Arumugam Siddarth, Ma Jiawei, Macar Uzay, Han Guangxing, McAulay Kathrine, Ingram Darrell, Ying Alex, Chellani Harshit Harpaldas, Chern Terry, Reilly Kenta, Colburn David A M, Stanciu Robert, Duffy Craig, Williams Ashley, Grys Thomas, Chang Shih-Fu, Sia Samuel K
Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Department of Computer Science, Columbia University, New York, NY, 10027, USA.
Commun Med (Lond). 2023 Jun 23;3(1):91. doi: 10.1038/s43856-023-00312-x.
Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.
We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images.
Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images.
The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.
即时检测诊断设备，如侧向流动分析检测，正被公众广泛使用。然而，确保检测操作正确和结果解读准确的工作依赖于难以扩展的硬件或需要大量训练数据集的图像处理方法，这就需要针对每种新的检测试剂盒格式进行大量测试以及使用经过验证的样本进行专家标注。
我们开发了一种名为AutoAdapt POC的软件架构，该架构集成了自动膜提取、自监督学习和少样本学习，以使用智能手机摄像头以可扩展的方式自动解读即时检测诊断测试。在单个侧向流动分析检测试剂盒上预训练的基础模型仅使用20个标注图像就能适配五种不同的新冠病毒检测（三种抗原检测、两种抗体检测）。
在此我们展示了AutoAdapt POC在726次检测（350次阳性、376次阴性）中准确率达到99%至100%。在一项有74名未经培训的用户进行自我检测的新冠病毒免下车检测研究中，98%的用户认为图像采集很容易，并且快速适配的模型在新冠病毒抗原和抗体检测试剂盒上的分类准确率均达到100%。与对105个检测试剂盒结果进行的传统视觉解读相比，该算法正确识别了所有图像；没有专家解读的假阴性情况。最后，与在艾滋病毒检测试剂盒上训练的传统卷积神经网络相比，该算法在仅需要其1/50的训练图像的情况下仍显示出高准确率。
该研究展示了机器学习中的快速领域适配如何能为未经培训的用户在各种即时检测诊断测试中提供质量保证、医疗关联和公共卫生追踪。
