Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection

Elena Dacal, David Bermejo-Peláez, Lin Lin, Elisa Álamo, Daniel Cuadrado, Álvaro Martínez, Adriana Mousa, María Postigo, Alicia Soto, Endre Sukosd, Alexander Vladimirov, Charles Mwandawiro, Paul Gichuki, Nana Aba Williams, José Muñoz, Stella Kepha and Miguel Luengo-Oroz

PLOS Neglected Tropical Diseases, 2021, vol. 15, issue 9, 1-14

Abstract: Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic 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 STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI models.Author summary: Soil-transmitted helminths (STH), including hookworm, Ascaris lumbricoides and Trichuris trichiura, are common intestinal infections in low-income countries. Global estimates indicate that more than 1.5 billion people are infected with at least one STH species. They cause anaemia, gastro-intestinal problems, tiredness amongst other symptoms. Diagnosis of STH infection is mainly performed by analyzing stool samples under the microscope using the so-called Kato-Katz technique. However, the analysis of Kato-Katz samples, which is usually performed by microscopy experts, is a subjective procedure based on visual inspection of the samples and requires to be done in a short period of time since the sample preparation. In this work we proposed a novel system to digitize the microscopy samples using an affordable 3D-printed adapter and smartphones. Digitized images were uploaded to a telemedicine platform enabling remote diagnosis. Additionally, the digitized images were automatically analyzed by an Artificial Intelligence (AI) algorithm which was fully-integrated in the telemedicine platform, performing an automatic and objective count of different types of STH parasites (A. lumbricoides and T. trichiura).

Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0009677 (text/html)
https://journals.plos.org/plosntds/article/file?id ... 09677&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pntd00:0009677

DOI: 10.1371/journal.pntd.0009677

Access Statistics for this article

More articles in PLOS Neglected Tropical Diseases from Public Library of Science
Bibliographic data for series maintained by plosntds ().