These findings highlight a growing role for AI in medical care, he added, offering a way to improve the efficiency and accuracy of pathology analyses. Importantly, the cell spatial organisation features identified by Ceograph are interpretable and lead to biological insights into how individual cell-cell spatial interaction change could produce diverse functional consequences, Xiao said. In each scenario, the Ceograph model significantly outperformed traditional methods in predicting patient outcomes. In the third, the team identified which lung cancer patients were most likely to respond to a class of medications called epidermal growth factor receptor inhibitors. In another, they predicted the likelihood of potentially malignant oral disorders-precancerous lesions of the mouth-progressing to cancer. In one, they used Ceograph to distinguish between two subtypes of lung cancer, adenocarcinoma or squamous cell carcinoma. The researchers successfully applied this tool to three clinical scenarios using pathology slides. The new AI model, named Ceograph, mimics how pathologists read tissue slides, starting with detecting cells in images and their positions.įrom there, it identifies cell types as well as their morphology and spatial distribution, creating a map in which the arrangement, distribution, and interactions of cells can be analysed. However, these models don't successfully recapitulate more complex aspects of how pathologists interpret tissue images, such as discerning patterns in cell spatial organisation and excluding extraneous "noise" in images that can muddle interpretations.
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