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Tag: ai

Digital Pathology, Computational Biology, Organ Transplants, & the Future of Medicine

Both transplant outcomes and lab methods have stagnated over the last 40 years. Ishita Moghe and Professor Kim Solez comment upon the rapidly changing landscape of medical research and the potential of digital pathology for transforming patient outcomes.

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Developing computationally derived imaging biomarkers for maximum clinical impact

Professor Anant Madabhushi is a world-recognised, award-winning leader in computerized imaging research and translational applications, with over 160 peer-reviewed journal publications and close to 100 patents issued or pending. He is a keynote speaker at the 6th Digital Pathology & AI Congress: USA. He explains here why having patents is not enough…

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Developing Deep Learning Models for Pathology Analysis

Ahead of the 6th Digital Pathology & AI Congress: USA, Dr Saeed Hassanpour introduces us to the subject of his presentation: the opportunities and challenges in developing deep learning based tools for histology.

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Inspirata and Cirdan Announce New Technical Partnership at the Digital Pathology & AI Congress

At the 6th Digital Pathology & AI Congress, Inspirata and Cirdan announced their new technical partnership designed to empower NHS clinical laboratories to accelerate their use of digital pathology.

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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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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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Reshaping drug discovery with deep learning and polypharmacology

Cyclica is a Toronto-based biotech that leverages artificial intelligence and computational biophysics to reshape the drug discovery process. We spoke to their Chief Scientific Officer Andreas Windemuth.

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