JavaScript is Disabled
Your browser's JavaScript functionality is disabled. It has to be enabled to use this function of ConfTool.
Here you can find information on how to enable JavaScript
If you have any problems, please contact the organizers at parasitologycommunicator@gmail.com.

Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Daily Overview

Session

CP3.2: Tropical Health 5 min talks sponsored by QIMR Berghofer, Centre for Tropical Health & Emerging Diseases
Location: Lecture Theatre 3
Session Chair: Darren Gray, QIMR Berghofer
Session Chair: Chika Zumuk, Queensland Institute of Medical Research

Presentation 4

EnAIbling parasitic worm control – development of the first artificial intelligence diagnostic test for strongyloidiasis

Suzy Ossipow¹, Richard Bradbury², Catherine Gordon¹, Peter Ward³, Matthew Watts⁴, Darren Gray¹

1: QIMR Berghofer, Australia; 2: James Cook University, Australia; 3: ENAIBLERS, Sweden; 4: New South Wales Health Pathology, Westmead

Strongyloidiasis, “the most neglected tropical disease”, caused by the helminth Strongyloides stercoralis, is a major global concern. It can persist life-long following infection, unless treated, due to the auto-infective lifecycle. Infection can be fatal, particularly among patients with immunosuppression. Despite health risks, knowledge surrounding strongyloidiasis burden and diagnostics remains limited.

The World Health Organization (WHO) advocates for strongyloidiasis’ inclusion in parasite control programs; however, with no population-based diagnostic tests and no large-scale surveys, large-scale treatments are not being provided. Global strongyloidiasis targets are off-track, and millions of people suffer from preventable debility.

Microscopy is a mainstay of population-based helminth surveys. Artificial intelligence (AI) improvements have led to AI-guided microscopy. A novel automated AI-based platform that images and digitises samples on standard microscopy slides to detect and quantify parasitic infections recently showed increased sensitivity and rapidity over human slide readers.

We are developing the first AI model for detecting S. stercoralis using larvae from lab and clinical samples, supporting AI training. We are trialling different preparations for Strongyloides spp. detection, and will undertake field validation in North East Arnhem Land. Our expected outcome is the first field-validated, AI-based S. stercoralis population-level diagnostic test, to allow population treatment programs to commence.