Open Access
Issue
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01009
Number of page(s) 9
DOI https://doi.org/10.1051/itmconf/20224301009
Published online 14 March 2022
  1. alzheimers-facts-and-figures.pdf [Internet]. [cité 4 sept 2021]. Disponible sur: https://www.alz.org/media/documents/alzheimers-facts-and-figures.pdf [Google Scholar]
  2. Laske C, Sohrabi HR, Frost SM, López-de-Ipiña K, Garrard P, Buscema M, Dauwels J, Soekadar SR, Mueller S, Linnemann C, Bridenbaugh SA, Kanagasingam Y, Martins RN, O’Bryant SE. Innovative diagnostic tools for early detection of Alzheimer’s disease. Alzheimer’s & Dementia. mai 2015;11(5):561-78 [CrossRef] [Google Scholar]
  3. Machine Learning [Internet]. DeepAI. 2019 [cité 4 sept 2021]. Disponible sur: https://deepai.org/machine-learning-glossary-and-terms/machine-learning. [Google Scholar]
  4. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 29 mars 2021;372:n71 [CrossRef] [Google Scholar]
  5. Fuchs KL, Hannay HJ, Huckeba WM, Andrews Espy K. Construct Validity of the Continuous Recognition Memory Test. The Clinical Neuropsychologist. févr 1999;13(1):54-65 [CrossRef] [Google Scholar]
  6. Tian J, Smith G, Guo H, Liu B, Pan Z, Wang Z, Xiong S, Fang R. Modular machine learning for Alzheimer’s disease classification from retinal vasculature. Sci Rep. 8 janv 2021;11:238. [CrossRef] [Google Scholar]
  7. Zee B, Wong Y, Lee J, Fan Y, Zeng J, Lam B, Wong A, Shi L, Lee A, Kwok C, Lai M, Mok V, Lau A. Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images. Brain Communications [Internet]. 1 juill 2021 [cité 6 sept 2021];3(3). Disponible sur:https://doi.org/10.1093/braincomms/fcab124 [Google Scholar]
  8. Nunes A, Silva G, Duque C, Januário C, Santana I, Ambrósio AF, Castelo-Branco M, Bernardes R. Retinal texture biomarkers may help to discriminate between Alzheimer’s, Parkinson’s, and healthy controls. PLoS One. 21 juin 2019;14(6):e0218826. [CrossRef] [Google Scholar]
  9. Qiu Y, Jin T, Mason E, Campbell MCW. Predicting Thioflavin Fluorescence of Retinal Amyloid Deposits Associated With Alzheimer’s Disease from Their Polarimetric Properties. Transl Vis Sci Technol. 14 août 2020;9(2):47. [CrossRef] [Google Scholar]
  10. Lemmens S, Van Craenendonck T, Van Eijgen J, De Groef L, Bruffaerts R, de Jesus DA, Charle W, Jayapala M, Sunaric-Mégevand G, Standaert A, Theunis J, Van Keer K, Vandenbulcke M, Moons L, Vandenberghe R, De Boever P, Stalmans I. Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer’s disease patients. Alzheimer’s Research & Therapy. 10 nov 2020;12(1):144. [CrossRef] [Google Scholar]
  11. Pereira MLG de F, Camargo M von Z de A, Bellan AFR, Tahira AC, dos Santos B, dos Santos J, Machado-Lima A, Nunes FLS, Forlenza OV. Visual Search Efficiency in Mild Cognitive Impairment and Alzheimer’s Disease: An Eye Movement Study. Douglass A, éditeur. JAD. 5 mai 2020;75(1):261-75. [Google Scholar]
  12. Jiang J, Yan Z, Sheng C, Wang M, Guan Q, Yu Z, Han Y, Jiang J. A Novel Detection Tool for Mild Cognitive Impairment Patients Based on Eye Movement and Electroencephalogram. JAD. 12 nov 2019;72(2):389-99. [CrossRef] [Google Scholar]
  13. Haque RU, Pongos AL, Manzanares CM, Lah JJ, Levey AI, Clifford GD. Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment. IEEE Trans Biomed Eng. janv 2021;68(1):11-8. [CrossRef] [Google Scholar]
  14. Artificial Intelligence in Medicine: 19th International Conference on ... - Allan Tucker - Google Livres [Internet]. [cité 6 sept 2021]. Disponible sur: https://books.google.co.ma/books?id=PDYyEAAAQBAJ0026pg=PA1680026lpg=PA1680026dq=Detecting+Mild+Cognitive+Impairment+Using+Smooth+Pursuit+and+a+Modified+Corsi+Task0026source=bl0026ots=3 3GftLl6uv0026sig=ACfU3U1W-QsePgjnAJfCtEipgHhQht6Ngg0026hl=fr0026sa=X0026ved=2ahUKEwiMqKGllevyAhUQkhQKHWphACoQ6AF6BAgbEAM#v=onepage0026q=Detecting%20Mild%20Cognitive%20Impairment%20Using%20Sm ooth%20Pursuit%20and%20a%20Modified%20Co rsi%20Task…f=false [Google Scholar]
  15. Pavisic IM, Firth NC, Parsons S, Rego DM, Shakespeare TJ, Yong KXX, Slattery CF, Paterson RW, Foulkes AJM, Macpherson K, Carton AM, Alexander DC, Shawe-Taylor J, Fox NC, Schott JM, Crutch SJ, Primativo S. Eyetracking Metrics in Young Onset Alzheimer’s Disease: A Window into Cognitive Visual Functions. Front Neurol. 7 août 2017;8:377. [CrossRef] [Google Scholar]
  16. Hernandez F, Vega R, Tapia F, Morocho D, Fuertes W. Early detection of Alzheimer’s using digital image processing through iridology, an alternative method. In: 2018 13th Iberian Conference on Information Systems and Technologies (CISTI) [Internet]. Caceres: IEEE; 2018 [cité 6 sept 2021]. p. 1-7. Disponible sur:https://ieeexplore.ieee.org/document/8399151/ [Google Scholar]
  17. Hernández F, Vega R, Tapia F, Morocho D, Fuertes W. Early Detection of Alzheimer’s Using Digital Image Processing Through Iridology, An Alternative Method. 2019;4(3):12. [MathSciNet] [Google Scholar]
  18. Salahuddin T, Al-Maadeed SA, Petropoulos IN, Malik RA, Ilyas SK, Qidwai U. Smart Neuropathy Detection using Machine Intelligence: Filling the Void Between Clinical Practice and Early Diagnosis. In: 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4) [Internet]. London, United Kingdom: IEEE; 2019 [cité 6 sept 2021]. p. 141-6. Disponible sur: https://ieeexplore.ieee.org/document/8904015/ [CrossRef] [Google Scholar]
  19. Lim JKH, Li Q-X, He Z, Vingrys AJ, Wong VHY, Currier N, Mullen J, Bui BV, Nguyen CTO. The Eye As a Biomarker for Alzheimer’s Disease. Front Neurosci. 17 nov 2016;10:536. [Google Scholar]
  20. Vermunt L, Sikkes SAM, van den Hout A, Handels R, Bos I, van der Flier WM, Kern S, Ousset P-J, Maruff P, Skoog I, Verhey FR, Freund-Levi Y, Tsolaki M, Wallin ÅK, Rikkert MO, Soininen H, Spiru L, Zetterberg H, Blennow K, Scheltens P, Muniz-Terrera G, Visser PJ. Duration of Preclinical, Prodromal and Dementia Alzheimer Disease Stages in Relation to Age, Sex, and APOE genotype. Alzheimers Dement. juill 2019;15(7):888-98. [CrossRef] [Google Scholar]

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