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

Autor: Emmanuel Mrimi, Doreen J. Siria, Salum A. Mapua, Marta F. Maia, Elihaika G. Minja, Heather M. Ferguson, Halfan S. Ngowo, Simon A. Babayan, Klaas Wynne, Francesco Baldini, Ally Olotu, Prashanth Selvaraj, Said Abbasi, Caleb Stica, Maggy T. Sikulu-Lord, Emmanuel P. Mwanga, Fredros O. Okumu, Johnson K. Swai, Mario González Jiménez
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Plasmodium
lcsh:Arctic medicine. Tropical medicine
Spectrophotometry
Infrared
lcsh:RC955-962
030231 tropical medicine
Plasmodium falciparum
Biology
Logistic regression
Tanzania
lcsh:Infectious and parasitic diseases
03 medical and health sciences
0302 clinical medicine
Dried blood spots
Attenuated total reflection-Fourier Transform Infrared spectrometer
parasitic diseases
medicine
Humans
lcsh:RC109-216
Malaria
Falciparum
Bootstrapping (statistics)
Mid-infrared spectroscopy
030304 developmental biology
Dried Blood Spot Testing
0303 health sciences
Rapid diagnostic test
Human blood
Spots
Malaria diagnosis
business.industry
Research
Pattern recognition
medicine.disease
Plasmodium ovale
biology.organism_classification
3. Good health
Statistical classification
Infectious Diseases
PCR
Logistic Models
Parasitology
Ifakara Health Institute
Artificial intelligence
Supervised Machine Learning
business
Malaria
Zdroj: Malaria Journal
Malaria Journal, Vol 18, Iss 1, Pp 1-13 (2019)
ISSN: 1475-2875
Popis: 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.
Databáze: OpenAIRE
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