快速适应的COVID-19诊断点的自动化解释
Siddarth Arumugam1, Jiawei Ma2, Uzay Macar2
1Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Communications medicine
|June 23, 2023
概括
自动调整POC使用机器学习,通过智能手机图像自动化医院诊断测试. 这种可扩展的解决方案可确保在各种测试套件中为未经培训的用户提供准确的结果.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习在诊断中的应用.
- 在医疗保健点测试技术的测试.
背景情况:
- 公众越来越多地使用临床诊断设备 (例如侧流检测).
- 确保正确运行和解释的现有方法缺乏可扩展性,并且需要对新测试格式进行广泛的专家标签.
研究的目的:
- 开发一个可扩展的软件架构,用于使用智能手机摄像头自动化对诊断测试的解释.
- 为了使未经培训的用户能够准确地解释各种诊断测试.
主要方法:
- 开发了AutoAdapt POC,集成了自动膜提取,自我监督学习和几次射击学习.
- 将预先训练的模型调整为五种不同的COVID-19测试,每套只使用20个标记图像.
主要成果:
- 在726次测试中,AutoAdapt POC实现了99%-100%的准确性.
- 在一项现实研究中,98%的未经培训的用户发现图像收集很容易,对COVID-19测试的准确性为100%.
- 该算法表现优于传统方法,正确识别了专家解释的结果的100%,并且需要少得多的培训数据.
结论:
- 机器学习中的快速域调整为医疗诊断点的质量保证提供了一个可扩展的解决方案.
- 这项技术可以加强对未经培训的个人使用的各种测试的护理和公共卫生跟踪的联系.
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