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AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers – A narrative review of a growing field

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Abstract

Objectives

The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management.

Methods

We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline.

Results

Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations.

Conclusion

AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.

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References

  1. Lane CA, Hardy J, Schott JM (2018) Alzheimer’s disease. Eur J Neurol 25(1):59–70. https://doi.org/10.1111/ene.13439

    Article  CAS  PubMed  Google Scholar 

  2. Nichols E, Szoeke CEI, Vollset SE et al (2019) Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18(1):88–106. https://doi.org/10.1016/S1474-4422(18)30403-4

    Article  Google Scholar 

  3. Dubois B, Padovani A, Scheltens P, Rossi A, Dell’Agnello G (2016) Timely diagnosis for Alzheimer’s disease: a literature review on benefits and challenges. J Alzheimers Dis 49(3):617–631. https://doi.org/10.3233/JAD-150692

    Article  PubMed  Google Scholar 

  4. Scheltens P, De Strooper B, Kivipelto M et al (2021) Alzheimer’s disease. Lancet 397(10284):1577–1590. https://doi.org/10.1016/S0140-6736(20)32205-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Jack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14(4):535–562. https://doi.org/10.1016/j.jalz.2018.02.018

    Article  PubMed  Google Scholar 

  6. Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 155:530–548. https://doi.org/10.1016/j.neuroimage.2017.03.057

    Article  PubMed  Google Scholar 

  7. Wen J, Thibeau-Sutre E, Diaz-Melo M et al (2020) Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med Image Anal 63:101694. https://doi.org/10.1016/j.media.2020.101694

    Article  PubMed  Google Scholar 

  8. Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220. https://doi.org/10.3389/fnagi.2019.00220

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tanveer M, Richhariya B, Khan RU et al (2020) Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Trans Multim Comput Commun Appl 16(1s):1–35. https://doi.org/10.1145/3344998

    Article  Google Scholar 

  10. Ansart M, Epelbaum S, Bassignana G et al (2021) Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review. Med Image Anal 67:101848. https://doi.org/10.1016/j.media.2020.101848

    Article  PubMed  Google Scholar 

  11. Pini L, Pievani M, Bocchetta M et al (2016) Brain atrophy in Alzheimer’s Disease and aging. Ageing Res Rev 30:25–48. https://doi.org/10.1016/j.arr.2016.01.002

    Article  PubMed  Google Scholar 

  12. de Flores R, Mutlu J, Bejanin A et al (2017) Intrinsic connectivity of hippocampal subfields in normal elderly and mild cognitive impairment patients. Hum Brain Mapp 38(10):4922–4932. https://doi.org/10.1002/hbm.23704

    Article  PubMed  PubMed Central  Google Scholar 

  13. Dickerson BC, Wolk DA (2013) Alzheimer's Disease Neuroimaging Initiative. Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front Aging Neurosci. 5:55. https://doi.org/10.3389/fnagi.2013.00055

  14. Pettigrew C, Soldan A, Zhu Y et al (2016) Cortical thickness in relation to clinical symptom onset in preclinical AD. Neuroimage Clin 12:116–122. https://doi.org/10.1016/j.nicl.2016.06.010

    Article  PubMed  PubMed Central  Google Scholar 

  15. Badhwar A, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P (2017) Resting-state network dysfunction in Alzheimer’s disease: A systematic review and meta-analysis. Alzheimers Dement (Amst) 8:73–85. https://doi.org/10.1016/j.dadm.2017.03.007

    Article  PubMed  Google Scholar 

  16. Eyler LT, Elman JA, Hatton SN et al (2019) Resting state abnormalities of the default mode network in mild cognitive impairment: A systematic review and meta-analysis. J Alzheimers Dis 70(1):107–120. https://doi.org/10.3233/JAD-180847

    Article  PubMed  PubMed Central  Google Scholar 

  17. Papma JM, Smits M, de Groot M et al (2017) The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment. Eur Radiol 27(9):3716–3724. https://doi.org/10.1007/s00330-017-4768-1

    Article  PubMed  PubMed Central  Google Scholar 

  18. Joo SH, Lim HK, Lee CU (2016) Three large-scale functional brain networks from resting-state functional MRI in subjects with different levels of cognitive impairment. Psychiatry Investig 13(1):1–7. https://doi.org/10.4306/pi.2016.13.1.1

    Article  PubMed  Google Scholar 

  19. Sperling RA, Bates JF, Chua EF, Cocchiarella AJ, Rentz DM, Rosen BR, Schacter DL, Albert MS (2003) fMRI studies of associative encoding in young and elderly controls and mild Alzheimer’s disease. J Neurol Neurosurg Psychiatry 74(1):44–50

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Remy F, Mirrashed F, Campbell B, Richter W (2005) Verbal episodic memory impairment in Alzheimer’s disease: a combined structural and functional MRI study. Neuroimage 25(1):253–266. https://doi.org/10.1016/j.neuroimage.2004.10.045

    Article  PubMed  Google Scholar 

  21. Yetkin FZ, Rosenberg RN, Weiner MF, Purdy PD, Cullum CM (2006) FMRI of working memory in patients with mild cognitive impairment and probable Alzheimer’s disease. Eur Radiol 16(1):193–206. https://doi.org/10.1007/s00330-005-2794-x

    Article  PubMed  Google Scholar 

  22. Rice L, Bisdas S (2017) The diagnostic value of FDG and amyloid PET in Alzheimer’s disease-A systematic review. Eur J Radiol 94:16–24. https://doi.org/10.1016/j.ejrad.2017.07.014

    Article  PubMed  Google Scholar 

  23. Ou YN, Xu W, Li JQ et al (2019) FDG-PET as an independent biomarker for Alzheimer’s biological diagnosis: a longitudinal study. Alzheimers Res Ther 11(1):57. https://doi.org/10.1186/s13195-019-0512-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Palmqvist S, Schöll M, Strandberg O et al (2017) Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun 8(1):1214. https://doi.org/10.1038/s41467-017-01150-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hanseeuw BJ, Betensky RA, Jacobs HIL et al (2019) Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study. JAMA Neurol 76(8):915–924. https://doi.org/10.1001/jamaneurol.2019.1424

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ossenkoppele R, Schonhaut DR, Schöll M et al (2016) Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain 139(Pt 5):1551–1567. https://doi.org/10.1093/brain/aww027

    Article  PubMed  PubMed Central  Google Scholar 

  27. Pontecorvo MJ, Devous MD Sr, Kennedy I et al (2019) A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer’s disease dementia. Brain 142(6):1723–1735. https://doi.org/10.1093/brain/awz090

    Article  PubMed  PubMed Central  Google Scholar 

  28. Morris E, Chalkidou A, Hammers A, Peacock J, Summers J, Keevil S (2016) Diagnostic accuracy of (18)F amyloid PET tracers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 43(2):374–385. https://doi.org/10.1007/s00259-015-3228-x

    Article  CAS  PubMed  Google Scholar 

  29. Leuzy A, Chiotis K, Lemoine L et al (2019) Tau PET imaging in neurodegenerative tauopathies-still a challenge. Mol Psychiatry 24(8):1112–1134. https://doi.org/10.1038/s41380-018-0342-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Samper-González J, Burgos N, Bottani S et al (2018) Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. Neuroimage 183:504–521. https://doi.org/10.1016/j.neuroimage.2018.08.042

    Article  PubMed  Google Scholar 

  31. Nettiksimmons J, DeCarli C, Landau S, Beckett L (2014) Biological heterogeneity in ADNI amnestic mild cognitive impairment. Alzheimers Dement 10(5):511-521.e1. https://doi.org/10.1016/j.jalz.2013.09.003

    Article  PubMed  Google Scholar 

  32. Gamberger D, Lavrač N, Srivatsa S, Tanzi RE, Doraiswamy PM (2017) Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease. Sci Rep 7(1):6763. https://doi.org/10.1038/s41598-017-06624-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sagi O, Rokach L (2018) Ensemble learning: A survey. WIREs Data Min Knowl Discovery 8(4):e1249. https://doi.org/10.1002/wid

    Article  Google Scholar 

  34. Basaia S, Agosta F, Wagner L et al (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 21:101645. https://doi.org/10.1016/j.nicl.2018.101645

    Article  PubMed  Google Scholar 

  35. Ding Y, Sohn JH, Kawczynski MG et al (2019) A deep learning model to predict a diagnosis of Alzheimer Disease by using 18F-FDG PET of the brain. Radiology 290(2):456–464. https://doi.org/10.1148/radiol.2018180958

    Article  PubMed  Google Scholar 

  36. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision 618--626. https://doi.org/10.1109/ICCV.2017.74.

  37. Ji J (2019) Gradient-based interpretation on convolutional neural network for classification of pathological images. In: Proceeding of the International Conference on Information Technology and Computer Application, ITCA 83--86. https://doi.org/10.1109/ITCA49981.2019.00026

  38. Kowsari K, Sali R, Ehsan L et al (2020) HMIC: hierarchical medical image classification, a deep learning approach. Information 11(6):318. https://doi.org/10.3390/INFO11060318

    Article  PubMed  Google Scholar 

  39. Windisch P, Weber P, Fürweger C, et al. Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology. Published online June 5, 2020:1–11. https://doi.org/10.1007/s00234-020-02465-1

  40. Böhle M, Eitel F, Weygandt M, Ritter K (2019) Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer’s disease classification. Front Aging Neurosci 10:194. https://doi.org/10.3389/fnagi.2019.00194

    Article  Google Scholar 

  41. Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. arXiv:14126806 [cs]. Published online December 21, 2014. Accessed March 16, 2024. http://arxiv.org/abs/1412.6806

  42. Ghazi MM, Nielsen M, Pai A et al (2019) Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling. Med Image Anal 53:39–46. https://doi.org/10.1016/j.media.2019.01.004

    Article  Google Scholar 

  43. Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM; Alzheimer's Disease Neuroimaging Initiative. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLoS Comput Biol. 2018;14(9):e1006376. https://doi.org/10.1371/journal.pcbi.1006376

  44. Suk HI, Lee SW, Shen D (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:569–582. https://doi.org/10.1016/j.neuroimage.2014.06.077

    Article  PubMed  Google Scholar 

  45. Ju R, Hu C, Zhou P, Li Q (2019) Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinform 16(1):244–257. https://doi.org/10.1109/TCBB.2017.2776910

    Article  PubMed  Google Scholar 

  46. Liu M, Zhang J, Adeli E, Shen D (2018) Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43:157–168. https://doi.org/10.1016/j.media.2017.10.005

    Article  PubMed  Google Scholar 

  47. Shi J, Zheng X, Li Y, Zhang Q, Ying S (2018) Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer’s Disease. IEEE J Biomed Health Inform 22(1):173–183. https://doi.org/10.1109/JBHI.2017.2655720

    Article  PubMed  Google Scholar 

  48. Plis SM, Hjelm DR, Salakhutdinov R et al (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229. https://doi.org/10.3389/fnins.2014.00229

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer’s Disease Based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):85. https://doi.org/10.1007/s10916-018-0932-7

    Article  PubMed  Google Scholar 

  50. Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X (2020) Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci 14:259. https://doi.org/10.3389/fnins.2020.00259

    Article  PubMed  PubMed Central  Google Scholar 

  51. Woo CW, Chang LJ, Lindquist MA, Wager TD (2017) Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 20(3):365–377. https://doi.org/10.1038/nn.4478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Calhoun VD, Sui J (2016) Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. Biol Psych Cognit Neurosci Neuroimag 1(3):230–244. https://doi.org/10.1016/j.bpsc.2015.12.005

    Article  Google Scholar 

  53. Bouts MJRJ, van der Grond J, Vernooij MW, Koini M, Schouten TM, de Vos F ... Rombouts SARB (2021) Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. Human Brain Mapping, 42(9), 2819–2831. https://doi.org/10.1002/hbm.25388

  54. Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R ... Feng D (2015) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans Biomed Eng, 62(4), 1132–1140. https://doi.org/10.1109/TBME.2014.2372011

  55. Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3):856–867. https://doi.org/10.1016/j.neuroimage.2011.01.008

    Article  PubMed  Google Scholar 

  56. Paquerault S, Allard M, Grigis A et al (2018) Combining multiple imaging and non-imaging biomarkers to improve early prediction of Alzheimer’s disease. Alzheimers Dement 14(7):P293–P294. https://doi.org/10.1016/j.jalz.2018.06.070

    Article  Google Scholar 

  57. Teipel SJ, Kurth J, Krause B, Grothe MJ (2015) The relative importance of imaging markers for the prediction of Alzheimer’s disease dementia in mild cognitive impairment - Beyond classical regression. Neuroimage Clin 8:583–593. https://doi.org/10.1016/j.nicl.2015.05.006

    Article  PubMed  PubMed Central  Google Scholar 

  58. Lei B, Jiang F, Chen S, Ni D, Wang T (2017) Longitudinal analysis for disease progression via simultaneous multi-relational temporal-fused learning. Front Aging Neurosci 9:6. https://doi.org/10.3389/fnagi.2017.00006

    Article  PubMed  PubMed Central  Google Scholar 

  59. Singanamalli A, Wang H, Madabhushi A, Initiative ADN (2017) A supervised graph-based approach for the early diagnosis of Alzheimer’s disease using resting-state fMRI data. J Alzheimers Dis 56(4):1263–1280. https://doi.org/10.3233/JAD-160927

    Article  CAS  Google Scholar 

  60. Marinescu RV, Oxtoby NP, Young AL, et al. (2018) TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease. arXiv [preprint]. arXiv:1805.03909. https://doi.org/10.48550/arXiv.1805.03909

  61. Enders CK (2010) Applied missing data analysis. Guilford press

  62. Biering K, Hjollund NH, Frydenberg M (2015) Using multiple imputation to deal with missing data and attrition in longitudinal studies with repeated measures of patient-reported outcomes. Clin Epidemiol 7:91–106. https://doi.org/10.2147/CLEP.S72247

    Article  PubMed  PubMed Central  Google Scholar 

  63. Jakobsen JC, Gluud C, Wetterslev J, Winkel P (2017) When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol 17(1):162. https://doi.org/10.1186/s12874-017-0442-1

    Article  PubMed  PubMed Central  Google Scholar 

  64. Miao W, Tchetgen Tchetgen EJ, Geng Z (2018) Identification and Doubly Robust Estimation of Data Missing Not at Random With an Ancillary Variable. J Am Stat Assoc 113(524):1718–1734. https://doi.org/10.1080/01621459.2017.1381740

    Article  Google Scholar 

  65. Rahman MG, Islam MZ (2016) Missing value imputation using a fuzzy clustering-based EM approach. Knowl Inf Syst 46(2):389–422. https://doi.org/10.1007/s10115-015-0822-y

    Article  Google Scholar 

  66. Tang F, Ishwaran H (2017) Random forest missing data algorithms. Stat Anal Data Mining: The ASA Data Sci J 10(6):363–377. https://doi.org/10.1002/sam.11348

    Article  Google Scholar 

  67. Petersen RC, Aisen PS, Beckett LA et al (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74(3):201–209. https://doi.org/10.1212/WNL.0b013e3181cb3e25

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ellis KA, Bush AI, Darby D et al (2009) The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr 21(4):672–687. https://doi.org/10.1017/S1041610209009405

    Article  PubMed  Google Scholar 

  69. Wachinger C, Reuter M, Klein T (2018) DeepNAT: Deep convolutional neural network for segmenting neuroanatomy. Neuroimage 170:434–445. https://doi.org/10.1016/j.neuroimage.2017.02.035

    Article  PubMed  Google Scholar 

  70. Saint-Aubert L, Lemoine L, Chiotis K, Leuzy A, Rodriguez-Vieitez E, Nordberg A (2017) Tau PET imaging: present and future directions. Mol Neurodegener 12(1):19. https://doi.org/10.1186/s13024-017-0162-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Jack CR Jr, Bernstein MA, Fox NC et al (2008) The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27(4):685–691. https://doi.org/10.1002/jmri.21049

    Article  PubMed  PubMed Central  Google Scholar 

  72. Rieke N, Hancox J, Li W et al (2020) The future of digital health with federated learning. NPJ Digit Med 3(1):1–7. https://doi.org/10.1038/s41746-020-00323-1

    Article  Google Scholar 

  73. Arslan S, Ktena SI, Glocker B, Rueckert D. Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity. In: Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities. Springer, Cham; 2018:3–13. https://doi.org/10.1007/978-3-030-00689-1_1

  74. Montavon G, Samek W, Müller KR (2018) Methods for interpreting and understanding deep neural networks. Digit Signal Process 73:1–15. https://doi.org/10.1016/j.dsp.2017.10.011

    Article  Google Scholar 

  75. Davatzikos C (2019) Machine learning in neuroimaging: Progress and challenges. Neuroimage 197:652–656. https://doi.org/10.1016/j.neuroimage.2018.10.003

    Article  PubMed  Google Scholar 

  76. Neu SC, Pa J, Kukull W et al (2017) Apolipoprotein E genotype and sex risk factors for Alzheimer disease: a meta-analysis. JAMA Neurol 74(10):1178–1189. https://doi.org/10.1001/jamaneurol.2017.2188

    Article  PubMed  PubMed Central  Google Scholar 

  77. Young AL, Marinescu RV, Oxtoby NP et al (2018) Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun 9(1):4273. https://doi.org/10.1038/s41467-018-05892-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Kaplan A, Haenlein M (2019) Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz 62(1):15–25. https://doi.org/10.1016/j.bushor.2018.08.004

    Article  Google Scholar 

  79. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

    Article  CAS  PubMed  Google Scholar 

  80. U.S. Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). White Paper. 2019. https://www.fda.gov/media/122535/download

  81. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: Mapping the debate. Big Data Soc 3(2):2053951716679679. https://doi.org/10.1177/2053951716679679

    Article  Google Scholar 

  82. Char DS, Shah NH, Magnus D (2018) Implementing machine learning in health care—addressing ethical challenges. N Engl J Med 378(11):981–983. https://doi.org/10.1056/NEJMp1714229

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA

    Thorsten Rudroff

  2. Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA

    Thorsten Rudroff

  3. Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland

    Oona Rainio & Riku Klén

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Correspondence to Thorsten Rudroff.

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Rudroff, T., Rainio, O. & Klén, R. AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers – A narrative review of a growing field. Neurol Sci (2024). https://doi.org/10.1007/s10072-024-07649-8

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