The Diagnostic Performance of a Sysmex XN‐31 Automated Malaria Analyzer vs. Expert Microscopy

ABSTRACT

Introduction

Malaria is a common and life‐threatening infection. Malaria diagnosis needs to be fast and reliable. Although malaria microscopy is currently the gold standard, it is laborious, requires extensive training, and relies heavily on the proficiency of microscopists. Though malaria rapid tests are widely used, they show poor sensitivity at low parasitemia levels, are affected by gene deletions, and offer only qualitative results. There is a need to explore new techniques for the diagnosis of malaria.

Methodology

A single‐center, cross‐sectional study evaluated the diagnostic performance of the Sysmex XN‐31 automated analyzer for detecting malaria parasites compared to expert microscopy. The primary objective was to assess the XN‐31's sensitivity, specificity, and ability to quantify malaria parasites relative to microscopy, the current gold standard. Blood samples from 310 adult patients undergoing routine malaria testing in a hospital setting were used. This included 118 confirmed malaria‐positive cases. The Sysmex XN‐31 results were compared to blinded expert microscopy on the same samples. Dried blood spot samples were collected for any discrepancies and resolved using molecular testing.

Results

This study analyzed 310 patient samples for malaria using both microscopy and the XN‐31 analyzer. Microscopy identified 122 positive samples (39%), with P. falciparum being the most prevalent species. Expert malaria microscopy demonstrated a sensitivity of 97.6% and a specificity of 100%. The XN‐31 analyzer showed a sensitivity of 100% and a specificity of 99.46%. In malaria speciation, the XN‐31 correctly flagged P. falciparum in 116 out of 117 cases (99.1%) among 125 positive cases. Additionally, five nonfalciparum malaria cases ( Plasmodium malariae—four cases and Plasmodium ovale —one case) were accurately flagged as ‘Malaria (Others).’ However, five P. falciparum cases were incorrectly flagged as ‘Malaria (Others),’ highlighting limitations in malaria speciation by the analyzer. Statistical analysis revealed a strong correlation (Spearman coefficient of 0.8) between the parasite density measured via microscopy and the XN‐31. Passing–Bablok regression indicated a strong linear relationship between these two methods.

Conclusion

The Sysmex XN‐31 analyzer provides a quick and accurate method for the diagnosis of malaria. It detects, quantifies, and speciates plasmodium infections in less than 1 minute. Our study showed that the analyzer shows high sensitivity and specificity comparable to those of expert microscopy in detecting Plasmodium species, making it a promising alternative to current diagnostic methods. By overcoming the numerous limitations of existing tests, the XN‐31 proves to be well‐suited for malaria testing, especially in malaria‐endemic regions.

1. Introduction

1.1. Background of Malaria Diagnosis

Malaria is an identifiable, preventable, and treatable illness, yet it continues to pose a significant challenge to global health [1]. Recognizing the necessity for a united approach to address malaria, the World Health Organization (WHO) launched the Global Technical Strategy for Malaria 2016–2030, aims to reduce the global malaria burden by 90% [2]. Malaria has been one of the most serious infectious diseases affecting mankind for thousands of years, killing millions of people around the world. Approximately 3 billion people in over 97 countries remain at risk [2]. Malaria remains a major cause of morbidity and mortality in more than 85 tropical and subtropical countries around the world. Young children, pregnant women, and nonimmune visitors remain the most vulnerable to malaria and its complications. In Sub‐Saharan Africa, children account for 76% of all malaria deaths on the continent [3]. In their 2024 annual report, the World Health Organization (WHO) estimated that there were a total of 263 million malaria infections resulting in 597,000 deaths globally. The WHO African Region experiences a disproportionately high malaria burden, with 94% (246 million cases) of total malaria cases globally and 95% (569000) of fatalities occurring within this region in 2023 [3]. Four countries in the WHO African Region (Nigeria, The Democratic Republic of Congo, Uganda, and Mozambique) contribute to approximately 50% of the global malaria disease burden [4].

Malaria infection is caused by Plasmodium parasites in the genus Plasmodium and phylum Apicomplexa [5]. Malaria is transmitted through the bite of an infected Anopheles mosquito species. There are five species that affect humans in the genus Plasmodium, namely, P. falciparum, P. vivax , P. ovale , P. malariae, and P. knowlesi [6]. By far, P. falciparum has the highest mortality rate and is the most prevalent species on the African continent. P. vivax and P. ovale infections are less severe; however, the parasites can remain dormant in the liver and require different treatment regimens [6].

The diagnosis of malaria relies on the detection of parasites or their antigens in patient samples [7]. Since 2010, the current WHO guidelines recommend that all suspected cases of malaria undergo rapid diagnostic confirmation either through microscopy or the use of rapid malaria diagnostic tests (RDTs) before treatment is started [8]. A laboratory diagnosis of malaria leads to reduced presumptive antimalarial use, saves costs, reduces the risk of developing antimalarial resistance, and leads to better clinical outcomes [9].

The sensitivity and specificity of malaria diagnosis are affected by several factors, such as the detection of different malaria species, various parasite stages, varying levels of parasitemia ranging from 1 parasite per microlitre to tens of thousands per microlitre, and the sequestration of parasites in deeper vascular tissues, among others [10]. All these factors can influence the performance of various diagnostic modalities available today.

The two methods routinely used for parasitological confirmation include microscopy for direct detection of the parasite and immuno‐chromatographic RDTs for indirect detection via the presence of malarial antigens or proteins [11]. RDTs have high “false positive” rates in patients with prior infection, even in the absence of clinical signs, which may lead to unnecessary treatment. RDTs have a sensitivity of approximately 100 parasites per microlitre, which is lower than that of expert microscopists, although they are easy to run and interpret [12]. RDTs may also result in false negative test results due to gene deletions that affect target antigens [13]. There are now several reports, including from Kenya, of histidine‐rich protein 2 gene (HRP2) deletions enabling the parasite to avoid detection by HRP2‐based RDTs, thereby generating false negative test results [12, 14]. Light microscopy remains the current gold standard for malaria diagnosis. However, its accuracy depends heavily on the quality of the blood smear preparation and the skill of the laboratory personnel [9]. Microscopic examination is subjective, time‐consuming, and unsuitable for high‐throughput testing, particularly in busy clinical settings [15]. While polymerase chain reaction (PCR) methods offer high sensitivity and specificity for malaria diagnosis, their high cost per test and infrastructure requirements limit their application to a few reference centers. Therefore, there is a critical need for new technologies that can address the current limitations of malaria diagnosis.

1.2. Introduction to the Sysmex XN‐31 Analyser

Automation in malaria detection has the potential to increase the speed of detection, improve reliability, and provide high throughput, which may contribute to global public efforts against malaria. The Sysmex XN‐31 (Sysmex, Kobe, Japan) is an automated hematology analyzer that can detect and quantify malaria parasites. It has the potential to overcome some of the limitations of currently available test methods [16, 17]. XN‐31 enables rapid detection and quantification of malaria‐infected red blood cells (Mi‐RBCs) by using fluorescence flow cytometry for both Mi‐RBC and other hematological measurements such as hemoglobin levels, platelet counts, and white cell counts. The analyzer allows rapid detection and enumeration of malaria parasites within 1 min and is unimpacted by operator skill levels. The analyzer stains samples with a staining solution (Fluorocell M) along with a lysis solution (Lysercell M). Infected red blood cells (RBCs) are detected by a violet semiconductor laser beam at 405 nm. The percentage of parasitemia (%) is calculated as the ratio of infected to uninfected RBCs. The output data includes complete blood count (CBC) parameters, the presence of gametocytes, and parasitemia—both as a percentage of infected red blood cells (MI‐RBC%) and absolute parasite density (MI‐RBC#) expressed as parasites/μL [17]. The instrument also provides a CBC report that can aid in the assessment of anemia, thrombocytopenia, and other co‐infections as shown in Figure 1. The manufacturer‐reported limit of quantitation is 20 MI‐RBCs/μL. In this study, we sought to evaluate the diagnostic performance of the Sysmex XN‐31 analyzer compared with that of expert microscopy in a busy clinical setting.

FIGURE 1.

Figure 1 depicts a typical scattergram of a patient sample infected with Plasmodium falciparum marked with a red arrow. The accompanying complete blood count report details the percentage (MI‐RBC%) and absolute number (MI‐RBC#) of malaria‐infected red blood cells aids in patient evaluation and subsequent management.

2. Study Objectives

The objective of this study was to evaluate the diagnostic performance of the Sysmex XN‐31 analyzer for detecting malaria parasitemia compared to that of expert microscopy at Aga Khan Hospital, Kisumu. Kisumu is a city located within Kisumu County in the western part of Kenya, bordering Lake Victoria to the south, at coordinates Latitude: −0.1022⁰ and Longitude: 34.7617⁰. The county has a population of approximately 1.2 million people and covers a land area of approximately 2008 km². Kisumu is a malaria‐endemic area characterized by favorable climatic conditions for mosquito breeding and a high rate of ongoing year‐round transmission, estimated at 14.2% according to one study [18]. The objectives of this study were to assess the sensitivity and specificity of the Sysmex XN‐31 analyzer for malaria diagnosis, determine its ability to accurately identify the presence or absence of infection, and establish a correlation between malaria parasite quantification using both methods. The ability of XN‐31 to correctly speciate malaria was also evaluated.

3. Study Methodology

3.1. Study Design

This was a cross‐sectional study that compared the diagnostic accuracy of the Sysmex XN‐31 analyzer to that of expert microscopy, which is widely considered the ‘gold standard’.

3.2. Participant Recruitment and Sample Collection

The study utilized residual samples from routine malaria tests in a busy hospital environment. Samples, encompassing both known positives and negatives, were obtained from patients presenting with symptoms suggestive of malaria, the diagnosis of which was based on clinical evaluations made by the attending physicians. A purposive sampling technique was employed to guarantee the inclusion of both negative and positive samples, meeting the criteria for our required sample size. Subsequently, eligible patients were approached and asked to provide written informed consent. Samples without written informed consent were excluded. The study was carried out for a period of 6 months from October 2022 to March 2023.

3.3. Inclusion Criteria

• Clinical suspicion of malaria with a test request and a sample collected for malaria testing.

• Adequate leftover sample of at least 2 mL of venous EDTA blood.

• Providing written informed consent.

3.4. Exclusion Criteria

• Use of capillary samples for malaria diagnosis.

• Samples that were older than 24 h

• Any person younger than 18 years of age.

3.5. Study Procedures

The study was conducted in two stages. Initially, the analysis was performed using the XN‐31 for the eligible samples. Subsequently, microscopy was carried out by blinded malaria expert microscopists using the same set of samples. The expert microscopists were blinded to the results of the XN‐31. The microscopists were provided with total blood cell (WBC) counts to help in the estimation of parasitemia per microlitre.

3.6. Sysmex XN‐31 Analysis Procedure

Only laboratory personnel who had received training and were deemed competent were permitted to operate the XN‐31 analyzer. All standard quality control (QC) procedures were followed according to the manufacturer's instructions. All samples were processed within 24 h of collection. To avoid mixing errors, a sampler on the Sysmex XN‐31 analyzer was used to analyze all the samples. The Low Malaria (LM) mode (default sample measurement mode) was used for processing all the samples. In the case of a Mi‐RBC abnormal scattergram flag, the samples automatically triggered a reflex to the whole blood (WB) mode, which provided the highest probability of minimizing the number of indeterminate results. Several conditions that can trigger abnormal scattergram flags include basophilic inclusions in RBCs, Howell‐Jolly bodies, and abnormal RBC shapes in patients with sickle cell disease (SCD) or thalassemia.

If there were discrepancies between the XN‐31 analyzer and routine microscopy results, PCR utilizing primers for P. falciparum was used as the final reference. This assessment was performed on dried blood spot (DBS) samples for all discrepant cases. The study results did not impact patient care, as the XN‐31 results were not utilized for patient management. All the results were entered into an Excel spreadsheet in a password‐protected computer.

3.7. Expert Microscopy Procedure

All EDTA tubes, microscopy slides, and DBS samples were labeled with a unique sample identification number for identification and tracking purposes. The patients' names were not utilized for sample identification. Two thick and two thin blood smears were prepared at the laboratory using 9 μL of blood according to WHO research‐grade microscopy standards [19]. One pair of thick and thin smears was stained, and another pair was kept as a backup. The smears were stained with 3% Giemsa stain for 45 min and independently examined by two expert microscopists who had passed an external quality assurance program provided by the National Institute of Communicable Diseases (NCID), South Africa, and certified at the equivalent of WHO malaria microscopy competency level 1 or 2 for the presence and accuracy in the detection of malaria parasites, species identification, and density calculations. We used buffered water at a pH of 7.2 to dilute the stock Giemsa stain solution, ensuring uniform staining characteristics. Positive and negative control slides were prepared, stained with each batch, and reviewed before reporting on study cases. Two independent microscopists, blinded to each other's results, reviewed the stained slides. Parasite densities were calculated as the arithmetic mean of their two readings. A malaria smear was considered negative if no parasites were found in 200 high‐power microscopic fields. A third microscopist, blinded to the results of prior examinations, confirmed discordant results if the results from the two readers differed in terms of the presence of parasites, differences in parasite species between the two readers, or differences in parasite density between the two readers by ≥ 2 or 10 for high and low parasite densities, respectively.

The quantitation of positive malaria thick smears was carried out by counting the number of parasites for every 500 white blood cells and expressing the parasites per microliter based on the actual number of total white cells in that sample, as outlined here [20]. We used XN‐31 blood count reports that are produced alongside the malaria results to accurately quantify the total white cell count in the samples.

Estimation of parasites on the thick smears employed the following formula:

$$\begin{array}{c}\text{Number of parasites}/\text{microliter blood}=\left(\text{parasites}/\text{WBCs}\right)\hfill \\ \times \mathrm{WBC}\text{count}\mathrm{per}\text{microliter}\mathrm{as}\text{generated}\mathrm{by}\text{the}\mathrm{XN}\hfill \\ -31\text{analyzer}.\hfill \end{array}$$

Thin smear was evaluated for speciation purposes. All the results were entered and stored in an Excel spreadsheet in a password protected computer.

3.8. Resolution of Discrepant Results Through Polymerase Chain Reaction

For all cases, dried blood spot (DBS) samples were collected, labelled, and stored to assist in tie‐breaking for malaria PCR tests for any discrepant results between microscopy and the XN‐31 analyzer.

All patient samples had a dried blood spot sample prepared by adding 50 μL of EDTA WB onto Whatman Protein Saver 903 Snap‐Apart card papers and allowing it to dry at room temperature for 3 h in a biosafety cabinet. The sample impregnated cards were then stored individually in gas‐impermeable biohazard bags containing a desiccant packet. The DBS cards were kept frozen at −20° within 12 h after collection. The actual analysis was only carried out for samples that had discrepant results between XN‐31 and expert microscopists. DNA was extracted using a QIAamp blood kit (QIAGEN Inc., Chatsworth, CA) according to the manufacturer's instructions. Reactions were performed in a 20 μL total volume containing 2X TaqMan Universal mMix, 5 μM forward (18S‐QF‐GTA ATT GGA ATG ATA GGA ATT TAC AAG GT) and reverse primers (18S‐QR—TCA ACT ACG AAC GTT TTA ACT GCAAC) (for P. falciparum) and 1.5 μM probe (18S‐PROBE—FAM‐TGC CAG CAG CCG CGG TAA TTC‐BHQ1). The cycling conditions consisted of 1 cycle at 95°C for 10 min, 40 cycles at 95°C for 20 s, and 40 cycles at 58°C for 1 min.

3.9. Sample Size Calculation

The sample size for diagnostic performance was estimated to obtain the desired sensitivity and specificity from a baseline level of 80% (null hypothesis) and an alternative hypothesis of 90% to detect a 10% clinically significant effect size. The sample size is also aimed at estimating the true positives of the malaria cases tested. The true incidence of malaria is assumed to be approximately 40% [21], with a type 1 error rate of 4% [22]. Based on these assumptions, the minimum sample required will be 268 samples and 107 positive samples. The sample size was adjusted by 10% to accommodate invalid samples. The final sample size was 295 total and 118 positive samples. The sample size was calculated using PASS software (PASS 11. NCSS LLC. Kaysville, Utah, USA).

3.10. Data Analysis

Data analysis was performed by using Microsoft Excel 2016. Passing–Bablok regression analysis was used to determine the accuracy of the MI‐RBC produced by the XN‐31 compared to microscopy.

3.11. Ethical Issues

All participants provided their informed consent prior to inclusion in the study. This research was conducted in strict adherence to the principles of the Declaration of Helsinki. The study protocol received approval from the Maseno University Ethics Review Committee (MUERC) under the protocol number MSU/DRPI/MUERC/0986/21, and a research license was granted from the National Commission for Science, Technology, and Innovation (NACOSTI) under license number NACOSTI/P/22/20612.

4. Results

The study analyzed a total of 310 patient samples. Microscopy revealed that 122 samples (39%) were positive. P. falciparum was the predominant species, accounting for 117 cases or 96% of the positive cases. P. malariae and P. ovale followed, with 4 cases (3.2%) and 1 case (0.8%), respectively. Notably, no P. vivax cases were detected. There was a total of 3 false negatives detected by microscopists. The XN‐31 detected 125 positive samples (40%) and tested 184 negative cases. The XN‐31 achieved a sensitivity and specificity of 100% and 99.46%. The sensitivity and specificity of malaria microscopy were 97.6% and 100%, respectively. This is summarized in Figure 2.

FIGURE 2.

A table showing comparative analysis of diagnostic performance between thick smear microscopy and XN‐31 methods for malaria detection, showing respective sensitivity and specificity rates based on true positive and true negative counts.

The three malaria‐positive cases that were detected by the XN‐31 system but missed by expert microscopy were due to low parasitemia levels. The parasitemia levels were as follows: 0.0101% in Sample 1, 0.0069% in Sample 2, and 0.0008% in Sample 3. Microscopy, although widely used and the current gold standard, can miss cases with low levels of parasitemia, especially in asymptomatic patients or those in the early stages of infection. Theses findings highlight the challenges of relying solely on visual inspection, which is prone to human error and can fail in cases with scanty parasitemia.

There was one case of false‐positive malaria (two discrepant runs on the same sample), which was subsequently confirmed as negative by both PCR and expert microscopy. It must be noted that only primers for P. falciparum were utilized.

Malaria species information on the XN‐31 is presented as flags, classifying cases into P. falciparum, ‘Malaria (Others)’ representing nonfalciparum species, and ‘Malaria (UNC)’ for unclassifiable cases. The malaria (UNC) flag is triggered when the Mi‐RBCs are below 100 cells/μL or when the malaria‐infected RBC count is above this threshold, but sufficient information is unavailable in the ‘gating’ areas for the algorithm to determine the likely Plasmodium species. Out of 125 positive malaria cases identified by the XN‐31, P. falciparum was correctly flagged in 116 out of 117 cases (99.1%). The remaining non‐falciparum malaria cases (P. malariae—four cases and P. ovale —one case) were flagged as ‘Malaria (Others)’. Additionally, five P. falciparum cases were incorrectly flagged under ‘Malaria (Others)’. While the XN‐31 demonstrated strong performance as an automated system in detecting P. falciparum, it has limitations in differentiating non‐P. falciparum species, suggesting that it may not fully replace microscopy when accurate species differentiation is critical for treatment decisions.

Further statistical analysis revealed a strong positive correlation between parasite density quantified via microscopy and the XN‐31 analyzer, as indicated by a Spearman rank correlation coefficient of 0.8 (95% CI; 0.72–0.85) as shown in Figure 3. Passing–Bablok regression revealed a strong linear relationship between the measurements obtained from the two methods, yielding a slope of 1.05 (95% CI; 0.97–1.18). With an intercept of 185.4 (95% CI; −443.7 to 834.3), there seems to be a slight systematic difference between the two methods of measurement. However, given the broad confidence interval that includes zero, this difference was not statistically significant. In conclusion, these results suggest that there is a strong correlation and good agreement between the two methods of measurement under evaluation. The XN‐31 analyzer is a reliable alternative to microscopy for the diagnosis, quantification, and speciation of malaria parasites.

FIGURE 3.

Passing‐Bablok regression analysis revealed good agreement between the two methods for measuring malaria parasite density.

5. Discussion

Malaria is a preventable, detectable, and treatable disease, yet it remains a significant public health problem, with more than 50% of the global population at risk across more than 106 countries [23]. Accurate diagnosis of malaria remains a challenge. The speed, accuracy, simplicity, and affordability of malaria diagnosis are fundamental to any effective diagnostic method for infectious diseases [7]. The gold standard for malaria diagnosis remains microscopic examination of blood smears [7, 19]. Since 2010, the World Health Organization (WHO) has advocated for a universal parasite‐based diagnostic approach for all patients with suspected malaria before treatment is administered. The current diagnostic technologies have several limitations. Microscopy requires well‐trained and proficient workers who are often absent in remote, malaria‐prone areas. Resource limitations are also a major challenge, with many facilities lacking functional microscopes, reliable power sources, or quality reagents needed for accurate and reliable diagnosis [24]. Overburdened health workers may not devote the necessary time and attention to each test due to high workloads, potentially compromising the quality of the results. Furthermore, microscopic examination of blood smears is time‐consuming, posing problems in areas with high caseloads. Despite the widespread use of malaria microscopy, there is an urgent need for improved parasite‐based diagnostic methods. Since the introduction of rapid diagnostic tests (RDTs) in the 1990s, they have been widely adopted. Despite being less expensive and quicker, RDTs can yield false‐positive and false‐negative results, exhibit low sensitivity at low parasite densities, and have incorrect interpretation of results [24].

Malaria misdiagnosis, whether false‐positive or false‐negative, poses serious challenges to malaria control. False‐negative cases can lead to the unnecessary use of antimicrobials, extra consultations, or even progression to severe malaria. Conversely, false‐positive results lead to the unnecessary use of antimalarial drugs, causing patients to suffer potential side effects and increasing the risk of antimalarial drug resistance [10]. In our study, XN‐31 detected three additional positive malaria cases that were missed by expert microscopy. These discrepancies between expert microscopy and XN‐31 results underscore the enhanced sensitivity of XN‐31 in detecting low‐level parasitemia that traditional microscopy may miss. Negative microscopy results may occur due to very low parasite loads, particularly in early‐stage infections or partially treated cases, where parasites are present at concentrations below the detection threshold of manual methods. XN‐31's ability to identify these low parasitemia levels is further corroborated by hematological abnormalities, such as anemia and thrombocytopenia, which are commonly associated with malaria. In our study, 2 out of 3 of the patients had mild thrombocytopenia.

In sub‐Saharan Africa, significant challenges persist in malaria diagnostics. Following the WHO's recommendation that all suspected malaria cases undergo parasitological testing [25], there has been a rapid adoption of malaria tests, especially RDTs. Although malaria testing rates have increased from 36% in 2005 to 65% in 2014, substantial gaps in diagnostics remain, highlighting the unmet needs in this area [23]. Although the uptake of RDTs continues to increase in Africa, it is threatened by the increasing prevalence of P. falciparum HRP2/3 gene deletions that pose a significant threat to the effectiveness of RDTs in the fight against malaria [26, 27, 28]. There is an urgent need for additional diagnostic modalities to support the global public fight against malaria.

The Sysmex XN‐31 is a new innovative approach for malaria diagnosis, since RDTs for malaria were introduced in the 1990s. Unlike microscopy, which is time‐consuming and requires considerable expertise, the automated XN‐31 provides rapid, automated malaria detection with better sensitivity (20 parasites per microlitre) than expert microscopy, as claimed by the manufacturer. In addition to malaria detection, the system will provide a CBC with all RBC indices to aid in the diagnosis of anemias and possible thrombocytopenia that urgently need to be addressed.

This study assessed the diagnostic performance of the XN‐31 analyzer compared to expert microscopy using clinical samples from patients suspected of having malaria. The XN‐31 achieved a sensitivity of 100% and a specificity of 97.6% in parasite detection. The analyzer also correctly identified Plasmodium falciparum in 116 out of 117 cases (99.1%). Nonfalciparum malaria cases (P. malariae—four cases and P. ovale —one case) were flagged as ‘Malaria (Others)’ since the analyzer cannot differentiate the non‐falciparum species. However, five P. falciparum cases were incorrectly categorized as ‘Malaria (Others)’, underscoring the current limitations in malaria speciation by the analyzer. This study showed that the XN‐31 analyzer is non‐inferior to expert microscopy for the diagnosis, speciation, and quantification of malaria parasites.

Previous studies using the XN‐31 for the diagnosis of malaria in various settings have shown similar findings. In one study conducted in an endemic region of Colombia, XN‐31 demonstrated a sensitivity of 90% in detecting Plasmodium species, which was equivalent to that of microscopy and RDTs [29]. Another study in Malawi evaluating XN‐31 for blood donor malaria screening reported a high sensitivity of 100% in detecting malaria parasites [30]. These findings were replicated in our study. A recent study that used the XN‐31 prototype in rural Kenya showed that the equipment could detect Plasmodium‐infected erythrocytes, differentiate species, and provide parasite counts in approximately 1 min without preparation in a hub‐and‐spoke setting. The study showed the feasibility of using capillary samples, showing equivalence in results in addition to showing short‐term refrigerator stability of testing samples for up to 24 h without affecting patient results [31]. Rural laboratories in employing the hub‐spoke model. Considering the diagnostic performance of the XN‐31 in our study, we hypothesize that the instrument may support multiple roles in malaria control programs, ranging from case management to monitoring treatment efficacy and clinical research/therapeutic efficacy programs, bringing us closer to malaria eradication. The XN‐31 analyzer overcomes the limitations of the currently available malaria diagnostic tests. Microscopy requires trained personnel, is time‐consuming, and has operator‐dependent limits of detection. RDTs have limitations in quantifying parasitemia, and poor sensitivity is associated with low parasite counts and the risk of loss of sensitivity due to parasite antigen genetic alterations [12, 14, 32]. XN‐31 offers a reliable alternative with improved sensitivity, high throughput, and the ability to detect all Plasmodium species. However, deploying the Sysmex XN‐31 analyzer in rural settings may face significant logistical challenges. These challenges include the requirement for a stable and reliable electricity supply, which is often absent or inconsistent in these areas. Additionally, the upfront cost of procurement and setup, combined with the ongoing need for proprietary reagents and specific storage conditions, creates a significant financial burden. These factors could potentially limit the adoption of this technology in low‐resource regions. Addressing these challenges is crucial to ensure the broader deployment of the Sysmex XN‐31 analyzer and its contribution to global malaria control and eradication efforts.

Rare cases of false positives are possible in patients with red cell abnormalities. In this study, the single false positive, where XN‐31 yielded indeterminate results, was due to red blood changes due to sickle cell disease (as identified on further examination of the slide). The presence of abnormal red cell changes, such as crystallized sickle hemoglobin or Howell‐Jolly bodies, could lead to abnormal scattergrams and increase the likelihood of indeterminate results [33]. In such cases, morphological evaluation or the use of a secondary confirmatory method may be needed. Careful evaluation of red cell scattergrams may also provide more information since malaria‐positive and malaria‐negative cases will have different features on their respective scattergrams as shown in Figure 4. Although the XN‐31 shows excellent speciation of P. falciparum, further improvements in XN‐31 technology or alternative detection methods such as molecular techniques and microscopy are required to correctly differentiate various malaria species.

FIGURE 4.

A careful evaluation of equipment scattergrams can provide additional information, as malaria‐positive and malaria‐negative cases exhibit distinct features in their respective scattergrams. In this diagram, A represents a negative sample, B shows a sample with P. falciparum infection, C indicates a sample with a mixed infection of P. falciparum and other species, and D illustrates a malaria infection with gametocytes.

In conclusion, the Sysmex XN‐31 analyzer provides a quick and accurate method for the diagnosis of malaria. It not only detects Plasmodium infections but also quantifies parasitemia and performs a complete blood cell count, all within a minute. The analyzer was also able to correctly speciate P. falciparum in most cases that were reviewed, though the other non‐falciparum species are not differentiated. Our study showed that XN‐31 exhibits high sensitivity and specificity comparable to those of expert microscopy in detecting Plasmodium species, making it a promising alternative to current diagnostic methods. By overcoming the numerous limitations of existing tests, the XN‐31 proves to be suited for malaria testing, especially in malaria‐endemic regions.

6. Study Limitations

This study has some limitations that affect the generalizability of its findings. Firstly, the study only included participants above 18 years old, limiting its applicability to younger populations. Secondly, the research was conducted in a single center in a malaria‐endemic region. While this ensured a well‐defined study environment, it raises concerns about how well the results translate to real‐world clinical settings with more diverse conditions, variable disease epidemiology, and different sample types. Finally, a lack of follow‐up testing means assessment of parasite clearance could not be evaluated.

Author Contributions

S.O. played a role in designing the study, overseeing its execution, analyzing the data, and writing the manuscript. K.O. and L.M. were responsible for collecting and analyzing the data. E.M. coordinated the study and supported data collection. G.K., G.M., and V.N. offered valuable supervision during the research process. A.T.H. collaborated on the study design and data analysis. T.O. and S.K. provided technical support, study oversight and reviewed the final manuscript. All authors have reviewed and approved the final version of this manuscript.

Ethics Statement

This research was conducted in strict adherence to the principles of the Declaration of Helsinki. The study protocol received approval from the Maseno University Ethics Review Committee (MUERC) under the protocol number MSU/DRPI/MUERC/0986/21, and a research license was granted from the National Commission for Science, Technology, and Innovation (NACOSTI) under license number NACOSTI/P/22/20612.

Consent

All participants provided their informed consent before to inclusion in the study.

Conflicts of Interest

This study was partially supported by a reagent grant from Sysmex South Africa (PTY) Limited.

Onsongo S., Otieno K., Mathenge L., et al., “The Diagnostic Performance of a Sysmex XN‐31 Automated Malaria Analyzer vs. Expert Microscopy,” International Journal of Laboratory Hematology 47, no. 4 (2025): 613–621, 10.1111/ijlh.14456.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.