Frontiers in cardiovascular medicine · 2026
Artificial intelligence technology in aortic valve disease: a decade of scientometric and narrative review.
СтатьяСистематический обзорНазвание 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.
Hei Peng, Ren He, Ma Wenshuai, Fang Wei, Li Yan
Аннотация
BACKGROUND: Aortic valve disease, particularly aortic stenosis, poses a growing global health burden with aging populations. Artificial intelligence technology offers promising tools for diagnosis, risk stratification, and prognosis prediction, yet the knowledge structure of this interdisciplinary field remains unsystematically characterized.
OBJECTIVE: This study aims to conduct a scientometric analysis to delineate the research landscape, identify hotspots, and trace evolutionary trends of AI technology applications in aortic valve disease over the past decade.
METHODS: We retrieved relevant literature published between January 2016 and January 2026 from the Web of Science Core Collection and Scopus databases. After screening, 270 eligible articles were included. CiteSpace and VOSviewer were employed to perform visualization analyses of authors, institutions, countries, journals, keywords, and co-citation networks.
RESULTS: Annual publications increased steadily, with the United States leading in both output and influence. The Mayo Clinic emerged as the most prolific institution. Research hotspots focused on AI-assisted diagnosis, risk stratification, and prognosis prediction for aortic stenosis, primarily using deep learning and machine learning techniques. Keyword clustering revealed themes spanning disease diagnosis, therapeutic technologies, AI-enabled applications, and clinical outcomes. Co-citation analysis highlighted key studies on AI-enhanced electrocardiography and echocardiography for valve disease detection.
CONCLUSIONS: AI technology research in aortic valve disease is advancing rapidly. Based on the keyword clustering and timeline analysis, we propose a conceptual mapping of AI techniques onto clinical phases. Future efforts should prioritize developing multimodal models, facilitating clinical integration, and enhancing patient lifecycle management.
OBJECTIVE: This study aims to conduct a scientometric analysis to delineate the research landscape, identify hotspots, and trace evolutionary trends of AI technology applications in aortic valve disease over the past decade.
METHODS: We retrieved relevant literature published between January 2016 and January 2026 from the Web of Science Core Collection and Scopus databases. After screening, 270 eligible articles were included. CiteSpace and VOSviewer were employed to perform visualization analyses of authors, institutions, countries, journals, keywords, and co-citation networks.
RESULTS: Annual publications increased steadily, with the United States leading in both output and influence. The Mayo Clinic emerged as the most prolific institution. Research hotspots focused on AI-assisted diagnosis, risk stratification, and prognosis prediction for aortic stenosis, primarily using deep learning and machine learning techniques. Keyword clustering revealed themes spanning disease diagnosis, therapeutic technologies, AI-enabled applications, and clinical outcomes. Co-citation analysis highlighted key studies on AI-enhanced electrocardiography and echocardiography for valve disease detection.
CONCLUSIONS: AI technology research in aortic valve disease is advancing rapidly. Based on the keyword clustering and timeline analysis, we propose a conceptual mapping of AI techniques onto clinical phases. Future efforts should prioritize developing multimodal models, facilitating clinical integration, and enhancing patient lifecycle management.