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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SCREENING
Masking
NONE
Enrollment
212
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.
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.
Qinghai Provincial Women and Children's Hospital
Qinghai, China
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Shanghai, China
Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
Time frame: From enrollment to the end of treatment at 3 months
Specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: From enrollment to the end of treatment at 3 months
The rate of diagnostic revisions by physicians, the proportions of correct and incorrect changes
Time frame: From enrollment to the end of treatment at 3 months
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