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Digital Pathology

3 Ways Single-Cell Sequencing and AI are Transforming Kidney Transplantation

Transplantation is now a successful therapy for end-stage renal disease (ESRD). The first successful kidney transplantation happened in Boston in 1954 and the procedure is now routine clinical practice in more than 80 countries worldwide.

Ishita Moghe and Kim Solez outline some of the major strides taken in kidney pathology and transplantation through the use of scRNA-seq and Artificial Intelligence (AI). 

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Digital Pathology Solutions for low-budget and low-tech regions of the world

Digital pathology is transforming possibilities for patient care and the nature of pathologists’ work. But it is clear that in low-income countries, access to pathology and laboratory medicine (PALM) services is very restricted and under-developed.[1] Low-income countries bear a disproportionate share of the global disease burden, yet they are so under-resourced in the pathology and laboratory services that are vital for the accurate diagnosis and treatment of disease.[2] This presents a huge issue for global health.

Given this global context, Rebecca Calder, Daniel Stevens and Zev Leifer’s poster, presented at the 5th Digital Pathology and AI Congress, is of notable significance.

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Deep Learning based detection of tumor tissue compartments improves prognostic immunoprofiling in muscle-invasive bladder cancer

Worldwide, bladder cancer (BC) is the 11th most commonly diagnosed cancer. In men, BC is the 7thmost commonly diagnosed cancer worldwide.[1]Although men are more likely to develop BC than women, women present with more advanced disease and have worse survival rates.[2]

Muscle-invasive bladder cancers (MIBC) are cancers that have grown into or through the muscle layers of the bladder wall.

Dr. Katharina Nekolla and Ansh Kapil and their team applied Deep Learning enabled pathology to better understand prognostic factors in MIBC. Here we review the significance of the research and share their original poster.

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How will Ground Truth Colour Standardisation yield the benefits of AI in Medical Imaging?

The essence of colour management is to ensure that the original target translates through a digital pathway so that the output images are exactly the same colour as the original.

When applied to medical imaging, and considering all the specific stains used in pathology, colour management becomes important. Particular coloured stains bind specific structures of cells in tissue to confer visualisation of diagnostic information. Without translating the colour through the digital pathway correctly, you lose the aspect of diagnostic information, which comes from specific colours.

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Machine Learning Innovation to aid Clinical Decision Making in Pathology

“A medical image alone doesn’t add value unless it is tied to a clinical decision-making process” – Jochen Lennerz

The Breast Cancer Scanning Initiative (BCSI) scans histological slides of patients with high-risk breast lesions to generate annotated images. We then apply a Machine Learning algorithm to the slides to determine whether surgery is necessary.

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Taking biomarker research to a new level

Ahead of the 6th Digital Pathology and AI Congress: Europe in December, we are revisiting Professor Inti Zlobec’s research, presented at last year’s Congress, which is opening a whole field of digital pathology research. Zlobec’s research applies the latest digital pathology technology to produce high-quality tissue microarrays for biomarker analysis.

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Bridging the gap between pathologist and algorithm

Could AI replace pathologists? As we look forward to the 6th Digital Pathology and AI Congress: Europe on the 5th – 6th December 2019, we look back at Dr Hamid Tizhoosh’s keynote presentation from last year.

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Pixel analysis: the new era of digital pathology

Professor David Snead is leading the implementation of digital pathology for Coventry and Warwickshire Pathology Services and directs one of the five Government funded centres in the UK pioneering AI and image diagnostics integration. His main research interests lie in extending the capability of digital pathology for routine diagnostic use and the advancement and deployment of computer assisted diagnostic algorithms. In this third highlight from the conference we revisit Professor Snead’s presentation to get a clinical perspective on how deep learning and artificial intelligence (AI) are set to play an increasing role in daily practice.

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