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Malar J. 2019 Oct 16;18(1):348. doi: 10.1186/s12936-019-2945-1.

Infrared spectroscopy coupled to cloud-based data management as a tool to diagnose malaria: a pilot study in a malaria-endemic country.

Author information

1
Centre for Biospectroscopy, School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, VIC, 3800, Australia.
2
Department of Microbiology and the Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Wellington Road, Clayton, VIC, 3800, Australia.
3
Centre for Research and Development of Medical Diagnostic Laboratories, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand.
4
Graduate School, Khon Kaen University, Khon Kaen, Thailand.
5
Centre for Biospectroscopy, School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, VIC, 3800, Australia. david.perez.guaita@monash.edu.
6
Centre for Biospectroscopy, School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, VIC, 3800, Australia. bayden.wood@monash.edu.

Abstract

BACKGROUND:

Widespread elimination of malaria requires an ultra-sensitive detection method that can detect low parasitaemia levels seen in asymptomatic carriers who act as reservoirs for further transmission of the disease, but is inexpensive and easy to deploy in the field in low income settings. It was hypothesized that a new method of malaria detection based on infrared spectroscopy, shown in the laboratory to have similar sensitivity to PCR based detection, could prove effective in detecting malaria in a field setting using cheap portable units with data management systems allowing them to be used by users inexpert in spectroscopy. This study was designed to determine whether the methodology developed in the laboratory could be translated to the field to diagnose the presence of Plasmodium in the blood of patients presenting at hospital with symptoms of malaria, as a precursor to trials testing the sensitivity of to detect asymptomatic carriers.

METHODS:

The field study tested 318 patients presenting with suspected malaria at four regional clinics in Thailand. Two portable infrared spectrometers were employed, operated from a laptop computer or a mobile telephone with in-built software that guided the user through the simple measurement steps. Diagnostic modelling and validation testing using linear and machine learning approaches was performed against the gold standard qPCR. Sample spectra from 318 patients were used for building calibration models (112 positive and 110 negative samples according to PCR testing) and independent validation testing (39 positive and 57 negatives samples by PCR).

RESULTS:

The machine learning classification (support vector machines; SVM) performed with 92% sensitivity (3 false negatives) and 97% specificity (2 false positives). The Area Under the Receiver Operation Curve (AUROC) for the SVM classification was 0.98. These results may be better than as stated as one of the spectroscopy false positives was infected by a Plasmodium species other than Plasmodium falciparum or Plasmodium vivax, not detected by the PCR primers employed.

CONCLUSIONS:

In conclusion, it was demonstrated that ATR-FTIR spectroscopy could be used as an efficient and reliable malaria diagnostic tool and has the potential to be developed for use at point of care under tropical field conditions with spectra able to be analysed via a Cloud-based system, and the diagnostic results returned to the user's mobile telephone or computer. The combination of accessibility to mass screening, high sensitivity and selectivity, low logistics requirements and portability, makes this new approach a potentially outstanding tool in the context of malaria elimination programmes. The next step in the experimental programme now underway is to reduce the sample requirements to fingerprick volumes.

KEYWORDS:

Cloud based diagnostics; Infrared spectroscopy; Malaria diagnosis; Plasmodium

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