Automated malaria detection
Automated digital microscopy and cell classification with CellaVision (Lund, Sweden) DM96 Advanced RBC Application in the fully automated mode, showed high specificity; however, this has been reported to give a sensitivity of only 23% [
4]. The same image classification software is used in the Sysmex (Kobe, Japan) DI-60, where sensitivity has been found to be similar to microscopy, but only after manual human reclassification [
5]. False negative results are still seen, even for 6.3% parasitaemia, possibly because these systems are very sensitive to smear quality and staining [
6]. Furthermore, these systems provide a general parasite class without
Plasmodium species identification, again unable to circumvent the need for skilled morphologists.
Haematology Analyzers reporting CBC/DIFF have also been used for malarial screening. Parameters frequently used for screening, such as platelet and WBC counts, may also be affected in diseases other than malaria and hence provide poor sensitivity and specificity. These reported malarial algorithms still involve some mathematical calculation and require interpretative skills to assess laboratory results.
During intra-erythrocytic stages, a malarial parasite in the RBC digests haemoglobin and releases free haem, which is toxic to cells, further converting it into a bi-refringent insoluble crystalline form called haemozoin [
7]. Abbott (Santa Clara, USA) designed the Cell-Dyn series with a depolarized diffracted light (DLL) detector, therefore malaria cases could be flagged by cells identified as large mononuclear cells with a high depolarized signal due to haemozoin [
8,
10]. This detection method lacks sensitivity for early infection, as significant haemozoin production occurs only with late stages or mature Plasmodium parasite forms. Regarding specificity, false positives have been reported in cases of other parasites, such as filaria [
9], or children treated with sulfonamide, as its derivatives form yellow granules that give a similar depolarized signal [
10]. Varying performance has been reported among studies [
10] and among instruments, depending on laser wavelengths. Detection of haemozoin has also been proposed using a magneto-optical device [
11].
In infected red blood cells (iRBCs), the cytoskeleton is remodelled by the
Plasmodium [
12‐
14], accompanied by changes in iRBC membrane properties. Although not yet clearly understood, these changes can result in an increased resistance to lysis for iRBCs, especially those harbouring mature forms of the parasite (late amoeboid, schizonts, gametocytes). These incompletely lysed iRBC may then produce a spurious signal in WBC channels, as a peak on the left of WBC volume histogram or as a separate cluster [
15]. Since this process is lysis-dependent, balance alarms are often triggered by differences between WBC counting channels using different lysis methods.
Detecting iRBCs has been used for malaria detection with Sysmex XE [
16‐
18], XN series [
19] and Mindray (Shenzhen, China) BC-6800 [
20]. This technique lacks sensitivity for low levels of parasitaemia and in early infection, as only mature forms are likely to have remodeled the cytoskeleton sufficiently to increase lysis resistance. Furthermore, while
Plasmodium vivax remodelling results in a more flexible (deformable) membrane of the iRBC in order to avoid splenic entrapment, the presence of
Plasmodium falciparum conversely affects iRBC by increasing membrane rigidity [
21]. The
P. falciparum survival strategy is to increase iRBC membrane adherence and sequestration in venules and capillaries [
22]. This may explain the scarcity of finding mature forms of
P. falciparum in venous blood, thus resulting in less sensitivity for those Analyzers. Recently, Sysmex has released the high range XN-30 Analyzer, embedding a 405 nm violet laser with scattering and fluorescence measurements, that has a dedicated module (for additional cost) for
Plasmodium detection and counting with a partial lysis reagent allowing parasites to remain inside iRBCs and nucleic acid staining for labelling parasites DNA, which claims an improved detection limit of 20 parasites/µL [
23]. This device has been evaluated for malaria detection, reporting a ROC with an AUC = 0.98 and achieving 98.7% sensitivity and 96.5% specificity when compared with microscopy reference [
24,
25].
Another technique was developed using various discriminant factors based on statistical variables of cell population data (CPD) for high range Beckman-Coulter instruments using VCS technology to identify malarial infection. Standard deviations of the volume and conductivity of lymphocytes has been used along with PLT count [
26], or the standard deviation of the volume of lymphocytes and monocytes [
27]. Using CPD on the Sysmex XN, Buoro et al
. found 93% sensitivity, but employing both techniques, an AUC of 0.96 was achieved with sensitivity 100% and specificity 91%, albeit with a limited study of only 14 positive cases [
19].
All of these techniques developed to date have been on high range instruments using laser-technology that are generally not in routine use in endemic regions of developing countries where field laboratories typically have low or middle range instruments, thus leaving a persistent need for affordable screening tools. In a previous work [
28], the complete data set was generated using two Haematology Analyzers (ABX Pentra XL80, 5-Diff, HORIBA Medical, Montpellier, France and Microsemi CRP, 3-Diff + CRP, HORIBA Medical, Kyoto, Japan) and explored automated SVM classifiers for malaria with data-mining techniques customized for each instrument. The flags generated by these classifiers were evaluated in a prospective study and showed similar performance to claims for malarial flags or algorithms on high range instruments. For the Micros-EMI-CRP, the inclusion of CRP in the flagging algorithm provided valuable information for malaria screening, increasing sensitivity beyond only the CBC data. Unfortunately, the addition of a CRP value to a malarial flagging algorithm does not allow for a cost-effective malarial screening tool.
Dengue fever
Dengue virus is an arbovirus transmitted by
Aedes mosquitoes and exists as four serotypes, DENV-1 to 4 [
29]. Dengue infection can range from simple fever to more severe cases with lethal bleeding tendencies due to thrombocytopenia and in the most serious cases with plasma leakage, referred as severe dengue fever. As opposed to malaria where haemozoin or lysis-resistant iRBCs can be observed, there is no obvious specific cellular signal indicative of dengue virus infection in standard Haematology Analyzers. Only the non-specific host immunologic responses common to many viral infections can be observed.
Some effects on haematology parameters, such as lymphocytes, neutrophils and monocytes, have been reported in dengue infections [
30,
31], which correlate with the number of days of illness and thus are not so effective for screening in early stages of the disease [
32].
Some previous studies have defined certain discriminant factors for dengue screening, such as the one developed using CPD on Beckman-Coulter VCS instruments. A review of literature in relation to discriminating dengue fever from other febrile illness can be found in the references [
30]. Algorithms distinguishing malaria and dengue fever from other febrile conditions have been designed using the PLT count [
26] or via the percentage of lymphocytes and standard deviation of the conductivity of lymphocytes on the LH750, reporting an AUC of 0.893, also evaluated with an AUC of 0.931 in reference [
15]. Soto et al. reported AUC of 0.76 for dengue factor built from the percentage of monocytes and the standard deviation in the volume of monocytes [
33]. The lymph-index, defined as lymphocyte mean volume x SD of lymphocyte volume/lymphocyte mean conductivity, has been designed as a factor for viral infection for the LH750, reporting a potential sensitivity of 71% and specificity of 78% for dengue fever detection [
34,
35]. Dengue factor and lymph-index factor have also been evaluated with 2 new factors: Monocyte Factor from Mono% and SD of Monocyte volume, and a new dengue factor mixing monocyte factor, lymph-index and platelet (PLT) count [
36], but none of these achieved a ROC AUC greater than 0.70. A decision tree designed with the same variables with the LH780 has been reported to achieve a sensitivity of 94% and specificity of 77% for suspected samples [
37]. Two decision trees (one of these only using Haematology Analyzer parameters) for classifying severity of the disease have been proposed [
38].