HYBRID EVENT: You can participate in person at London, UK or Virtually from your home or work.

International Conference on
Veterinary Science

August 28-30, 2023 | London, UK
VET 2023

Kerrie Duffy

Kerrie Duffy, Speaker at Veterinary Science Conferences
Micron Agritech, Ireland
Title : Comparison of strongyle egg counts from ovine faecal samples obtained through a common laboratory analysis (traditional McMaster) and a rapid, on-site parasite diagnostic device utilising machine learning (Micron Kit).


Conventional treatment for gastrointestinal parasitic worms (helminths) in grazing livestock often involves untargeted, metaphylactic blanket treatment of animals with anthelmintics. As a result, worm resistance to anthelmintic drugs has become a significant issue for farmers and veterinarians worldwide, impacting farm profitability and animal welfare. Faecal egg counts (FECs) are an important diagnostic test to combat further anthelmintic resistance as they enable practitioners to better distinguish between animals that require treatment and those that do not. FECs are labour-intensive, time-consuming and require trained personnel to process the samples and visually identify the parasite eggs and oocysts. Consequently, the time between sample collection, transport, analysis, results and treatment can take days. This study aimed to evaluate a rapid, on-site parasite diagnostic device utilising a smartphone app and machine learning (Micron Kit) in terms of its capability to provide reliable egg counts while mitigating the time and labour requirements associated with a common FEC method (traditional McMaster) performed by a specialised laboratory. A total of 105 ovine faecal samples were collected. Each sample was homogenised and split equally between two labelled containers. One container per sample was processed using Micron Kit, the second container was sent to the laboratory. Briefly, 3 g of faeces were added to 42 mL of water (Micron Kit) or saturated saline (laboratory). The mixture was then homogenised, strained and prepared for either automated or manual microscopic analysis. Strongyle egg counts were conducted via video footage of samples by the Micron Kit machine learning algorithm (ML) and a Micron Agritech technician (MT) and via McMaster slide by an independent laboratory technician (MM). Results were statistically analysed using a generalised linear model using SAS® (Version 9.4) software. The ratio of means was used to determine non-inferiority of the ML results compared to the MM results. The detection limit for each method was obtained using the sample dilution ratio and the total volume analysed. Absolute egg counts were converted to eggs per gram of faeces (EPG). Both Micron Kit egg counts (ML and MT) were higher (p < 0.0001) compared to those obtained from the laboratory (MM). There was no difference between the ML and MT counts. The lower detection limit of the Micron Kit was 30 EPG compared to 50 EPG for the laboratory method. The Micron Kit method utilising a machine learning algorithm has been found to be non-inferior to the traditional McMaster method used
by a specialised laboratory at quantifying Strongyle eggs in ovine faecal samples. With its lower detection limit and reduced labour and time requirements this diagnostic device can help veterinarians to increase their testing capacity, perform on-farm testing and deliver faster and more targeted parasite treatment to combat anthelmintic resistance. Future research will be aimed at validating this technology for further parasite species.
Audience take away:
• Faecal samples can be processed directly on-farm or in practice with reliable results obtained in minutes.
• Simple sample preparation and automated analysis through machine learning enables high throughput testing and requires no specialised training.
• Client results can be tracked and shared with farmers directly via the app through email or messenger services.
• Fast turnaround on results can help farmers to adopt a test-then-treat approach more easily. This will help to combat further anthelmintic resistance.
• Further parasites are currently being validated for use with the method and technology.


Dr. Ní Dhufaigh graduated from the National University of Galway in 2015 with a BSc (Hon) in Marine Science. She then joined the MFRC of Galway Mayo Institute of Technology in 2017 as a PhD student elucidating the virulence factors of Neoparamoeba perurans. In February 2021, she commenced her post-doctoral position at Teagasc, Ireland working on bovine respiratory disease by viral metagenomic and 16S sequencing. She is now working as a research associate at Micron Agritech, an Irish biotechnology company specialising in rapid animal parasite diagnostics.