Frontiers in cardiovascular medicine · 2026
Heartbeat: a multimodal dataset of fetal echocardiography and clinical metadata for early detection of congenital heart disease.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Rodríguez Santiago, Pérez Alejandra, Echeverry Lina Marcela, Castillo Ángela, Ramírez Nataly Alejandra, Escobar María, Guarín Monroy Sofía, Vega Daniela, Rodríguez Nicolás, Castro-Páez Camila, Navarro Javier, Domínguez María Teresa, Laverde Nicolás, Sarmiento Luis Andrés, Afanador Daniel, D'silva Londoño Liz, Torres Narváez Erika, Fandiño María Juliana, Madrid Antonio José, Quintero Juan Carlos, Rodríguez Nadiezhda, Briceño Juan Carlos, Arbeláez Pablo
Аннотация
BACKGROUND: Congenital heart diseases (CHDs) remain the leading cause of infant mortality attributable to birth defects. Although artificial intelligence has demonstrated promise for automated CHD detection from prenatal ultrasound imaging, progress is constrained by the limited availability of large, high-quality datasets and the absence of multimodal resources integrating imaging with clinically relevant patient metadata.
METHODS: This study introduces Heartbeat, a multicenter, anonymized multimodal dataset comprising fetal cardiac ultrasound images and associated clinical metadata collected between 2019 and 2023. Standard echocardiographic views, including the three-vessel trachea, four-chamber, left ventricular outflow tract, and right ventricular outflow tract planes, were acquired according to established protocols. Multiple deep learning architectures, including convolutional neural networks and a transformer-based baseline, were trained and evaluated. Heart-ViT, a multimodal transformer-based model, was developed to integrate imaging features with patient-specific clinical metadata via adaptive layer normalization.
RESULTS: Heartbeat includes 1,475 patients, with CHD prevalences of 6.50% and 7.25% in the second and third trimesters, respectively. On the second-trimester test set ( n = 61 ), Heart-ViT achieved a sensitivity of 79.17%, specificity of 93.18%, PPV of 56.95%, AUROC of 88.56%, and an F1-score of 65.63%, outperforming multiple image-only convolutional neural network architectures and baseline models. Multimodal integration yielded a 13.22-point improvement in F1-score relative to the imaging-only transformer baseline.
CONCLUSION: Heartbeat provides a clinically relevant multimodal resource for prenatal CHD research that reflects real-world imaging variability and class imbalance. Integrating ultrasound imaging with patient-specific clinical metadata significantly enhances CHD detection compared with image-only approaches. These findings establish a reproducible benchmark and support the development of AI-assisted tools for early prenatal screening. The dataset and analytical framework will be made publicly available to support further research in prenatal cardiology and the early detection of CHDs.
METHODS: This study introduces Heartbeat, a multicenter, anonymized multimodal dataset comprising fetal cardiac ultrasound images and associated clinical metadata collected between 2019 and 2023. Standard echocardiographic views, including the three-vessel trachea, four-chamber, left ventricular outflow tract, and right ventricular outflow tract planes, were acquired according to established protocols. Multiple deep learning architectures, including convolutional neural networks and a transformer-based baseline, were trained and evaluated. Heart-ViT, a multimodal transformer-based model, was developed to integrate imaging features with patient-specific clinical metadata via adaptive layer normalization.
RESULTS: Heartbeat includes 1,475 patients, with CHD prevalences of 6.50% and 7.25% in the second and third trimesters, respectively. On the second-trimester test set ( n = 61 ), Heart-ViT achieved a sensitivity of 79.17%, specificity of 93.18%, PPV of 56.95%, AUROC of 88.56%, and an F1-score of 65.63%, outperforming multiple image-only convolutional neural network architectures and baseline models. Multimodal integration yielded a 13.22-point improvement in F1-score relative to the imaging-only transformer baseline.
CONCLUSION: Heartbeat provides a clinically relevant multimodal resource for prenatal CHD research that reflects real-world imaging variability and class imbalance. Integrating ultrasound imaging with patient-specific clinical metadata significantly enhances CHD detection compared with image-only approaches. These findings establish a reproducible benchmark and support the development of AI-assisted tools for early prenatal screening. The dataset and analytical framework will be made publicly available to support further research in prenatal cardiology and the early detection of CHDs.