Computer methods and programs in biomedicine · 2026
Development and clinical usability assessment of a web-based AI platform for automated echocardiography analysis.
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.
Nazari Mojdeh, Emami Hassan, Rabiei Reza, Rabiee Hamid R, Salari Arsalan, Sadr Hossein
Аннотация
BACKGROUND: Echocardiography is the cornerstone of cardiovascular diagnosis, yet its manual interpretation is labor-intensive and prone to inter-observer variability. While Deep Learning (DL) offers expert-level potential, existing models struggle with clinical generalization due to domain shifts and are often limited to single-view analysis, failing to provide the comprehensive assessment required in real-world practice.
OBJECTIVES: To overcome these limitations, this study aimed to design, develop, and clinically evaluate EchoAI, a secure, browser-based Clinical Decision Support System to bridge the gap between high-performance DL algorithms and routine echocardiography analysis.
METHODS: We developed a secure web-based framework that integrates our previously validated, multi-task UDA-VAE engine capable of simultaneous quantification of Left Ventricular Ejection Fraction (LVEF) and Wall Thickness across multiple standard acoustic windows (A4C, A2C, PLAX). Uniquely, the platform employs a User-Centered Design with a "Human-in-the-loop" workflow, transforming the AI from a "black box" into a transparent assistant that allows physicians to visualize and verify segmentation masks in real-time.
RESULTS: A multicenter clinical validation involving 18 cardiologists and residents across an academic hospital and a private cardiac center demonstrated real-time performance with an average processing time of 1.15 s per cycle across diverse ultrasound vendors. The system achieved a strong correlation with expert measurements (r = 0.98, P < 0.001) and a negligible bias of 0.12%. Usability assessment yielded a high overall satisfaction score (6.20/7). Notably, physicians accepted 86% of the AI-generated outputs without modification (84% in the academic setting and 88% in the private sector), confirming the system's robust reliability and cross-domain adaptability.
CONCLUSION: EchoAI demonstrates that integrating vendor-agnostic, domain-adaptive AI into an intuitive, interactive web interface effectively bridges the gap between algorithmic capability and clinical adoption. This multicenter approach significantly reduces manual workload while fostering the high level of clinical trust necessary for routine deployment across diverse healthcare settings.
OBJECTIVES: To overcome these limitations, this study aimed to design, develop, and clinically evaluate EchoAI, a secure, browser-based Clinical Decision Support System to bridge the gap between high-performance DL algorithms and routine echocardiography analysis.
METHODS: We developed a secure web-based framework that integrates our previously validated, multi-task UDA-VAE engine capable of simultaneous quantification of Left Ventricular Ejection Fraction (LVEF) and Wall Thickness across multiple standard acoustic windows (A4C, A2C, PLAX). Uniquely, the platform employs a User-Centered Design with a "Human-in-the-loop" workflow, transforming the AI from a "black box" into a transparent assistant that allows physicians to visualize and verify segmentation masks in real-time.
RESULTS: A multicenter clinical validation involving 18 cardiologists and residents across an academic hospital and a private cardiac center demonstrated real-time performance with an average processing time of 1.15 s per cycle across diverse ultrasound vendors. The system achieved a strong correlation with expert measurements (r = 0.98, P < 0.001) and a negligible bias of 0.12%. Usability assessment yielded a high overall satisfaction score (6.20/7). Notably, physicians accepted 86% of the AI-generated outputs without modification (84% in the academic setting and 88% in the private sector), confirming the system's robust reliability and cross-domain adaptability.
CONCLUSION: EchoAI demonstrates that integrating vendor-agnostic, domain-adaptive AI into an intuitive, interactive web interface effectively bridges the gap between algorithmic capability and clinical adoption. This multicenter approach significantly reduces manual workload while fostering the high level of clinical trust necessary for routine deployment across diverse healthcare settings.