Support Vector Machine for Color Classification of RNA

The fast developments in artificial intelligence have allowed scientists to use it for the high-throughput analysis of medical data to assist clinicians in diagnosis and health monitoring tasks. Machine learning has the potential to complement point-of-care diagnosis when coupled with readout techniques, such as colorimetry. Here, a support vector machine (SVM) is used for the rapid analysis of colorimetric images, readout to a point-of-care viral RNA detection device. We used a paradigm of viral respiratory infection, Covid-19, to illustrate SVM capabilities for diagnosis. In the test set, the SVM achieves a 94% success rate in its classification of healthy vs patients after 10 minutes. This point-of-care system would help to prevent the fast spread of infectious diseases through rapid screening operations.