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Artificial Intelligence Can Better Help Doctors To Recognize Cancer Cells

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

Cancer
Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)

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