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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. 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. The algorithm helps pathologists obtain the most accurate cancer cell
analysis - in a much faster way. _(This story was auto-published from a syndicated feed. No part of the story has been edited by __FIT__.)_ _(India, and the Capital especially, has been in
an air pollution crisis. How has the hazardous air #pollution impacted you? Write down your #PollutionKaSolution and send it to us at [email protected]. )_