ArticleMalaria journal2015
Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria.
Article in Malaria journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Parasitology of the twenty-first century: are we moving in the right direction?Journal of medical microbiology · 2025Review
- Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings.Frontiers in artificial intelligence · 2022Article
- A Novel Concentric Circular Coded Target, and Its Positioning and Identifying Method for Vision Measurement under Challenging Conditions.Sensors (Basel, Switzerland) · 2021Article
- The Influence of Camera and Optical System Parameters on the Uncertainty of Object Location Measurement in Vision Systems.Sensors (Basel, Switzerland) · 2020Article
- Image analysis and machine learning for detecting malaria.Translational research : the journal of laboratory and clinical medicine · 2018Review
- Tools for surveillance of anti-malarial drug resistance: an assessment of the current landscape.Malaria journal · 2018Review
- Diagnostic tools in childhood malaria.Parasites & vectors · 2018Review
- Evaluation of the Parasight Platform for Malaria Diagnosis.Journal of clinical microbiology · 2017Article
- Computer Vision Malaria Diagnostic Systems-Progress and Prospects.Frontiers in public health · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroscopy has long been considered to be the gold standard for diagnosis of malaria despite the introduction of newer assays. However, it has many challenges like requirement of trained microscopists and logistic issues. A vision based device that can diagnose malaria, provide speciation and estimate parasitaemia was evaluated.
methodsThe device was evaluated using samples from 431 consented patients, 361 of which were initially screened by RDT and microscopy and later analysed by PCR. It was a prospective, non-randomized, blinded trial. Quantification of parasitaemia was performed by two experienced technicians. Samples were subjected to diagnosis by Sight Dx digital imaging scanning.
resultsThe sensitivity and specificity of the SightDx P1 device for analysed samples were found to be 97.05 and 96.33%, respectively, when compared to PCR. When compared to microscopy, sensitivity and specificity were found to be 94.4 and 95.6%, respectively. The device was able to speciate 73.3% of the PCR Plasmodium falciparum positive samples and 91.4% of PCR Plasmodium vivax positive samples.
conclusionThe ability of the device to detect parasitaemia as compared with microscopy, was within 50% in 71.3% of cases and demonstrated a correlation coefficient of 0.89.
Indexed as
Identifiers
What OpenQuestion holds
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.