Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data
By Taeho C. Jo, Kwangsik Nho, Andrew J. Saykin. Frontiers in Aging Neuroscience, 2019
618 citations recorded by the source corpus.
Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as—omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.
Subfields: Artificial Intelligence, Neurology, Psychiatry and Mental health
View Paper: https://www.semanticscholar.org/paper/1f93588bb075eed40ffdfae2f7907c946e5974d9
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Citations in the corpus
- Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, methodology): “Kanegae et al. (2020) used machine learning techniques to validate the prediction of risk for newonset hypertension. They used data in a split form for the model construction and development and validation to test its performance. The models they used were XGBoost and ensemble, in which the XGBoost ”
- Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond (2021, methodology): “Deep learningmethods have been recently applied onmedical domains, especially onmedical imaging analyses [108], such as the classification of Alzheimer’s [83], lung cancer detection [75], tuberculosis diagnosis [136], retinal disease detection [146], etc.”