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Abstract #2830

Age prediction from time-of-flight MR angiography using deep learning: Comparison of the predicted age among normal, MCI and AD.

Hunjin Chung1, Yoonho Nam2, KoungMi Kang3, Chul-Ho Sohn3, KangHyun Ryu1, Jinhee Jang2, and Dong-Hyun Kim1

1Yonsei University, Seoul, Korea, Republic of, 2Seoul St. Mary’s Hospital, Seoul, Korea, Republic of, 3Seoul National University Hospital, Seoul, Korea, Republic of

Time-of-Flight (TOF) MR angiography (MRA) provides meaningful vascular information related to aging. Recently, we have developed a deep learning based chronological age prediction model from 3D TOF data and demonstrated its accuracy in predicting the age of normal volunteers. In this study, to investigate its clinical utilities, we applied the deep learning model to subjects with mild cognitive impairment and Alzheimer’s disease.

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