Artificial intelligence and deep learning to map immune cell types in inflamed human tissue
2022
Van Buren, Kayla | Li, Yi | Zhong, Fanghao | Ding, Yuan | Puranik, Amrutesh | Loomis, Cynthia A. | Razavian, Narges | Niewold, Timothy B.
Biopsies of inflammatory tissue contain a complex network of interacting cells, orchestrating the immune or autoimmune response. While standard histological examination can identify relationships, it is clear that a great amount of data on each slide is not quantitated or categorized in standard microscopic examinations. To deal with the huge amount of data present in biopsy tissue in an unbiased and comprehensive way, we have developed a deep learning algorithm to identify immune cells in biopsies of inflammatory lesions. We focused on T follicular helper (Tfh) cell subsets and B cells in dermatomyositis biopsy images. We achieved strong performance on detection and classification of cells, including the rare Tfh cell subsets present in the tissue. This algorithm could be used to perform distance mapping between cell types in tissue, and could be easily adapted to other disease states.
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