research.