Automatic disease detection and monitoring in calves

Bovine respiratory disease (BRD) is the most common and costly disease affecting cattle in the world. BRD is a complex bacterial infection that can be fatal and is estimated to cost the UK cattle industry £80M annually. Although manual scoring systems exist to aid early identification of the disease, they are time consuming and rarely used in practice. Commonly, identification of BRD is only in later stages of the disease when antibiotics are essential for treatment. Early and automated identification of BRD will have significant impact: 1) on the economic cost to farmers; 2) reducing the quantity of antimicrobial medicines used to treat the disease; and 3) improving the general welfare of animals.

The proposed project uses artificial intelligence techniques, coupled with visible-range and thermal cameras, to identify BRD at the earliest possible stage with main goals of establishing: 1) how early in disease development affected animals can be reliably identified; 2) the best way to scale up image capture and machine learning to automatically screen animals and alert farmers to those needing treatment; together with 3) developing a protocol for effective use of the trained system. The aim of the proposal is to develop a system based on providing the best possible information in a timely manner, which is key to making right judgements for farmers and vets alike. It is believed that a system based on low cost cameras and sensors, together with state of the art deep neural networks, can provide this.

The completed fellowship will result in a working and tested prototype system capable of development into a viable commercial product. During the fellowship a network of industry collaborators (including farmers, vets, advisory/regulatory bodies, equipment manufacturers and food producers) will be developed to support and promote the research and resulting product.

This proposal details of the activities needed for early automatic detection of disease in collaboration with the farming industry. In the first instance and for the purposes of research and development, the focus of the proposal is Bovine respiratory disease (BRD). BRD is a complex bacterial infection and is the most common and costly disease affecting cattle. In particular, planned collaboration with UK industry in the form of commercial farms and vets, will be developed in order to support the programme outlined in the proposal. Completion of the programme not only offers opportunities within the UK, based on a commercial start-up, but also the potential to develop the product for export, both of which will be important for the UK's future industrial strategy. Beneficiaries of the proposed programme are in both the academic and professional communities, as well benefits for society more generally.

Academically, benefits will accrue in artificial intellegence through applying deep learning to a combination of (low quality) visual and thermographic spectrum data, with the aim of characterisation of pre-clinical BRD. Of particular interest will be the potential to identify which stage an animal is in the disease development, rather than a simple binary classification. Indeed, it is expected that this will highlight subtleties and relationships in the data that are not currently known and will aid disease characterisation. The ability to characterise disease in this 'automatic' way will also be of considerable interest to academic vets who study animal diseases. In veterinary medicine one of the deliverables will be a method of screen-based scoring of disease, based on assessment of visible-range and thermographic images, which will further contribute to the general approach to study diseases in animals. Overall and significantly, the project will contribute to knowledge of how early in the development of disease (BRD in the first instance) it can be reliably identified, this in turn provides information for vets when choosing the most appropriate treatment.

Being able to identify disease earlier has obvious benefits to farmers, such as fewer sick animals for shorter periods, that contribute to lower costs and better welfare. Professional veterinary surgeons also benefit from an addition to the diagnostic tools at their disposal, together with more appropriate treatment. Of particular interest and benefit from the point of view of professional vets is that early diagnosis leads to reduced use of antimicrobial medicines. This is a significant and pressing issue that leads to benefits to wider society through reducing the levels of antimicrobial resistance.

These benefits have already been acknowledged by our collaborators as the following quote (from an email to the applicants, talking about BRD) illustrates

"In the last few weeks - at least in my practice - initial treatment was often followed by re-occurrence and in such cases the previous treatment was not effective anymore. This leads to a prolonged treatment period, which increases the amount of medicine required.

A repeated and prolonged treatment profile regularly results in side-effects; particularly problems with digestion, anorexia, increased gas levels in the rumen, diarrhoea, changes in gastric flora, enterotoxemia, pododermatitis, lameness, and recumbency followed by emaciation. These individuals generally fall behind, others push them away from feeders and harass them, which leads to continuous fear and stress, loss of growth, and increased mortality due to other diseases.

These are real issues that I experience on a daily basis, causing significant amount of loss for the farmers, not to mention aspects of animal welfare. The proposed early detection coupled with timely treatment promises lower medicine consumption, which will minimise the side-effects of antimicrobial treatment."