Mathematical algorithm for the automatic recognition of intestinal parasites

Mantari, Alicia Alva; Cangalaya, Carla; Quiliano, Miguel; Krebs, Casey; Gilman, Robert H.; Sheen, Patricia; Zimic, Mirko

doi:10.1371/journal.pone.0175646

Cited by 28 publications

(16 citation statements)

References 14 publications

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“…Several approaches for computer-aided analysis of helminth eggs detection and classification using artificial intelligence have been investigated in the last years. Alva et al proposed the use of hand-crafted features along with a multivariate logistic regression for intestinal parasites classification (14). Other notable recent deep learning-based approach used a large fecal database with over 1122 patients including 22440 images for the identification of visible components in feces, including blood and epithelial cells, as well as STH eggs, proposing the so-called FecalNet (15) .This work proved the potential of the use of these methods for the automatic analysis of stool samples using conventional microscopy images.…”

Section: Introductionmentioning

confidence: 99%

“…Alva et al . proposed the use of hand-crafted features along with a multivariate logistic regression for intestinal parasites classification (14). Other notable recent deep learning-based approach used a large fecal database with over 1122 patients including 22440 images for the identification of visible components in feces, including blood and epithelial cells, as well as STH eggs, proposing the so-called FecalNet (15).…”

Section: Introductionmentioning

confidence: 99%

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Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Dacal¹,

Bermejo-Peláez²,

Lin

et al. 2021

Preprint

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Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). Kato-Katz technique is the diagnosis method recommended by WHO and although is generally more sensitive than other microscopic methods in high transmission settings, it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence methods based on digitized samples can support diagnostics efforts by support diagnostics efforts by performing an automatic and objective quantification of disease infection.In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of soil-transmitted helminths. Our solution includes (1) a digitalization system based on a mobile app that digitizes the microscope samples using a low-cost 3D-printed microscope adapter, (2) a telemedicine platform for remote analysis and labelling and (3) novel deep learning algorithms for automatic assessment and quantification of parasitological infection of STH.This work has been evaluated by comparing the STH quantification using both a manual remote analysis based on the digitized images and the AI-assisted quantification against the reference method based on conventional microscopy. The deep learning algorithm has been trained and tested on 41 slides of stool samples containing 949 eggs from 6 different subjects using a cross-validation strategy obtaining a mean precision of 98,44% and mean recall of 80,94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs.In conclusion, this work has presented a comprehensive pipeline using smartphone-based microscopy integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using artificial intelligence models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Dacal¹,

Bermejo-Peláez²,

Lin

et al. 2021

Preprint

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“…Parasitic diseases as one of the most important health problems in the society, has a higher prevalence in developing countries and economically depressed communities [1,2]. According to a report by the world health organization (WHO), about three billion of world population is infected with intestinal parasite [3,4] and 450 million people, including children experience the adverse effects of intestinal parasitic disease [5].…”

Section: Introductionmentioning

confidence: 99%

Prevalence of Intestinal Parasite Infections among Children in the Day Care Centers of Gonbad-e Kavus County, North-Eastern Iran

Mesgarian

Sofizadeh

Shoraka

et al. 2017

Zahedan J Res Med Sci

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Background: Intestinal parasite infection is one of the major health problems in the world especially in the developing countries. Objectives: This study was an attempt to examine the prevalence of intestinal parasite infection in the day care centers in Gonbad-e Kavus. Methods: In this cross sectional study, 932 children were recruited from 45 day care centers in Gonbad-e Kavus in Iran, through a survey and their stool samples were examined using direct wet mount, formalin-ether concentration and Trichrome staining techniques. Also, scotch tape slides were microscopically analyzed. Data were analyzed using percentage, frequency and Chi-square test using SPSS ver.16. Odds Ratio was used show the effect size of socio-demographic variables on the rate of intestinal parasite infection. Results: The prevalence of intestinal parasite infections among the children was 26.6%. This study found a significant relationship between the children age, parent education and the place of residency with the rate of intestinal parasite infection (P < 0.05). The children in rural areas were 1.55 times more likely than the children in urban areas to be infected with intestinal parasites (OR = 1.55). Moreover, the results of this study showed that an increase in parent's education for one level decreased the risk of infection in their children by 6% (OR = 0.94). Also, with a rise in the age of participants for a year, the risk of infection increased by 44% (OR = 1.44). Conclusions: Due to the high rate of intestinal parasite infections among children, parents and child care workers need to learn about the various means by which parasites can be transmitted in day care centers.

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“…In addition, the DAPI system significantly advanced the technique compared to several studies on automated diagnosis algorithms for human feces [31][32][33][34][35][36], since, to our understanding, these works largely evade a real examination process of feces by relying on perfect parasite images and are really good with respect to focus plane and color, and preferably with a residue-free parasite. The real examination has a mandatory protocol consisting of several steps, such as sampling, transport, and laboratory preparation of feces for later reading under conventional light microscopy [14,[22][23][24].…”

Section: Discussionmentioning

confidence: 99%

Automated Diagnosis of Canine Gastrointestinal Parasites Using Image Analysis

et al. 2020

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Because canine intestinal parasites are considered cosmopolitan, they carry significant zoonotic potential to public health. These etiological agents are routinely diagnosed using microscopic examination commonly used because of its low cost, simple execution, and direct evidence. However, there are reports in the literature on the poor performance of this test due to low to moderate sensitivity resulting from frequent errors, procedures and interpretation. Therefore, to improve the diagnostic efficiency of microscopic examination in veterinary medicine, we developed and evaluated a unique new protocol. This system was tested in a study involving four genera of highly prevalent canine intestinal parasites in an endemic region in São Paulo state, Brazil. Fecal samples from 104 animals were collected for this research. The new protocol had a significantly higher (p < 0.0001) number of positive cases on image data, including parasites and impurities, and was elaborate to test them with the TF-GII/Dog technique, with a moderate agreement and Kappa index of 0.7636. We concluded that the new Prototic Coproparasitological Test for Dogs (PC-Test Dog) allowed a better visualization of the parasitic structures and showed a favorable result for the diagnosis of intestinal parasites in dogs.

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Mathematical algorithm for the automatic recognition of intestinal parasites

Cited by 28 publications

References 14 publications

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Mobile microscopy and telemedicine platform assisted by deep learning for quantification ofTrichuris trichiurainfection

Prevalence of Intestinal Parasite Infections among Children in the Day Care Centers of Gonbad-e Kavus County, North-Eastern Iran

Automated Diagnosis of Canine Gastrointestinal Parasites Using Image Analysis

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