Improvement of malaria diagnostic system based on acridine orange staining

Application of image recognition technology in pathological diagnosis of blood smears

Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.

The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas

Even if malaria is rare in Europe, it is a medical emergency and programs for its control should ensure both an early diagnosis and a prompt treatment within 24-48 h from the onset of the symptoms. The increasing number of imported malaria cases as well as the risk of the reintroduction of autochthonous cases encouraged laboratories in non-endemic countries to adopt diagnostic methods/algorithms. Microscopy remains the gold standard, but with limitations. Rapid diagnostic tests have greatly expanded the ability to diagnose malaria for rapid results due to simplicity and low cost, but they lack sensitivity and specificity. PCR-based assays provide more relevant information but need well-trained technicians. As reported in the World Health Organization Global Technical Strategy for Malaria 2016-2030, the development of point-of-care testing is important for the improvement of diagnosis with beneficial consequences for prompt/accurate treatment and for preventing the spread of the disease. Despite their limitations, diagnostic methods contribute to the decline of malaria mortality. Recently, evidence suggested that artificial intelligence could be utilized for assisting pathologists in malaria diagnosis.

Malaria therapeutics: are we close enough?

Malaria is a vector-borne parasitic disease caused by the apicomplexan protozoan parasite Plasmodium. Malaria is a significant health problem and the leading cause of socioeconomic losses in developing countries. WHO approved several antimalarials in the last 2 decades, but the growing resistance against the available drugs has worsened the scenario. Drug resistance and diversity among Plasmodium strains hinder the path of eradicating malaria leading to the use of new technologies and strategies to develop effective vaccines and drugs. A timely and accurate diagnosis is crucial for any disease, including malaria. The available diagnostic methods for malaria include microscopy, RDT, PCR, and non-invasive diagnosis. Recently, there have been several developments in detecting malaria, with improvements leading to achieving an accurate, quick, cost-effective, and non-invasive diagnostic tool for malaria. Several vaccine candidates with new methods and antigens are under investigation and moving forward to be considered for clinical trials. This article concisely reviews basic malaria biology, the parasite's life cycle, approved drugs, vaccine candidates, and available diagnostic approaches. It emphasizes new avenues of therapeutics for malaria.

Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future

Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. According to the World Health Organisation (WHO), millions of people are infected annually, leading to inevitable deaths in the infected population. Statistical records show that early detection of malaria parasites could prevent deaths and machine learning (ML) has proved helpful in the early detection of malarial parasites. Human error is identified to be a major cause of inaccurate diagnostics in the traditional microscopy malaria diagnosis method. Therefore, the method would be more reliable if human expert dependency is restricted or entirely removed, and thus, the motivation of this paper. This study presents a systematic review to understand the prevalent machine learning algorithms applied to a low-cost, portable optical microscope in the automation of blood film interpretation for malaria parasite detection. Peer-reviewed papers were downloaded from selected reputable databases eg. Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, etc. The extant literature suggests that convolutional neural network (CNN) and its variants (deep learning) account for 41.9% of the microscopy malaria diagnosis using machine learning with a prediction accuracy of 99.23%. Thus, the findings suggest that early detection of the malaria parasite has improved through the application of CNN and other ML algorithms on microscopic malaria parasite detection.

Malaria distribution and performance of malaria diagnostic methods in Malaysia (1980-2019): a systematic review

BACKGROUND

Malaysia has already achieved remarkable accomplishments in reaching zero indigenous human malaria cases in 2018. Prompt malaria diagnosis, surveillance and treatment played a key role in the country's elimination success. Looking at the dynamics of malaria distribution during the last decades might provide important information regarding the potential challenges of such an elimination strategy. This study was performed to gather all data available in term of prevalence or incidence on Plasmodium infections in Malaysia over the last four decades.

METHODS

A systematic review of the published English literature was conducted to identify malaria distribution from 1980 to June 2019 in Malaysia. Two investigators independently extracted data from PubMed, Scopus, Web of Science and Elsevier databases for original papers.

RESULTS

The review identified 46 epidemiological studies in Malaysia over the 39-year study period, on which sufficient information was available. The majority of studies were conducted in Malaysia Borneo (31/46; 67.4%), followed by Peninsular Malaysia (13/46; 28.3%) and in both areas (2/46; 4.3%). More than half of all studies (28/46; 60.9%) were assessed by both microscopy and PCR. Furthermore, there was a clear trend of decreases of all human malaria species with increasing Plasmodium knowlesi incidence rate throughout the year of sampling period. The summary estimates of sensitivity were higher for P. knowlesi than other Plasmodium species for both microscopy and PCR. Nevertheless, the specificities of summary estimates were similar for microscopy (40-43%), but varied for PCR (2-34%).

CONCLUSIONS

This study outlined the epidemiological changes in Plasmodium species distribution in Malaysia. Malaria cases shifted from predominantly caused by human malaria parasites to simian malaria parasites, which accounted for the majority of indigenous cases particularly in Malaysia Borneo. Therefore, malaria case notification and prompt malaria diagnosis in regions where health services are limited in Malaysia should be strengthened and reinforced to achieving the final goal of malaria elimination in the country.

Quantitative Detection of Plasmodium falciparum Using, LUNA-FL, A Fluorescent Cell Counter

The microscopic examination of Giemsa-stained thin and/or thick blood films (Giemsa microscopy) is the standard method of malaria diagnosis. However, the results of the diagnosis significantly depend on the skills of clinical technicians. Furthermore, sample preparation and analysis are laborious and time-consuming. Therefore, in this study, we investigated if a commercially available fluorescent cell counter, LUNA-FL, was useful for the detection of Plasmodium parasite and the estimation of parasitemia. Whole blood samples from uninfected persons, spiked with P. falciparum-infected erythrocytes, were analysed. Most of the leucocytes and platelets were removed from whole blood samples with SiO₂-nanofiber filters set on spin columns. The filtered samples were stained with acridine orange, and automatic detection, as well as counting of erythrocytes and parasites, were performed using LUNA-FL. Whole blood, with various levels of parasites, was analysed by Giemsa microscopy or with LUNA-FL to estimate parasitemia, and a comparative analysis was performed. The coefficient determination value of the regression line was high (R² = 0.98), indicating that accurate quantitative parasite detection could be performed using LUNA-FL. LUNA-FL has a low running cost; it is compact, fast, and easy to operate, and may therefore be useful for point-of-care testing in the endemic areas.

Diagnosis of clinical malaria in endemic settings

INTRODUCTION

Malaria continues to be a major global health problem, with over 228 million cases and 405,000 deaths estimated to occur annually. Rapid and accurate diagnosis of malaria is essential to decrease the burden and impact of this disease, particularly in children. We aimed to review the main available techniques for the diagnosis of clinical malaria in endemic settings and explore possible future options to improve its rapid recognition.

AREAS COVERED

literature relevant to malaria diagnosis was identified through electronic searches in Pubmed, with no language or date restrictions and limited to humans.

EXPERT OPINION

Light microscopy is still considered the gold standard method for malaria diagnosis and continues to be at the frontline of malaria diagnosis. However, technologies as rapid diagnostic tests, mainly those who detect histidine-rich protein-2, offer an accurate, rapid and affordable alternative for malaria diagnosis in endemic areas. They are now the technique most extended in endemic areas for parasitological confirmation. In these settings, PCR-based assays are usually restricted to research and they are not currently helpful in the management of clinical malaria. Other technologies, such as isothermal methods could be an interesting and alternative approach to PCR in the future.

Diagnosis of Malaria Parasites Plasmodium spp. in Endemic Areas: Current Strategies for an Ancient Disease

Fast and effective detection of the causative agent of malaria in humans, protozoan Plasmodium parasites, is of crucial importance for increasing the effectiveness of treatment and to control a devastating disease that affects millions of people living in endemic areas. The microscopic examination of Giemsa-stained blood films still remains the gold-standard in Plasmodium detection today. However, there is a high demand for alternative diagnostic methods that are simple, fast, highly sensitive, ideally do not rely on blood-drawing and can potentially be conducted by the patients themselves. Here, the history of Plasmodium detection is discussed, and advantages and disadvantages of diagnostic methods that are currently being applied are assessed.