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Abstract #3519

Machine Learning-based Features of DKI to Evaluate and Automate Alzheimer’s Disease and amnestic mild cognitive impairment Diagnoses

Yu Zhang1, Tongtong Li1, Xiuwei Fu2, Xianchang Zhang3, Yuan Luo4, and Hongyan Ni5
1First Central Clinical College, Tianjin Medical University, Tianjin, China, 2Tianjin Medical University General Hospital, Tianjin, China, 3MR Collaboration, Siemens Healthcare Ltd., Beijing, China, 4Department of Radiology, China-Japan Friendship Hospital, Beijing, China, 5Department of Radiology, Tianjin First Central Hospital, Tianjin, China

The early diagnoses of Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) are crucial. This study aimed to acquire new imaging markers to assess the severity of AD and provide early diagnoses using machine learning algorithm. Diffusion kurtosis imaging (DKI) parameters were acquired on 58 AD patients, 64 aMCI patients, and 60 healthy volunteers. It’s found that radial diffusivity value of right uncinate fasciculus was the most important feature for assessing severity. The random forest classifier showed the highest diagnostic efficacy for AD. The RF classifier can provide an early diagnosis of disease based on the quantitative DKI features.

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