{"doi":"10.3389/fnagi.2019.00220","title":"Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data","abstract":null,"journal":"Frontiers in Aging Neuroscience","year":2019,"id":617672,"datarank":11.861861397227955,"base_score":6.568077911411976,"endowment":6.568077911411976,"self_citation_contribution":0.9852116867117965,"citation_network_contribution":10.876649710516158,"self_endowment_contribution":0.9852116867117965,"citer_contribution":10.876649710516158,"corpus_percentile":null,"corpus_rank":null,"citation_count":711,"citer_count":200,"citers_with_citation_signal":200,"citers_with_endowment":200,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":53300,"name":"Kwangsik Nho","orcid":"0000-0002-7624-3872","position":1,"is_corresponding":false},{"id":27595,"name":"Andrew J. Saykin","orcid":"0000-0002-1376-8532","position":2,"is_corresponding":false},{"id":66399,"name":"Taeho Jo","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data","abstract":"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.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31481890","pmcid":"PMC6710444","openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"P30 AG10133","title":null},{"funder_name":"National Institutes of Health","grant_id":"R01 AG19771","title":null},{"funder_name":"National Institutes of Health","grant_id":"R01 AG057739","title":null},{"funder_name":"National Institutes of Health","grant_id":"R01 CA129769","title":null},{"funder_name":"U.S. National Library of Medicine","grant_id":"R01 LM012535","title":null},{"funder_name":"National Institute on Aging","grant_id":"R03 AG054936","title":null},{"funder_name":"National Institutes of Health","grant_id":"1U01AG024904-01","title":"Alzheimers Disease Neuroimaging Initiative"},{"funder_name":"National Institutes of Health","grant_id":"5R03AG054936-02","title":"Neurogenesis in Adult Brain: Gene Networks and Alzheimer’s Disease"},{"funder_name":"National Institutes of Health","grant_id":"2R01AG019771-06","title":"Memory Circuitry in MCI and Early Alzheimers Disease"},{"funder_name":"National Institutes of Health","grant_id":"5R01CA129769-10","title":"Older Breast Cancer Patients: Risk For Cognitive Decline"},{"funder_name":"National Institutes of Health","grant_id":"5P30AG010133-08","title":"CORE-- EDUCATION AND INFORMATION TRANSFER"},{"funder_name":"National Institutes of Health","grant_id":"5R01AG057739-04","title":"Leveraging Neuroimaging Biomarkers to Understand the Role of Social Networks in Alzheimer's Disease"},{"funder_name":"National Institutes of Health","grant_id":"1R01LM012535-01","title":"Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease"}],"total_grants":13,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.3389/fnagi.2019.00220","host_type":"publisher"},{"url":"https://www.frontiersin.org/article/10.3389/fnagi.2019.00220/full","host_type":"publisher"},{"url":"https://arxiv.org/pdf/1905.00931","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6710444","host_type":"repository"},{"url":"https://hdl.handle.net/1805/21678","host_type":"repository"},{"url":"https://doaj.org/article/5f26ad38449340c78a369db479e14806","host_type":"repository"},{"url":"http://hdl.handle.net/1805/21678","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC6710444","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC6710444?pdf=render","host_type":"Europe_PMC"},{"url":"https://www.frontiersin.org/articles/10.3389/fnagi.2019.00220/pdf","host_type":""},{"url":"https://dx.doi.org/10.48550/arxiv.1905.00931","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/31481890","host_type":""},{"url":"http://dx.doi.org/10.3389/fnagi.2019.00220","host_type":""},{"url":"http://arxiv.org/abs/1905.00931","host_type":""},{"url":"https://dx.doi.org/10.3389/fnagi.2019.00220","host_type":""}],"fields_of_study":["03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Artificial intelligence","Classification","Alzheimer's disease","Positron emission tomography","Magnetic Resonance Imaging","Machine Learning","Neuroimaging","Deep Learning","FOS: Computer and information sciences","Computer Science - Machine Learning","Neurosciences. Biological psychiatry. Neuropsychiatry","Machine Learning (stat.ML)","Machine Learning (cs.LG)","Statistics - Machine Learning","FOS: Electrical engineering, electronic engineering, information engineering","Image and Video Processing (eess.IV)","Electrical Engineering and Systems Science - Image and Video Processing","RC321-571","Neuroscience"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. Good health"},{"sdg_number":10,"sdg_label":"10. 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