AI models can be trained to diagnose COVID-19 using routine laboratory blood tests, according to new research published in Biomedical Signal Processing and Control.
“With new mutations of the virus with higher transmission rates, it is imperative to diagnose positive cases as quickly and accurately as possible,” wrote first author Samin Babaei Rikan, a specialist with the department of computer engineering at Urmia University in Iran, and colleagues. “Therefore, a fast, accurate, and automatic system for COVID-19 diagnosis can be very useful for clinicians.”
The group noted that PCR tests, chest X-rays and chest CT scans are all currently used to diagnose COVID-19—but all three have their own faults that make them less than ideal. To see if they could potentially provide clinicians with another option, Rikan et al. tested the ability of seven machine learning models and four deep learning models to diagnose COVID-19 using routing blood tests. Each model was developed and trained using three different datasets of blood tests that contained positive and negative COVID-19 cases.
Overall, the group had the most consistent success with the deep neural network (DNN) model. For the three datasets, its accuracy was measured as 92.11%, 93.16% and 93.33%, respectively. The model’s sensitivity (96.14%, 93.27% and 77.05%) and specificity (84.56%, 93.02% and 95.27%) for the three different datasets results were also especially strong.
The model’s area under the ROC curve for the three datasets, meanwhile, were 92.2%, 93.2% and 85.97%. The DNN was also faster and more efficient than any other AI model, the authors added.
“Based on the obtained results, it can be said that the DNN model proposed in this study is among the most accurate and fastest models introduced in the literature to date,” the authors wrote. “Our proposed model is an automated tool that can help clinicians to diagnose the COVID-19 disease. Using AI-based models for COVID-19 diagnosis is more accurate than traditional methods that require experience and time to diagnose.”
Read the full analysis here.