Abstract #1737

Predicting brain age using partition modeling strategy and Atlas-based attentional enhancement in Chinese population

Yang Yang¹, Bingsheng Huang², Yingqian Chen³, Yingtong Wu², Chuxuan Lin², Zhiyun Yang³, and Xia Liu⁴

¹Department of Radiology, Suining Central Hospital, Suining, China, ²Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China, ³Department of Radiology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, ⁴Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China

Synopsis

Keywords: Neurodegeneration, Neurodegeneration, Brain age, Partition modelingThis study aimed to develop and construct a MRI-based full-age-range brain age prediction model that can be applied in the Chinese health care system. We proposed a brain age prediction method based on transfer learning and partition modeling , which was using Atlas attention enhancement. The performance of models with different image masks were compared and the model constructed based on top60% image mask achieved the best prediction performance. The brain age prediction method proposed in this study can provide objective brain age for assessing brain health status in Chinese population.

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