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

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.

Point-of-Care Strategies Applied to Malaria Diagnosis

“…Heraud and colleagues evaluated this technique using portable infrared spectrometers at four regional clinics in Thailand, comparing against laboratorybased qPCR. 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].…”

The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence

“…Mwanga and colleagues identified P. falciparum with FTIR from field samples collected in Tanzania where an accuracy of 92%, sensitivity of 92.8%, and specificity of 91.7% in comparison to PCR findings was achieved [70]. The second study was done with P. falciparum samples collected in Thailand where a sensitivity of 92% and a specificity of 97% in comparison to PCR was observed [71]. A summary of the studies that applied MIR for diagnosis of malaria are indicated in Table 4, and an example of a MIR spectra for malaria infected and uninfected red blood cells is shown in Fig 2.…”

Recent evolutions of machine learning applications in clinical laboratory medicine

“…A simple automatic sensing method using digital in-line holographic microscopy combined with SVM has been proposed to identify unstained malariainfected RBCs with an accuracy of 97.5% [76]. Heraud et al [77] demonstrated that infrared (IR) analysis of packed RBCs using a portable spectrometer and a cloud-based SVM system could have the potential to become an accurate POC tool for the diagnosis of malaria in endemic countries. Furthermore, another study [78] also employed IR spectroscopy in combination with supervised ML to screen for malaria parasites in human dried blood spots.…”

“…While MALDI-TOF MS is commonly used for microbial identification in the routine clinical microbiology lab, vibrational spectroscopy (i.e. IR and Raman spectroscopy) is gaining considerable interest as an alternative technique in the classification of clinically relevant microorganisms due to its rapid, objective, nondestructive, and cost-effective nature [6,[76][77][78][79][80][81]. Although traditional ML techniques have been widely applied for spectral data analysis [8,[98][99][100][101][102], fewer efforts have been made in developing neural networks and deep learning algorithms [103][104][105].…”