Computational microscopic imaging for malaria parasite detection: a systematic review

Das, Dev Kumar; Mukherjee, Rajdeep; Chakraborty, Chandan

doi:10.1111/jmi.12270

Cited by 78 publications

(60 citation statements)

References 102 publications

(270 reference statements)

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“…Possible solutions to many of the technological challenges – particularly those related to data cost and network coverage or speed – may include the use of automated apps (such as automated recognition of malaria parasites on a microscope slide image) [26], or of apps with inbuilt clinical advice. Automated systems generally rely on higher processing power and so are less likely to work on cheaper smartphones, depending once again on the network for image upload and receipt of advice.…”

Section: Technological Considerationsmentioning

confidence: 99%

Integrating mHealth at point of care in low- and middle-income settings: the system perspective

Wallis

Blessing

Dalwai

et al. 2017

Global Health Action

122

121

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While the field represents a wide spectrum of products and services, many aspects of mHealth have great promise within resource-poor settings: there is an extensive range of cheap, widely available tools which can be used at the point of care delivery. However, there are a number of conditions which need to be met if such solutions are to be adequately integrated into existing health systems; we consider these from regulatory, technological and user perspectives. We explore the need for an appropriate legislative and regulatory framework, to avoid ‘work around’ solutions, which threaten patient confidentiality (such as the extensive use of instant messaging services to deliver sensitive clinical information and seek diagnostic and management advice). In addition, we will look at other confidentiality issues such as the need for applications to remove identifiable information (such as photos) from users’ devices. Integration is dependent upon multiple technological factors, and we illustrate these using examples such as products made available specifically for adoption in low- and middle-income countries. Issues such as usability of the application, signal loss, data volume utilization, need to enter passwords, and the availability of automated or in-app context-relevant clinical advice will be discussed. From a user perspective, there are three groups to consider: experts, front-line clinicians, and patients. Each will accept, to different degrees, the use of technology in care – often with cultural or regional variation – and this is central to integration and uptake. For clinicians, ease of integration into daily work flow is critical, as are familiarity and acceptability of other technology in the workplace. Front-line staff tend to work in areas with more challenges around cell phone signal coverage and data availability than ‘back-end’ experts, and the effect of this is discussed.

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Section: Technological Considerationsmentioning

confidence: 99%

Integrating mHealth at point of care in low- and middle-income settings: the system perspective

Wallis

Blessing

Dalwai

et al. 2017

Global Health Action

122

121

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“…Plasmodium is a malaria parasite having four well‐known species that is, vivax, ovale, malaria, and falciparum (Hutto, ) and (Das, Mukherjee, & Chakraborty, a). Furthermore, each of the species of Plasmodium has four life cycle stages that is, ring stage, trophozoite, schizont, and gametocyte (Capone et al, ; Das, Mukherjee, et al, a). The different species and life cycle stages of Plasmodium are presented in Figure .…”

Section: Research Backgroundmentioning

confidence: 99%

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Malaria parasitemia diagnosis and grading is hard and still far from perfection. Inaccurate diagnosis and grading has caused tremendous deaths rate particularly in young children worldwide. The current research deeply reviews automated malaria parasitemia diagnosis and grading in thin blood smear digital images through image analysis and computer vision based techniques. Actually, state-of-the-art reveals that current proposed practices present partially or morphology dependent solutions to the problem of computer vision based microscopy diagnosis of malaria parasitemia. Accordingly, a deep appraisal of the current practices is investigated, compared and analyzed on benchmark datasets. The open gaps are highlighted and the future directions are laid down for a complete automated microscopy diagnosis for malaria parasitemia based on those factors that have not been affected by other diseases. Moreover, a general computer vision framework to perform malaria parasitemia estimation/grading is constructed in universal directions. Finally, remaining problems are highlighted and possible directions are suggested. RESEARCH HIGHLIGHTS: The current research presents a microscopic malaria parasitemia diagnosis and grading of malaria in thin blood smear digital images through image analysis and computer vision based techniques. The open gaps are highlighted and future directions for a complete automated microscopy diagnosis of malaria parasitemia mentioned.

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“…Das et al [12], most of the automatic malaria diagnosis methods, using microscopic images, have a common pipeline of processes. The processes are like this, after image acquisition filtering is done to reduce noise and then region of interest (ROI) is segmented.…”

Section: Related Workmentioning

confidence: 99%

“…But this method suffers from over segmentation caused by watershed approach. Bibin, D. and Punitha, P. [6] uses complex algorithm based on active contour model to segment infected blood cells but this algorithm is not efficient for overlapped blood call segmentation properly [12].…”

Section: Related Workmentioning

confidence: 99%

Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples

Ghosh¹,

Bhattacharjee

Nasipuri

2017

J Healthc Inform Res

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Malaria spreads rapidly in a particular time of the year, and it becomes impossible to arrange sufficient number of pathologists and physician at that time, especially in remote places of the developing nations. Thus, low-cost pathological equipment, which can automatically identify and classify the type of malarial parasites from the microscopic images of blood samples, will be of great help for diagnosis and treatment of malaria. The proposed system detects malarial parasites from the microscopic images of blood samples and also categorizes them based on shape, size and texture. To capture different field images of the same sample, a fully automatic slide movement system is devised to control the slide movement under the "objective lens" of the microscope. The sensitivity and specificity of the system are found to be 91.42 and 87.36% respectively. The system has the potential to a powerful supporting tool for telemedicine with very simple operational instructions.

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Computational microscopic imaging for malaria parasite detection: a systematic review

Cited by 78 publications

References 102 publications

Integrating mHealth at point of care in low- and middle-income settings: the system perspective

Integrating mHealth at point of care in low- and middle-income settings: the system perspective

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples

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