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Decalcified histology and assessment of the percentage of empty lacunae are used to quantify the severity of ONFH. However, the current clinical practice of manually counting cells is a tedious and inefficient process. We utilized the power of artificial intelligence by training an established deep convolutional neural network framework, Faster\u2010RCNN, to automatically classify and quantify osteocytes (healthy and pyknotic) and empty lacunae in 135 histology images. The adjusted correlation coefficient between the trained cell classifier and the ground truth was <jats:italic>R<\/jats:italic>\u2009=\u20090.98. 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