Year: 2025
Type: article
Abstract: Effective first-trimester screening for congenital heart disease (CHD) remains an unmet clinical need, hampered by technical limitations and the absence of validated diagnostic tools. To address this, we collected a vast cohort of 108,521 first-trimester cardiac screenings conducted across multiple regions in China, from which 8062 Doppler flow four-chamber view images were selected. Using this curated dataset, we developed and validated an interpretable deep learning (DL) model that mimics clinical reasoning with diastolic flow patterns, providing accurate and explainable CHD diagnosis in first-trimester. Interpretability analyses confirmed its diagnostic logic strongly aligns with clinical expertise. In rigorous evaluations, the model demonstrated high accuracy across multiple external validation datasets, matched or surpassed experienced clinicians, and showed potential to augment their diagnostic capabilities. To our knowledge, this is the first validated interpretable DL system for first-trimester CHD screening, potentially enabling earlier intervention through an advanced diagnostic window.
Source: npj Digital Medicine
Institutions: Capital Medical University, Beijing Obstetrics and Gynecology Hospital, Wuhan University, Third Affiliated Hospital of Zhengzhou University, Guangxi Maternal and Child Health Hospital, Central South University, Xiangya Hospital Central South University, Changsha Hospital for Maternal and Child Health Care, Chinese PLA General Hospital, Northwest Women's and Children's Hospital
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Awards: G5705517997, G8400922664
OpenAlex ID: W4417102946