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Individual factors that lead to susceptibility to Mycobacterium tuberculosis infection among humans and animal models are not clearly defined. As a result, clinicians and scientists have little ability to diagnose and prognose the various clinical manifestations of tuberculosis, from life-long control of latent infection to active tuberculosis disease. Given the challenges in accurately predicting disease outcomes, vaccination with M. bovis Bacille Calmette-Guerin (BCG) vaccine is used globally in children to prevent systemic disease and tuberculous meningitis. However, in adults, epidemiological studies show variable protection ranging from 0% to 80%. As a part of a larger study to undercover the genomic and transcriptomic factors contributing to this variable efficacy, here we present a deep learning approach to identify mice which have been BCG-vaccinated from those that have not been vaccinated from hematoxylin and eosin stained lung sections of experimentally infected inbred mice. In a leave-one-out cross-validation of 59 slides, our method not only demonstrates ability to identify vaccinated mice with 93% accuracy and non-vaccinated mice with 100% accuracy. Through association with genomic and transcriptomic factors, we envision creating a blueprint for modifying and improving current vaccine strategies.
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