{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:26:50Z","timestamp":1743100010662,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031077036"},{"type":"electronic","value":"9783031077043"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-07704-3_32","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T13:13:06Z","timestamp":1654607586000},"page":"395-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning Framework for the Prediction of Conversion to Alzheimer Disease"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6275-3214","authenticated-orcid":false,"given":"Sofia","family":"Ostellino","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3433-7739","authenticated-orcid":false,"given":"Alfredo","family":"Benso","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5268-9899","authenticated-orcid":false,"given":"Gianfranco","family":"Politano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","unstructured":"Murphy, M.P., LeVine III, H.: Alzheimer\u2019s disease and the Amyloid-$$\\beta $$ Peptide. J. Alzheimer\u2019s Dis. 19(1), 311\u2013323 (2010). content.iospress.comhttps:\/\/doi.org\/10.3233\/JAD-2010-1221","DOI":"10.3233\/JAD-2010-1221"},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/S1474-4422(11)70072-2","volume":"10","author":"DE Barnes","year":"2011","unstructured":"Barnes, D.E., Yaffe, K.: The projected effect of risk factor reduction on Alzheimer\u2019s disease prevalence. Lancet Neurol. 10, 819\u2013828 (2011). https:\/\/doi.org\/10.1016\/S1474-4422(11)70072-2","journal-title":"Lancet Neurol."},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"105242","DOI":"10.1016\/j.cmpb.2019.105242","volume":"187","author":"MA Ebrahimighahnavieh","year":"2020","unstructured":"Ebrahimighahnavieh, M.A., et al.: Deep learning to detect Alzheimer\u2019s disease from neuroimaging: a systematic literature review. Comput. Methods Programs Biomed. 187, 105242 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2019.105242","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"32_CR4","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","volume":"74","author":"RC Petersen","year":"2020","unstructured":"Petersen, R.C., et al.: Alzheimer\u2019s disease neuroimaging initiative (ADNI). Neurology 74(3), 201\u2013209 (2020). https:\/\/doi.org\/10.1212\/WNL.0b013e3181cb3e25","journal-title":"Neurology"},{"key":"32_CR5","unstructured":"ADNI Homepage. https:\/\/adni.loni.usc.edu\/"},{"key":"32_CR6","unstructured":"Learn more about the ADNI Initiative. http:\/\/adni.loni.usc.edu\/about\/"},{"key":"32_CR7","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","volume":"189","author":"S Simeon","year":"2019","unstructured":"Simeon, S., et al.: A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease. NeuroImage 189, 276\u2013287 (2019). https:\/\/doi.org\/10.1016\/j.neuroimage.2019.01.031","journal-title":"NeuroImage"},{"key":"32_CR8","unstructured":"Sarraf, S., Tofighi, G.: Classification of Alzheimer\u2019s disease using fMRI data and deep learning convolutional neural networks. arXiv:1603.08631 [cs], March 2016"},{"key":"32_CR9","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.neucom.2020.01.053","volume":"388","author":"W Li","year":"2020","unstructured":"Li, W., et al.: Detecting Alzheimer\u2019s disease based on 4D fMRI: an exploration under deep learning framework. Neurocomputing 388, 280\u2013287 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.01.053","journal-title":"Neurocomputing"},{"key":"32_CR10","doi-asserted-by":"publisher","unstructured":"Parmar, H., et al.: Spatiotemporal feature extraction and classification of Alzheimer\u2019s disease using deep learning 3D-CNN for fMRI data. J. Med. Imaging 7(5), 056001 (2020). https:\/\/www.spiedigitallibrary.org, https:\/\/doi.org\/10.1117\/1.JMI.7.5.056001","DOI":"10.1117\/1.JMI.7.5.056001"},{"issue":"3","key":"32_CR11","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1002\/jmri.25947","volume":"48","author":"G Giulietti","year":"2018","unstructured":"Giulietti, G., et al.: Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and Alzheimer\u2019s disease patients: DTI histograms for staging AD. J. Magn. Reson. Imaging 48(3), 767\u2013769 (2018). https:\/\/doi.org\/10.1002\/jmri.25947","journal-title":"J. Magn. Reson. Imaging"},{"issue":"4","key":"32_CR12","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1007\/s11682-020-00366-8","volume":"15","author":"C Platero","year":"2021","unstructured":"Platero, C., et al.: Predicting Alzheimer\u2019s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers. Brain Imaging Behav. 15(4), 1728\u20131738 (2021). https:\/\/doi.org\/10.1007\/s11682-020-00366-8","journal-title":"Brain Imaging Behav."},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Lin, W., et al.: Predicting Alzheimer\u2019s disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data. Front. Aging Neurosci. 12, 77 (2020). https:\/\/www.frontiersin.org\/article\/10.3389\/fnagi.2020.00077","DOI":"10.3389\/fnagi.2020.00077"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics and Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-07704-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T23:08:29Z","timestamp":1655680109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-07704-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031077036","9783031077043"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-07704-3_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWBBIO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Bioinformatics and Biomedical Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwbbio2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwbbio.ugr.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"212","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}