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In Journal of pathology informatics ; h5-index 23.0

Assessment of the estrous cycle of mature female mammals is an important component of verifying the efficacy and safety of drug candidates. The common pathological approach of relying on expert observation has several drawbacks, including laborious work and inter-viewer variability. The recent advent of image recognition technologies using deep learning is expected to bring substantial benefits to such pathological assessments. We herein propose 2 distinct deep learning-based workflows to classify the estrous cycle stage from tissue images of the uterine horn and vagina, respectively. These constructed models were able to classify the estrous cycle stages with accuracy comparable with that of expert pathologists. Our digital workflows allow efficient pathological assessments of the estrous cycle stage in rats and are thus expected to accelerate drug research and development.

Onishi Shinichi, Egami Riku, Nakamura Yuya, Nagashima Yoshinobu, Nishihara Kaori, Matsuo Saori, Murai Atsuko, Hayashi Shuji, Uesumi Yoshifumi, Kato Atsuhiko, Tsunoda Hiroyuki, Yamazaki Masaki, Mizuno Hideaki


Deep learning, Digital workflow, Estrous cycle, Image recognition, Pathological assessment