Digital Pathology Platform for Respiratory Tract Infection Diagnosis via Multiplex Single-Particle Detections
Akihide Arima, Makusu Tsutsui, Takeshi Yoshida, Kenji Tatematsu, Tomoko Yamazaki, Kazumichi Yokota, Shun’ichi Kuroda, Takashi Washio, Yoshinobu Baba, Tomoji Kawai
Abstract
The variability of bioparticles remains a key barrier to realizing the competent potential of nanoscale detection into a digital diagnosis of an extraneous object that causes an infectious disease. Here, we report label-free virus identification based on machine-learning classification. Single virus particles were detected using nanopores, and resistive-pulse waveforms were analyzed multilaterally using artificial intelligence. In the discrimination, over 99% accuracy for five different virus species was demonstrated. This advance is accessed through the classification of virus-derived ionic current signal patterns reflecting their intrinsic physical properties in a high-dimensional feature space. Moreover, consideration of viral similarity based on the accuracies indicates the contributing factors in the recognitions. The present findings offer the prospect of a novel surveillance system applicable to detection of multiple viruses including new strains.