Predicting conversion of mild cognitive impairments to alzheimer’s disease and exploring impact of neuroimaging

The Alzheimer’s Disease Neuroimaging Initiative

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    16 Цитирования (Scopus)

    Аннотация

    Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimers Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit on the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does have an effect on conversion prediction, however cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is ACC = 0.76 ± 0.01 and the area under the ROC curve – ROC AUC = 0.86 ± 0.01.

    Язык оригиналаАнглийский
    Название основной публикацииGraphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities - 2nd International Workshop, GRAIL 2018 and 1st International Workshop, Beyond MIC 2018 Held in Conjunction with MICCAI 2018, Proceedings
    РедакторыDanail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor
    ИздательSpringer Verlag
    Страницы83-91
    Число страниц9
    ISBN (печатное издание)9783030006884
    DOI
    СостояниеОпубликовано - 2018
    Событие2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Испания
    Продолжительность: 20 сент. 201820 сент. 2018

    Серия публикаций

    НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Том11044 LNCS
    ISSN (печатное издание)0302-9743
    ISSN (электронное издание)1611-3349

    Конференция

    Конференция2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
    Страна/TерриторияИспания
    ГородGranada
    Период20/09/1820/09/18

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