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Tag: computational pathology

Transforming medical image analysis with deep learning AI

Aiforia Technologies is a medical AI software company seeking to transform clinical pathology and medical research by bringing deep learning AI to assist and augment human experts in medical image analysis.

We had the chance to ask CEO Kaisa Helminen about AI in healthcare and Aiforia’s newest platform.

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What does the future hold for AI in Digital Pathology?

This is the second of a two-part blog post. In his first post, Liron wrote on embedding AI in Digital Pathology workflows.

Digital Pathology AI apps are certainly feasible, but exactly when they will be ready for clinical use is less clear.

There are potentially hundreds or thousands of algorithms that will need to be developed. Currently, there are only a handful of algorithms that are approved by regulatory bodies for clinical practice, so we’ve got a long way to go.

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How do we embed AI into Digital Pathology workflows?

This is an exciting time in pathology: now that digital pathology is mature, we have noticed an uptake in a lot of AI start-up companies. Most of the algorithms have been developed on a research basis or in a test environment, and only recently applied. What follows is a summary of the work we are doing at the University of Pittsburgh Medical Center testing these AI apps, and some of the questions arising therefrom.

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Ten things you need to know about going 100% digital for primary histopathology diagnosis

Digital pathology is based on creating a digital replica of the glass slide, called whole slide image (WSI). This image is then viewed in a computer screen, which eliminates the need for using a microscope. Can you imagine working in a glassless environment, with better ergonomics and being able to immediately find the slide you need?

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The Benefits of Computational Pathology

The histological assessment of human tissue has emerged as a key challenge for the detection and treatment of cancer. Many tissue sections have to be processed in order to find those that contain cancer, which can be a timely and costly process. Similarly, procedures such as immunohistochemical scoring can be problematic for cases such as ER, PR, and HER2.

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