Public Funding for Histofy Ltd

**Mitotic figures** refer to cellular material seen as a cell divides into two daughter cells. They form an important feature of histopathology, which is the study of disease at the cellular level. Amongst other uses, mitotic figures feature prominently in the grading systems for many common cancers, such as breast cancer for example. The current '**_gold standard_**' of detecting and counting mitotic figures is largely based on an expert histopathologist's visual microscopic assessment of an extremely thin (only a few micrometers thick) section of the suspicious tissue specimen glued to a glass slide. This practice has remained more or less the same for several decades, and inevitably results in subjective and variable diagnosis and consequently variable patient management. The manual process is prone to error due to its very subjective nature and is also time consuming. Moreover, the data which can be recorded manually at present is limited to number of mitoses per unit area. Recent uptake of **digital slide scanners** by diagnostic histopathology laboratories and tissue research laboratories marks a new revolution in pathology practice, with our local Coventry & Warwickshire NHS trust being the first one in the UK to use digitally scanned images of tissue slides for routine diagnostics. Digital slide scanners produce multi-gigapixel whole-slide image (WSI) from tissue slides, offering the opportunity to develop intelligent computer algorithms that can process and analyse the image data, produce more objective, reliable and accurate results for diagnostic assistance and improved patient management and provide precise and reproducible measurements for research purposes. **This UK-Korea collaborative R&D project** aims to productise a recent award-winning Artificial Intelligence (AI) based computer algorithm produced in our group that can recognise and count mitotic figures automatically and collate the data in a number of novel ways which has not been possible before. We will capitalise on the recent uptake of digital slide scanners to generate digitally scanned images of tissue slides for routine use. The product developed and deployed in the cloud in this project will make the measurements of mitotic figures more accurate and consistent. In addition, by providing profiling data such as average count of mitotic figures per case and distribution of mitotic hotspot regions, our tool will improve the data provided to histopathologists and oncologists and thereby help improve the management of cancer patients. It will also improve efficiency by saving the time taken to perform manual counts.

**Mitotic figures** refer to cellular material seen as a cell divides into two daughter cells. They form an important feature of histopathology, which is the study of disease at the cellular level. Amongst other uses, mitotic figures feature prominently in the grading systems for many common cancers, such as breast cancer for example. The current '**_gold standard_**' of detecting and counting mitotic figures is largely based on an expert histopathologist's visual microscopic assessment of an extremely thin (only a few micrometers thick) section of the suspicious tissue specimen glued to a glass slide. This practice has remained more or less the same for several decades, and inevitably results in subjective and variable diagnosis and consequently variable patient management. The manual process is prone to error due to its very subjective nature and is also time consuming. Moreover, the data which can be recorded manually at present is limited to number of mitoses per unit area. Recent uptake of **digital slide scanners** by diagnostic histopathology laboratories and tissue research laboratories marks a new revolution in pathology practice, with our local Coventry & Warwickshire NHS trust being the first one in the UK to use digitally scanned images of tissue slides for routine diagnostics. Digital slide scanners produce multi-gigapixel whole-slide image (WSI) from tissue slides, offering the opportunity to develop intelligent computer algorithms that can process and analyse the image data, produce more objective, reliable and accurate results for diagnostic assistance and improved patient management and provide precise and reproducible measurements for research purposes. **This UK-Korea collaborative R&D project** aims to productise a recent award-winning Artificial Intelligence (AI) based computer algorithm produced in our group that can recognise and count mitotic figures automatically and collate the data in a number of novel ways which has not been possible before. We will capitalise on the recent uptake of digital slide scanners to generate digitally scanned images of tissue slides for routine use. The product developed and deployed in the cloud in this project will make the measurements of mitotic figures more accurate and consistent. In addition, by providing profiling data such as average count of mitotic figures per case and distribution of mitotic hotspot regions, our tool will improve the data provided to histopathologists and oncologists and thereby help improve the management of cancer patients. It will also improve efficiency by saving the time taken to perform manual counts.