Automatic classification of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) who will convert to AD using deep neural networks (P3.179)

Objective: To build and validate a deep learning (DL) algorithm that predicts the individual diagnosis of Alzheimer’s disease (AD) and the development of AD in mild cognitive impairment (MCI) patients based on a single cross-sectional brain structural MRI scan. Background: The utility of structural MRI in the clinical assessment of patients with suspected AD will be increased by development of robust algorithms for automated assessment. DL algorithms differ from conventional machine learning methods by the fact that they require little or no image pre-processing and can automatically infer an optimal representation of the data from the raw images without requiring prior feature selection, resulting in a more objective and less bias-prone process. Design/Methods: 3D T1-weighted images from ADNI (352 healthy controls [HC], 294 AD, 253 MCI converters, 510 MCI stable) and subjects recruited at our Institute (non-ADNI dataset: 55 HC, 124 AD, 27 MCI converters, 23 MCI stable) were used. Deep neural networks (DNNs), which are mathematical representations of the human neural architecture with multiple hidden layers of artificial neurons, were applied. The whole dataset was randomly divided into a training/validation set (90%) and a testing set (10%). DNN performance was improved by adding to the original dataset synthetic images created using data augmentation algorithms, as well as transfer learning to subsequent comparisons. Results: DNNs with different architectures and parameters were optimized. In the reference ADNI dataset and in a second dataset including also non-ADNI images, DNNs discriminated AD and HC with an accuracy up to 98%, with no difference between ADNI and non-ADNI images. In the whole dataset, DNN was also able to discriminate: HC vs (AD + all MCI) with an accuracy up to 86%, and MCI converters vs MCI stable with an accuracy up to 75%. Conclusions: DNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Study Supported by: The study was supported by the Italian Ministry of Health (GR-2011-02351217). Data collection and sharing was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). Disclosure: Dr. Basaia has nothing to disclose. Dr. Agosta has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with EXCEMED— Excellence in Medical Education. Dr. Wagner has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Effeventi s.r. l. Dr. Magnani has nothing to disclose. Dr. Filippi has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biogen Idec, Merk-Serono, Novartis, Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in an editorial capacity for Journal of Neurology. Dr. Filippi has received research support from Biogen Idec, Novartis, Teva Pharmaceutical Industries.