Lack of knowledge regarding the microscopic diagnosis of malaria by technicians of the laboratory network in Luanda, Angola

Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set

BACKGROUND

Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated.

METHODS

The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set.

RESULTS

The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set.

CONCLUSIONS

EasyScan GO's expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

Convolutional neural networks to automate the screening of malaria in low-resource countries

Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.

Challenges for the diagnosis and treatment of malaria in low transmission settings in San Lorenzo, Esmeraldas, Ecuador

BACKGROUND

Ecuador is on the verge of eliminating malaria according to the World Health Organization criteria. Nevertheless, active transmission foci still persist in the country, and these represent an important challenge for achieving the objectives set out. Diagnosis and treatment are a mainstay in the control and elimination of this disease. This study aimed to explore the barriers hindering the implementation of malaria diagnosis and treatment strategies in a focus of active transmission in the San Lorenzo canton, Ecuador.

METHODS

Using a convergent mixed methods design during 2017, the researchers assessed the physical and human resources of the services network at the primary level of care along with the quality assurance activities, patient access to healthcare services and perceptions regarding the care provided to patients with malaria.

RESULTS

The programme's administrative transition from the National Service of Vector-borne Diseases to the Ministry of Public Health is perceived from the interviewed participants to have weakened the diagnosis network established in recent years. A mean of 6.4 ± 0.88 months was found for anti-malarial medication shortage at the primary level of care. Likewise, there was high healthcare staff turnover (permanence, Me = 7 months; IQR = 5-16) and a deficit of general knowledge on the disease among the entirety of healthcare staff, as only 29% of physicians were aware of the correct first-line treatment for malaria by Plasmodium falciparum and Plasmodium vivax. It was evidenced that 95.7% of patients were hospitalized to receive anti-malarial treatment. Both patients and healthcare staff considered the area to be difficult to reach due to its geography and the presence of groups outside the law. They also identified the lack of personnel and microscopy posts in this border area as the main barrier.

CONCLUSION

The network of diagnostic services for malaria is weak in San Lorenzo, and socio-economic, political and historical factors hinder the implementation of the universal malaria elimination strategy based on diagnosis and treatment.