Open AccessMobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection
Author(s) -
Elena Dacal,
David Bermejo-Peláez,
Lin Lin,
Elisa Álamo,
Daniel Cuadrado,
Álvaro Martínez,
Adriana Mousa,
María Postigo,
Alicia Soto,
Endre Sukosd,
A. P. Vladimirov,
Charles Mwandawiro,
Paul M. Gichuki,
Nana Aba Williams,
José Muñóz,
Stella Kepha,
Miguel Luengo-Oroz
Publication year - 2021
Publication title -
plos neglected tropical diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.99
H-Index - 135
eISSN - 1935-2735
pISSN - 1935-2727
DOI - 10.1371/journal.pntd.0009677
Subject(s) - artificial intelligence , trichuris trichiura , computer science , deep learning , telepathology , telemedicine , digitization , trichuris , virtual microscopy , pattern recognition (psychology) , pathology , medicine , computer vision , helminths , immunology , ascaris lumbricoides , health care , economic growth , economics
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.