Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Abstract

Background Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models

“…In this retrospective analysis, we used six state-of-the-art machine learning models to predict asymptomatic CAS subjects based on 19 input features, which were selected according to binary logistic regression. Among the six models, LR achieved an AUCROC of 0.809, an accuracy of 74.7%, and a F1 score of 59.9% when validating in the separate hold-out testing set, generating the optimal predictive model for data, which was in keeping with other studies using machine learning techniques to assess clinical events [ 34 , 35 ]. This accuracy was equivalent to or exceeded that of other evaluating tools yet used in neurology [ 36 ].…”

Predicting the impact of outdoor vector control interventions on malaria transmission intensity from semi-field studies

“…Therefore, spatial repellents may be combined with odour-baited traps [ 20 ] to form a push–pull system, with the goal of diverting mosquitoes to a trap to kill them instead of diverting them to another host. Earlier studies found a significant reduction of house-entry by push–pull systems compared to push–only systems [ 21 , 22 ], while studies focusing on outdoor biting found that the push–pull system was only marginally more effective than [ 23 ] or equally effective [ 24 ] as the corresponding push formulations in reducing outdoor transmission. Recently, a large semi-field study in Kenya and Tanzania identified and tested candidate spatial repellents, odour-baited traps and combined push–pull systems targeting outdoor transmission in the peridomestic area.…”

Point-of-Care Strategies Applied to Malaria Diagnosis

“…The analysis of 318 patients resulted in a high degree of sensitivity and specificity, supporting further testing in POC settings [78]. Recently, Mwanga and colleagues extended previous work [77] and provided the first demonstration that infrared spectroscopy could be coupled with supervised machine learning to accurately diagnose malaria in human dried blood spots [79]. Although more extensive field-testing must be performed, FTIR spectroscopy is indeed a promising method for point of care diagnostic of malaria.…”