Gut worm infections are a significant burden on grazing livestock and humans and result in considerable expenditure on animal/ human health products. The Faecal egg count is the established method for estimating the severity of parasitic worm infection in livestock and humans. Existing methods for diagnosing the presence of worm infections are widely considered to be dated, inaccurate and laborious. Arrow Labs disruptive diagnostic delivers a new standard of detection and will help to address these challenges in animal and human systems for improved economic returns from farming, protection of animal welfare, and reduction of disease burdens in people. Arrow Labs novel processes that will be developed further by this project will lead to new products and streamline existing services for commercial service providers. Moreover, an end-to-end system that automates sample processing and result delivery does not currently exist: this will increase throughput, decrease unit costs, increase reliability by removing human error, and link to automated decision support. This project forms the final phase of industrial research that will deliver automated faecal sample preparation for machine analysis and enable the commencement of product commercialisation.

The detection and hence targeted control of parasitic worms in grazing livestock, domestic pets and humans in developing countries is of increasing worldwide interest as anthelmintic drug resistance and hence ineffectiveness takes hold. This proposal aims to prototype an automated, portable digital analyser for cytological samples which, in conjunction with cloud based digital image enhancement will provide data sufficiently rich in detail to allow automated machine counting of worm egg cells present in a faecal sample. Such a device has the prospect of being affordable, simple to use, rugged and compact. Automated faecal egg counts will lead to more efficient production of animal products, attenuation of greenhouse gas emissions by livestock, and benefit society at large from more sustainable, ethical and secure food production. Cattle, sheep, goats, pigs, horses, domestic pets and potentially humans will benefit from improved health and welfare.

Parasitic worms pose a major challenge to the health and productivity of grazing livestock, and are controlled by frequent adminstration of anthelmintic drugs. This favours the development of drug resistance. Increasingly, targeted approaches to deworming are required, based on levels of infection and known drug efficacy. The most flexible and useful test for worm infection is the faecal egg count (FEC), in which numbers of eggs are manually counted following extraction from faecal samples. However, this is time consuming and therefore expensive, and relies on highly trained staff in remote laboratories. On-farm FEC kits have met with some success but are undermined by operator error and time demands. This proposal aims to develop an automated FEC system, building on recent advances in image analysis through machine learning to identify and count eggs in faecal samples at the pen-side, and return results to farmers and owners quickly enough to influence treatment decisions. The potential for uptake of this new technology will be evaluated, and the best models for commercialisation considered. Automated FEC results will be linked to online decision support systems, making use of serial FEC results and management data provided by the farmer, as well as climate data. Intended beneficiaries include UK business, by becoming market leaders in globally attractive technology, farmers and consumers through more efficient production of animal products and attenuation of greenhouse gas emissions by livestock, and society at large from more sustainable, ethical and secure food production. Cattle, sheep, goats, pigs and horses will benefit from improved health and welfare.