Automated Detection of COVID-19 Using Machine Learning Analysis of White Blood Cell Flow Cytometry Images

COVID-19 is usually diagnosed by real-time polymerase chain reaction testing of nasopharyngeal swabs, but this approach may be associated with a prolonged time to diagnosis. Machine learning incorporating findings from standard laboratory tests may more rapidly identify individuals infected with SARS-CoV-2, enabling early management and protection for healthcare staff.

A machine learning model was developed to detect patients infected with COVID-19 using only white blood cell flow cytometry without using symptom or clinical history data. The model included images from 106 patients positive for SARS-CoV-2 and 211 controls admitted to a university hospital with respiratory symptoms. A total of 17 texture-feature analysis methods were tested using three classifiers. Finally, the Particle Swarm Optimization algorithm was used to combine the best-performing models. The performance of the final model was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, precision, and F1-Score.

Using the five-fold cross-validation technique, the final model achieved an accuracy of 88.96% for diagnosing patients with a SARS-CoV-2 infection. It also had a sensitivity of 78.30%, a specificity of 94.31%, a precision of 87.83%, an F1-score of 0.83, and an AUROC of 0.86.

The final algorithm showed good diagnostic performance even when compared with models that included clinical, epidemiological, and other laboratory or ancillary test data. The use of cytometry images could represent a significant advance in the early diagnosis of COVID-19.