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

التفاصيل البيبلوغرافية
العنوان: Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis
المؤلفون: Emmanuel P. Mwanga, Elihaika G. Minja, Emmanuel Mrimi, Mario González Jiménez, Johnson K. Swai, Said Abbasi, Halfan S. Ngowo, Doreen J. Siria, Salum Mapua, Caleb Stica, Marta F. Maia, Ally Olotu, Maggy T. Sikulu-Lord, Francesco Baldini, Heather M. Ferguson, Klaas Wynne, Prashanth Selvaraj, Simon A. Babayan, Fredros O. Okumu
المصدر: Malaria Journal, Vol 18, Iss 1, Pp 1-13 (2019)
بيانات النشر: BMC, 2019.
سنة النشر: 2019
المجموعة: LCC:Arctic medicine. Tropical medicine
LCC:Infectious and parasitic diseases
مصطلحات موضوعية: Malaria diagnosis, Plasmodium, Ifakara Health Institute, Mid-infrared spectroscopy, Dried blood spots, Supervised machine learning, Arctic medicine. Tropical medicine, RC955-962, Infectious and parasitic diseases, RC109-216
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1475-2875
Relation: http://link.springer.com/article/10.1186/s12936-019-2982-9; https://doaj.org/toc/1475-2875
DOI: 10.1186/s12936-019-2982-9
URL الوصول: https://doaj.org/article/5261c2178fc34ab48ba6aa459876859d
رقم الأكسشن: edsdoj.5261c2178fc34ab48ba6aa459876859d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:14752875
DOI:10.1186/s12936-019-2982-9