Current opinion in cardiology · 2026
The role of artificial intelligence in the diagnosis of pericardial constriction.
СтатьяОбзорНазвание и аннотация: Перевод пока не подготовлен
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.
Lipat Kevin, Booth Tyler, Sengupta Partho P
Аннотация
PURPOSE OF REVIEW: Constrictive pericarditis is a complex disease whose distinction from clinical mimickers remains challenging yet critically important. Diagnosis frequently requires integration of findings across multiple imaging modalities. Artificial intelligence (AI) applications in cardiac imaging are rapidly evolving and may play an increasingly important role in the diagnosis of constrictive pericarditis.
RECENT FINDINGS: Most established evidence supporting AI in diagnosis of constrictive pericarditis involves machine learning and deep learning applied to echocardiography, particularly for differentiating constrictive pericarditis from restrictive cardiomyopathy. Newer approaches incorporate multiple echocardiographic views and emphasize model generalizability. Emerging applications span cardiac computed tomography (CT)-for automated pericardial thickening and calcification quantification-large language models (LLMs) in cardiac magnetic resonance interpretation, and deep learning electrocardiogram analysis.
SUMMARY: Applications of AI for diagnosis of constrictive pericarditis are being studied across multiple imaging modalities, but remain in early stages of development. Emerging AI concepts, including multimodal LLMs and foundation models leveraging transfer learning, show promise for further advances and eventual meaningful implementation. Cardiac CT, though underrepresented in the current AI literature for constrictive pericarditis, represents an important target for future investigation given its established role in surgical planning.
RECENT FINDINGS: Most established evidence supporting AI in diagnosis of constrictive pericarditis involves machine learning and deep learning applied to echocardiography, particularly for differentiating constrictive pericarditis from restrictive cardiomyopathy. Newer approaches incorporate multiple echocardiographic views and emphasize model generalizability. Emerging applications span cardiac computed tomography (CT)-for automated pericardial thickening and calcification quantification-large language models (LLMs) in cardiac magnetic resonance interpretation, and deep learning electrocardiogram analysis.
SUMMARY: Applications of AI for diagnosis of constrictive pericarditis are being studied across multiple imaging modalities, but remain in early stages of development. Emerging AI concepts, including multimodal LLMs and foundation models leveraging transfer learning, show promise for further advances and eventual meaningful implementation. Cardiac CT, though underrepresented in the current AI literature for constrictive pericarditis, represents an important target for future investigation given its established role in surgical planning.