Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

Masateru Taniguchi (), Shohei Minami, Chikako Ono, Rina Hamajima, Ayumi Morimura, Shigeto Hamaguchi, Yukihiro Akeda, Yuta Kanai, Takeshi Kobayashi, Wataru Kamitani, Yutaka Terada, Koichiro Suzuki, Nobuaki Hatori, Yoshiaki Yamagishi, Nobuei Washizu, Hiroyasu Takei, Osamu Sakamoto, Norihiko Naono, Kenji Tatematsu, Takashi Washio, Yoshiharu Matsuura () and Kazunori Tomono ()
Additional contact information
Masateru Taniguchi: Osaka University
Shohei Minami: Osaka University
Chikako Ono: Osaka University
Rina Hamajima: Osaka University
Ayumi Morimura: Osaka University
Shigeto Hamaguchi: Osaka University
Yukihiro Akeda: Osaka University
Yuta Kanai: Osaka University
Takeshi Kobayashi: Osaka University
Wataru Kamitani: Gunma University
Yutaka Terada: University of Pittsburgh
Koichiro Suzuki: The Research Foundation for Microbial Diseases of Osaka University
Nobuaki Hatori: The Research Foundation for Microbial Diseases of Osaka University
Yoshiaki Yamagishi: Osaka University
Nobuei Washizu: ADVANTEST Corporation
Hiroyasu Takei: Aipore Inc.
Osamu Sakamoto: Aipore Inc.
Norihiko Naono: Aipore Inc.
Kenji Tatematsu: Osaka University
Takashi Washio: Osaka University
Yoshiharu Matsuura: Osaka University
Kazunori Tomono: Osaka University

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Date: 2021
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DOI: 10.1038/s41467-021-24001-2

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