ArticleFrontiers in public health2021
Identification of Snails and
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review.Communications medicine · 2026Article
- Automated real-time surveillance of Bithynia snails using a comparative YOLO based approach for liver fluke host detection.Scientific reports · 2026Article
- The research contribution of the Schistosomiasis Collection at the Natural History Museum (SCAN): highlights, challenges and future directions.Infectious diseases of poverty · 2025Review
- Metabolomics assays applied to schistosomiasis studies: a scoping review.BMC infectious diseases · 2025Article
- Development and Application of an In Vitro Drug Screening Assay forBiomedicines · 2024Article
- Scoping Review of Climate Change Adaptation Interventions for Health: Implications for Policy and Practice.International journal of environmental research and public health · 2024Article
- CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.Scientific reports · 2024Article
- High prevalence of natural infection by the ruminant blood flukeVeterinary world · 2024Article
- MALDI-TOF: A new tool for the identification of Schistosoma cercariae and detection of hybrids.PLoS neglected tropical diseases · 2023Article
- Development of New Technologies for Risk Identification of Schistosomiasis Transmission in China.Pathogens (Basel, Switzerland) · 2022Review
- Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams.Bioinorganic chemistry and applications · 2022Article
- Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.PLoS neglected tropical diseases · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans
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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.