ArticleJMIR research protocols2017
The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis.
Article in JMIR research protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Pathogenic Significance of Trypanosomatids: Progress in Drug Resistance, Control Strategies, and Artificial Intelligence.Interdisciplinary perspectives on infectious diseases · 2026Review
- Diagnosis ofJournal of clinical microbiology · 2025Article
- Diagnostic accuracy of an automated microscope solution (miLab™) in detecting malaria parasites in symptomatic patients at point-of-care in Sudan: a case-control study.Malaria journal · 2024Article
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system.PloS one · 2024Article
- Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears.Open forum infectious diseases · 2023Article
- Role of Artificial Intelligence in Global Surgery: A Review of Opportunities and Challenges.Cureus · 2023Review
- Web Geographic Information System: A Support Tool for the Study, Evaluation, and Monitoring of Foci of Malaria Transmission in Mexico.International journal of environmental research and public health · 2023Article
- Leveraging innovation technologies to respond to malaria: a systematized literature review of emerging technologies.Malaria journal · 2023Review
- Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
- Automatic detection of the parasitePeerJ · 2022Article
- Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances.Computational intelligence and neuroscience · 2022Review
- A scope of mobile health solutions in COVID-19 pandemics.Informatics in medicine unlocked · 2021Review
- Malaria Screener: a smartphone application for automated malaria screening.BMC infectious diseases · 2020Article
- DZC DIAG: mobile application based on expert system to aid in the diagnosis of dengue, Zika, and chikungunya.Medical & biological engineering & computing · 2020Article
- Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries.Globalization and health · 2020Article
- Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study.JMIR public health and surveillance · 2020Article
- Artificial intelligence for global health.Science (New York, N.Y.) · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMalaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority.
objectiveThe objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development.
methodsThe system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells.
resultsAs a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly.
conclusionsAccessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.
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