In Toxicologic pathology
Many compounds affect the cellularity of hematolymphoid organs including bone marrow. Toxicologic pathologists are tasked with their evaluation as part of safety studies. An artificial intelligence (AI) tool could provide diagnostic support for the pathologist. We looked at the ability of a deep-learning AI model to evaluate whole slide images of macaque sternebrae to identify and enumerate bone marrow hematopoietic cells. The AI model was trained and able to differentiate the hematopoietic cells from the other sternebrae tissues. We compared the model to severity scores in a study with decreased hematopoietic cellularity. The mean cells/mm2 from the model was lower for each increase in severity score. The AI model was trained by 1 pathologist, providing proof of concept that AI model generation can be fast and agile, without the need of a cross disciplinary team and significant effort. We see great potential for the role of AI-based bone marrow screening.
Smith Mark A, Westerling-Bui Thomas, Wilcox Angela, Schwartz Julie
artificial intelligence, bone marrow, cellularity, deep learning, digital pathology, image analysis, machine learning, whole slide imaging