ArticleScientific reports2016
Mobile phone imaging and cloud-based analysis for standardized malaria detection and reporting.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Efficacy of the mLab App: a randomized clinical trial for increasing HIV testing uptake using mobile technology.Journal of the American Medical Informatics Association : JAMIA · 2025Trial
- Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.Nature communications · 2026Article
- A multi-site laboratory evaluation of the MEDSCAN application for automated POC-CCA interpretation.Frontiers in parasitology · 2026Article
- Next-generation hybrid bioanalytical platforms and clinical integration of TDM technologies for precision monitoring to optimize last-resort antibiotic therapy.Frontiers in pharmacology · 2026Review
- Mobile app-based malaria surveillance and vector control integration: a scoping review of evidence from endemic settings.Malaria journal · 2025Article
- Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.medRxiv : the preprint server for health sciences · 2025Article
- Enhancing Sensitivity of Commercial Gold Nanoparticle-Based Lateral Flow Assays: A Comparative Study of Colorimetric and Photothermal Approaches.Sensors (Basel, Switzerland) · 2025Article
- Development of a novel quantitative lateral flow assay for vancomycin for therapeutic drug monitoring.Scientific reports · 2025Article
- Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.Advances and applications in bioinformatics and chemistry : AABC · 2024Review
- Service availability and readiness of malaria surveillance information systems implementation at primary health centers in Indonesia.PloS one · 2023Article
- Innovations in3 Biotech · 2021Review
- Recurrence monitoring for ovarian cancer using a cell phone-integrated paper device to measure the ovarian cancer biomarker HE4/CRE ratio in urine.Scientific reports · 2021Article
- Two-Color Duplex Platform for Point-of-Care Differential Detection of Malaria and Typhoid Fever.Analytical chemistry · 2021Article
- Smartphone-based DNA malaria diagnostics using deep learning for local decision support and blockchain technology for security.Nature electronics · 2021Article
- Article
- mHAT app for automated malaria rapid test result analysis and aggregation: a pilot study.Malaria journal · 2021Article
- Inductively coupled plasma optical emission spectroscopy as a tool for evaluating lateral flow assays.Analytical methods : advancing methods and applications · 2021Article
- Rapid Yeast Cell Viability Analysis by Using a Portable Microscope Based on the Fiber Optic Array and Simple Image Processing.Sensors (Basel, Switzerland) · 2020Article
- Feasibility of a point-of-care test based on quantum dots with a mobile phone reader for detection of antibody responses.PLoS neglected tropical diseases · 2019Article
- Towards Lateral Flow Quantitative Assays: Detection Approaches.Biosensors · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Rapid diagnostic tests (RDTs) have been widely deployed in low-resource settings. These tests are typically read by visual inspection, and accurate record keeping and data aggregation remains a substantial challenge. A successful malaria elimination campaign will require new strategies that maximize the sensitivity of RDTs, reduce user error, and integrate results reporting tools. In this report, an unmodified mobile phone was used to photograph RDTs, which were subsequently uploaded into a globally accessible database, REDCap, and then analyzed three ways: with an automated image processing program, visual inspection, and a commercial lateral flow reader. The mobile phone image processing detected 20.6 malaria parasites/microliter of blood, compared to the commercial lateral flow reader which detected 64.4 parasites/microliter. Experienced observers visually identified positive malaria cases at 12.5 parasites/microliter, but encountered reporting errors and false negatives. Visual interpretation by inexperienced users resulted in only an 80.2% true negative rate, with substantial disagreement in the lower parasitemia range. We have demonstrated that combining a globally accessible database, such as REDCap, with mobile phone based imaging of RDTs provides objective, secure, automated, data collection and result reporting. This simple combination of existing technologies would appear to be an attractive tool for malaria elimination campaigns.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.