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Automatic classification of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) who will convert to AD using deep neural networks
Federica Agosta1, Silvia Basaia1, Luca Wagner2, Giuseppe Magnani3, and Massimo Filippi1,3

1Neuroimaging Research Unit, INSPE, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, 2Effeventi, Milan, Italy, 3Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy

Synopsis

We built and validated a deep learning 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. The deep neural network (DNN) procedure discriminated AD and heathy controls with an accuracy up to 98%, and MCI converters and MCI stable with an accuracy up to 75%. DNNs provide a powerful tool for the automatic classification of AD and MCI prognosis.

Introduction

The utility of structural MRI in the clinical assessment of patients with suspected Alzheimer’s disease (AD) will be increased by development of robust algorithms for automated assessment. Deep learning (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. The aim of this study was to build and validate a DL algorithm that predicts the individual diagnosis of AD and the development of AD in mild cognitive impairment (MCI) patients based on a single cross-sectional brain structural MRI scan.

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 have been optimized and were able to classify with high accuracy HC, AD, MCI converters, MCI stable. In the reference ADNI dataset and in a second dataset including also non-ADNI images, the DNN procedure was able to discriminate 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%.

Discussion

DNN has several advantages including: ability to process large amount of data, ability to deal with heterogeneous data, computationally inexpensive (after training) and reduction of diagnosis time. DNNs are also ideal for real-time applications where immediate output is desirable.

Conclusions

DNNs provide a powerful tool for the automatic classification of AD patients and MCI prognosis. Future studies are warranted to test the accuracy of the procedure in the differential diagnosis between AD and other neurodegenerative diseases.

Acknowledgements

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).

References

No reference found.
Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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