Detection of COVID-19: A Smartphone-Based Machine-Learning-Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction.
Anal Chem
; 94(47): 16361-16368, 2022 Nov 29.
Article
in English
| MEDLINE | ID: covidwho-2119248
ABSTRACT
The unstoppable spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has severely threatened public health over the past 2 years. The current ubiquitously accepted method for its diagnosis provides sensitive detection of the virus; however, it is relatively time-consuming and costly, not to mention the need for highly skilled personnel. There is a clear need to develop novel computer-based diagnostic tools to provide rapid, cost-efficient, and time-saving detection in places where massive traditional testing is not practical. Here, we develop an electrochemiluminescence (ECL)-based detection system whose results are quantified as reverse transcriptase polymerase chain reaction (RT-PCR) cyclic threshold (CT) values. A concentration-dependent signal is generated upon the introduction of the virus to the electrode and is recorded with a smartphone camera. The ECL images are used to train machine learning algorithms, and a model using artificial neural networks (ANNs) for 45 samples was developed. The model demonstrated more than 90% accuracy in the diagnosis of 50 unknown real samples, detecting up to a CT value of 32 and a limit of detection (LOD) of 10-12 g mL-1 in the testing of artificial samples.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Anal Chem
Year:
2022
Document Type:
Article
Affiliation country:
Acs.analchem.2c03502
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