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 sars-cov-2 has severely threatened public health over the past two 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 ecl-based detection system with a sensitivity comparable to that of rt-pcr. 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 ann for 45 samples was developed. the model demonstrated more than 90% accuracy in the diagnosis of 50 unknown samples, detecting upto a ct value of 32.