Detection of COVID-19: A Smartphone-Based Machine-Learning- Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction

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.

Automated summary

The rapid spread of SARS-CoV-2 has raised the need for efficient and rapid diagnostic tools. In this study, an electrochemiluminescence (ECL)-based detection system was developed and trained using machine learning algorithms to accurately diagnose COVID-19 with over 90% accuracy. The system provides a cost-effective and time-saving alternative to traditional testing methods.