利用人工智能开发咳和PCR确认的COVID-19感染的预测算法,这些算法基于临床级可穿戴传感器的输入
Jessica R Walter1, Jong Yoon Lee2,3, Lian Yu2,3
1Department of Obstetrics and Gynecology, Northwestern University, Chicago, IL, USA.
Scientific reports
|April 5, 2024
概括
一个可穿戴的监测系统,ANNE One,显示了早期COVID-19检测的希望. 人工智能算法分析生理数据,包括咳计数,改善了高风险个体的疾病识别.
科学领域:
- 数字健康数字健康
- 医学中的人工智能
- 可穿戴技术是可穿戴的技术.
背景情况:
- 全球有超过7.69亿个COVID-19病例.
- 高达50%的感染者无症状,使检测复杂化.
- 需要新的方法来早期识别和监测疾病.
研究的目的:
- 评估ANNE 一个用于人工智能驱动的COVID-19检测的可穿戴系统.
- 开发用于咳检测和早期COVID-19识别的算法.
- 评估用于高风险人群疾病监测的生理数据.
主要方法:
- 从ANNE One传感器收集的前性数据的回顾性分析.
- 有两种方法:咳检测算法和COVID-19检测算法.
- 包括重要参数 (心率,呼吸率,咳数) 和症状调查.
主要成果:
- 咳检测算法在健康受试者中获得了F-1得分83.3%.
- 在325名高风险个体中,COVID-19检测算法显示0.47的灵敏度和0.72的特异性.
- 参与者的高度坚持 (≥4天/周佩戴) 证明了可行性.
结论:
- 安妮一款可穿戴系统展示了COVID-19检测的潜力.
- 结合呼吸系统生物标志物,如咳计数,可以提高预测准确度.
- 可穿戴设备为早期疾病检测和监测提供了宝贵的工具.
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