ArticleFrontiers in microbiology2023
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- An AI-powered self-driving microscope for low-cost acute leukemia detection.Nature communications · 2026Article
- Evaluating the Adversarial Robustness and Clinical Safety of Quantized Hierarchical Transformers for Edge-Based Malaria Microscopy.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Imaging malaria parasites across scales and time.Journal of microscopy · 2026Review
- Artificial intelligence for precision malaria control: transforming surveillance, prediction, and intervention strategies.Frontiers in digital health · 2026Review
- Blood Parasites and Wildlife: The Development of a Discipline.Integrative zoology · 2026Review
- Scoping review of artificial intelligence via mobile technology and social media for health in Africa.Nature communications · 2025Article
- Validation of Digital Slide Scanning and a Convolutional Neural Network for the Detection of Intestinal Parasites in Human Stool Samples.Diagnostics (Basel, Switzerland) · 2025Article
- Predictive for patients with pneumonia in pediatric intensive care unit.Frontiers in pediatrics · 2025Article
- Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.International journal of environmental research and public health · 2024Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.Malaria journal · 2024Article
- Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of
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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.