Performance evaluation of machine learning-based infectious screening flags on the HORIBA Medical Yumizen H550 Haematology Analyzer for vivax malaria and dengue fever

Background Automated detection of malaria and dengue infection has been actively researched for more than two decades. Although many improvements have been achieved, these solutions remain too expensive for most laboratories and clinics in developing countries. The low range HORIBA Medical Haematology Analyzer, Yumizen H550, now provides dedicated flags ‘vivax malaria’ and ‘dengue fever’ in routine blood testing, developed through machine learning methods, to be used as a screening tool for malaria and dengue fever in endemic areas. This study sought to evaluate the effectiveness of these flags under real clinical conditions. Methods A total of 1420 samples were tested using the Yumizen H550 Haematology Analyzer, including 1339 samples from febrile patients among whom 202 were infected with malaria parasites ( Plasmodium vivax only: 182, Plasmodium falciparum only: 18, both: 2), 210 were from febrile dengue infected patients, 3 were from afebrile dengue infected patients and 78 were samples from healthy controls, in an outpatient laboratory clinic in Mumbai, India. Microscopic examination was carried out as the confirmatory reference method for detection of malarial parasite, species identification and assessing parasitaemia based on different stages of parasite life cycle. Rapid diagnostic malarial antigen tests were used for additional confirmation. For dengue infection, NS1 antigen detection by ELISA was used as a diagnostic marker. Results For the automated vivax malaria flag, the original manufacturer’s cut off yielded a sensitivity and specificity of 65.2% and 98.9% respectively with the ROC AUC of 0.9. After optimization of cut-off value, flag performance improved to 72% for sensitivity and 97.9% specificity. Additionally it demonstrated a positive correlation with increasing levels of parasitaemia. For the automated dengue fever flag it yielded a ROC AUC of 0.82 with 79.3% sensitivity and 71.5% specificity. Conclusions The results demonstrate a possibility of the effective use of automated infectious flags for screening vivax malaria and dengue infection in a clinical setting.