ArticlePloS one2017
Mathematical algorithm for the automatic recognition of intestinal parasites.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 33 citations in OpenAlex.
- Automated detection and classification of intestinal protozoan cysts using a nuclei structure-based deep learning framework for epidemiological surveillance.Parasite epidemiology and control · 2026Article
- Detecting soil-transmitted helminth and Schistosoma mansoni eggs in Kato-Katz stool smear microscopy images: A comprehensive in- and out-of-distribution evaluation of YOLOv7 variants.PLoS neglected tropical diseases · 2025Article
- Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images.Scientific reports · 2025Article
- Validation of Vetscan ImagystParasites & vectors · 2024Article
- Diagnosis of Human Trematode Infections.Advances in experimental medicine and biology · 2024Review
- Parasitic egg recognition using convolution and attention network.Scientific reports · 2023Article
- Comparison of Mini-FLOTAC, Flukefinder and sedimentation techniques for detection and quantification of Fasciola hepatica and Calicophoron daubneyi eggs using spiked and naturally infected bovine faecal samples.Parasites & vectors · 2023Article
- A review on innovative optical devices for the diagnosis of human soil-transmitted helminthiasis and schistosomiasis: from research and development to commercialization.Parasitology · 2023Review
- Automatic recognition of parasitic products in stool examination using object detection approach.PeerJ. Computer science · 2022Article
- Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.PLoS neglected tropical diseases · 2021Article
- Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals.Frontiers in veterinary science · 2021Review
- Prevalence of Intestinal Parasites in a Low-Income Texas Community.The American journal of tropical medicine and hygiene · 2020Article
- Automated Diagnosis of Canine Gastrointestinal Parasites Using Image Analysis.Pathogens (Basel, Switzerland) · 2020Article
- A historical review of the techniques of recovery of parasites for their detection in human stools.Revista da Sociedade Brasileira de Medicina Tropical · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Parasitic infections are generally diagnosed by professionals trained to recognize the morphological characteristics of the eggs in microscopic images of fecal smears. However, this laboratory diagnosis requires medical specialists which are lacking in many of the areas where these infections are most prevalent. In response to this public health issue, we developed a software based on pattern recognition analysis from microscopi digital images of fecal smears, capable of automatically recognizing and diagnosing common human intestinal parasites. To this end, we selected 229, 124, 217, and 229 objects from microscopic images of fecal smears positive for Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica, respectively. Representative photographs were selected by a parasitologist. We then implemented our algorithm in the open source program SCILAB. The algorithm processes the image by first converting to gray-scale, then applies a fourteen step filtering process, and produces a skeletonized and tri-colored image. The features extracted fall into two general categories: geometric characteristics and brightness descriptions. Individual characteristics were quantified and evaluated with a logistic regression to model their ability to correctly identify each parasite separately. Subsequently, all algorithms were evaluated for false positive cross reactivity with the other parasites studied, excepting Taenia sp. which shares very few morphological characteristics with the others. The principal result showed that our algorithm reached sensitivities between 99.10%-100% and specificities between 98.13%- 98.38% to detect each parasite separately. We did not find any cross-positivity in the algorithms for the three parasites evaluated. In conclusion, the results demonstrated the capacity of our computer algorithm to automatically recognize and diagnose Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica with a high sensitivity and specificity.
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