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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.