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.
Highlights
Soil-transmitted helminths (STH), which include hookworms (Ancylostoma duodenale and Necator americanus), roundworm (Ascaris lumbricoides), and whipworm (Trichuris trichiura) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs) and occur predominantly in low- and middle-income tropical and subtropical countries [1]
Diagnosis of STH infection is mainly performed by analyzing stool samples under the microscope using the so-called Kato-Katz technique
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
Summary
Soil-transmitted helminths (STH), which include hookworms (Ancylostoma duodenale and Necator americanus), roundworm (Ascaris lumbricoides), and whipworm (Trichuris trichiura) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs) and occur predominantly in low- and middle-income tropical and subtropical countries [1]. The diagnostic method recommended by WHO is Kato-Katz, a laboratory method for preparing human stool samples in a microscope smear using a small spatula and slide template. It allows a standardized amount of faeces to be examined under a microscope and quantify STH infection [5,6]. Kato-Katz is generally more sensitive than other microscopic methods such as McMaster, formol-ether concentration, and direct microscopy in high transmission settings, and it requires limited equipment and is easy to perform in low resource settings [7]. It often presents a decreased sensitivity in low-transmission settings
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