Shankar Sumukh Vasisht, Oikonomou Evangelos K, Khera Rohan
Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT.
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
medRxiv. 2023 Oct 3:2023.10.02.23296404. doi: 10.1101/2023.10.02.23296404.
In the rapidly evolving landscape of modern healthcare, the integration of wearable and portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams that were previously accessible only through devices only available to healthcare providers. Amidst the variety of data collected by these gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. Notably, there has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of rare cardiac disorders. This design study describes the development of an innovative multi-platform system aimed at the rapid deployment of AI-based ECG solutions for clinical investigation and care delivery. The study examines various design considerations, aligning them with specific applications, and develops data flows to maximize efficiency for research and clinical use. This process encompasses the reception of single-lead ECGs from diverse wearable devices, channeling this data into a centralized data lake, and facilitating real-time inference through AI models for ECG interpretation. An evaluation of the platform demonstrates a mean duration from acquisition to reporting of results of 33.0 to 35.7 seconds, after a standard 30 second acquisition, allowing the complete process to be completed in 63.0 to 65.7 seconds. There were no substantial differences in acquisition to reporting across two commercially available devices (Apple Watch and KardiaMobile). These results demonstrate the succcessful translation of design principles into a fully integrated and efficient strategy for leveraging 1-lead ECGs across platforms and interpretation by AI-ECG algorithms. Such a platform is critical to translating AI discoveries for wearable and portable ECG devices to clinical impact through rapid deployment.
在现代医疗保健迅速发展的格局中，可穿戴和便携式技术的整合为社区中的个性化健康监测提供了独特机遇。苹果手表、FitBit和AliveCor KardiaMobile等设备彻底改变了复杂健康数据流的采集和处理方式，这些数据流以前只能通过医疗保健提供者专用的设备获取。在这些小工具收集的各种数据中，单导联心电图（ECG）记录已成为监测心血管健康的关键信息来源。值得注意的是，能够解读这些单导联心电图的人工智能取得了重大进展，有助于临床诊断以及罕见心脏疾病的检测。这项设计研究描述了一个创新的多平台系统的开发，旨在快速部署基于人工智能的心电图解决方案用于临床研究和护理提供。该研究考察了各种设计考量因素，使其与特定应用相匹配，并开发数据流以最大限度提高研究和临床使用的效率。这个过程包括从各种可穿戴设备接收单导联心电图，将这些数据导入一个集中的数据湖，并通过人工智能模型进行实时推理以解读心电图。对该平台的评估表明，在进行标准的30秒采集后，从采集到报告结果的平均时长为33.0至35.7秒，整个过程可在63.0至65.7秒内完成。在两款市售设备（苹果手表和KardiaMobile）上，从采集到报告的时长没有实质性差异。这些结果表明，设计原则已成功转化为一种完全集成且高效的策略，用于跨平台利用单导联心电图并通过人工智能心电图算法进行解读。这样一个平台对于将可穿戴和便携式心电图设备的人工智能发现通过快速部署转化为临床影响至关重要。
