Public Funding for Ffei Limited

This project will undertake research to characterise; current, emerging and possible future approaches to label production. The driver/incentive for the project is to define processes which; reduce costs, increase flexibility and reduce environmental impact. The current approach to label production utilises offline printing (away from end user product lines), usually provided by 3rd party suppliers who print, convert and ship labels to the packing plant. This approach leads to slow response times and large inventory holding. Emerging technologies utilise nearline or ‘through the wall’ printing, which gives a faster, justin- time response yielding better inventory management – with some companies leasing space to 3rd party label printers. Mass customisation is possible through this approach, but the reliability and uptime requirement is higher. The potential future approach uses inline printing – digital printing directly within the filling line. The benefits of this approach can be summarised as; reduced costs, no outsourcing, reduced waste, mass customisation and the potential for versioning. Potential barriers to the uptake of this approach include; digital print speed, environmental issues (due to the use of print chemicals in close proximity to – e.g. – food production processes), reliability, redundancy and uptime. Currently there are no digital print heads capable of producing the speed required to match the speed of high end applicators on a filling line. The table in Appendix A provides a summary of these current, emerging and possible future approaches. Determining industry inline print goals and requirements will inform FFEI’s technology development roadmap going forward. This research will help FFEI to develop systems that are best suited to the markets’ future inline printing needs. Better understanding of end user requirements and technology needs will enable a cost benefit analysis of the potential opportunity for FFEI to be undertaken.

"Pathologists are the **doctors who diagnose disease**. To do this, we examine samples (""biopsies"") using a microscope, to decide if a sample shows cancer, and what type of cancer it is. New scanners allow us to create digital images of microscope slides, so we can look at them on computers, and share them between hospitals more easily. This is called **digital pathology** - it will be a revolution in how we diagnose cancer. But digital images allow us to go one step further - to use computers to help us look at the images. ""**Artificial intelligence**"" (AI) is a computer technology which allows computers to do pattern recognition. AI can be trained to recognise the patterns of disease - for example searching for small areas of cancer in a large sample. So pathologists will be able to use AI to diagnose cancer faster, better and at lower cost. To work, AI needs to ""learn"" the patterns by looking at large numbers of images and learn from them, like a human would. But there are no large collections of digital images that can be used for this. In this project, the **Northern Pathology Imaging Co-operative (NPIC)**, we are bringing together the NHS, industry and scientists to solve this problem. We will put scanners in 12 Northern NHS hospitals to gather digital pathology images training AI systems. This will generate a lot of data - about 760,000 images per year by the end of the project, which is about 1.2 Petabytes. We will then work with industry and scientists to make new AI systems to analyse our images and make better diagnoses. This is a big opportunity for the UK to lead in this new area. Because it is a **co-operative with a shared goal, led by doctors**, this project will ensure that AI systems are safe, and that doctors and the NHS are in control of how they are used. Just as important, we need to ensure the public understand what we are doing and trust that we are using NHS data properly and securely - this will be an important part of our project. We hope that by the end of our project, we have used the funding to create something that is world leading. We want to develop the best systems for gathering and using data to make AI systems, bringing value for the NHS and patients."

The diagnosis of cancer and many other serious diseases is very often carried out by microscopic examination of stained tissue sections taken as biopsies from a patient. For many years this has been done using light microscopes and the trained eye of a pathologist; a doctor specialising in examining tissue with a microscope. Recently image scanners have been developed that automatically scan the tissue section slide to produce a high magnification colour digital image. Their use is called Digital Pathology and has shown great potential in research. It enables slide images to be viewed remotely (e.g. for a second opinion) and allows advanced digital image processing to help pathologists with diagnosis. As yet Digital Pathology has not been adopted for primary clinical diagnosis. Several factors affect this but an important one is the lack of high quality digital images compared to the microscope. The ‘apparent’ lower quality of the digital images, make pathologists reluctant to use them and the regulatory authorities have not approved them for diagnostic use. The project’s aim is to help enable the transition of Digital Pathology into clinical diagnosis by addressing one of the major barriers to adoption and regulatory approval. In particular the project will deliver images of the same colour when viewed by multiple pathologists at multiple locations and under multiple viewing conditions. Colour profiling to enable colour accuracy and reproducibility in cross-platform image viewing is well established in many fields such as print, cinematography, digital cameras, scanners, video displays etc. Similar colour standards, methods and equipment have yet to be adopted in biomedical fields. The project will develop techniques and hardware to create colour profiles appropriate to biomedical imaging, and digital pathology in particular, delivering colour fidelity to the high levels required for clinical diagnosis.

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