Abstract
Alzheimer’s disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.
Funding source: Scientific Research General Project of Beijing Municipal Education Committee
Award Identifier / Grant number: KM201810005033
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 81971683
Funding source: Natural Science Foundation of Beijing Municipality
Award Identifier / Grant number: L182010
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: This research was financially supported by grants from National Natural Science Foundation of China (81971683), Natural Science Foundation of Beijing Municipality (L182010), and the Scientific Research General Project of Beijing Municipal Education Committee (KM201810005033).
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
Abirami, R.N., Vincent, P.M.D.R., Srinivasan, K., Manic, K.S., and Chang, C.Y. (2022). Multimodal medical image fusion of positron emission tomography and magnetic resonance imaging using generative adversarial networks. Behav. Neurol. 2022: 6878783, https://doi.org/10.1155/2022/6878783.Search in Google Scholar PubMed PubMed Central
Agarwal, D., Marques, G., de la Torre-Díez, I., Franco Martin, M.A., García Zapiraín, B., and Martín Rodríguez, F. (2021). Transfer learning for Alzheimer’s disease through neuroimaging biomarkers: a systematic review. Sensors 21: 7259, https://doi.org/10.3390/s21217259.Search in Google Scholar PubMed PubMed Central
Aljuaid, A. and Anwar, M. (2022). Survey of supervised learning for medical image processing. Comput. Sci. 3: 292, https://doi.org/10.1007/s42979-022-01166-1.Search in Google Scholar PubMed PubMed Central
Ansart, M., Epelbaum, S., Bassignana, G., Bône, A., Bottani, S., Cattai, T., Couronné, R., Faouzi, J., Koval, I., Louis, M., 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.Search in Google Scholar PubMed
Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., and Khan, M.K. (2018). Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42: 226, https://doi.org/10.1007/s10916-018-1088-1.Search in Google Scholar PubMed
Arbabshirani, M.R., Plis, S., Sui, J., and Calhoun, V.D. (2017). Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145: 137–165, https://doi.org/10.1016/j.neuroimage.2016.02.079.Search in Google Scholar PubMed PubMed Central
Bae, J., Stocks, J.K., Heywood, A., Jung, Y., Jenkins, L.M., Hill, V.B., Katsaggelos, A.K., Popuri, K., Rosen, H.H., Beg, M.F., et al.. (2021). Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer’s type based on a three-dimensional convolutional neural network. Neurobiology 99: 53–64, https://doi.org/10.1016/j.neurobiolaging.2020.12.005.Search in Google Scholar PubMed PubMed Central
Balne, S. and Elumalai, A. (2021). Machine learning and deep learning algorithms used to diagnosis of Alzheimer’s: review. Mater. Today Proc. 47: 5151–5156, https://doi.org/10.1016/j.matpr.2021.05.499.Search in Google Scholar
Basaia, S., Agosta, F., Wagner, L., Canu, E., Magnani, G., Santangelo and, R., and Filippi, M. (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.Search in Google Scholar PubMed PubMed Central
Braak, H. and Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82: 239–259, https://doi.org/10.1007/bf00308809.Search in Google Scholar
Bron, E.E., Klein, S., Papma, J.M., Jiskoot, L.C., Venkatraghavan, V., Linders, J., Aalten, P., De Deyn, P.P., Biessels, G.J., Claassen, J., et al.. (2021). Alzheimer’s disease neuroimaging initiative, and parelsnoer neurodegenerative diseases study group cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease. Neuroimage Clin. 31: 102712, https://doi.org/10.1016/j.nicl.2021.102712.Search in Google Scholar PubMed PubMed Central
Camara, O., Schnabel, J.A., Ridgway, G.R., Crum, W.R., Douiri, A., Scahill, R.I., Hill, D.L., and Fox, N.C. (2008). Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal Alzheimer’s disease images. Neuroimage 42: 696–709, https://doi.org/10.1016/j.neuroimage.2008.04.259.Search in Google Scholar PubMed
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N.C., and Sardanelli, F. (2021). AI applications to medical images: from machine learning to deep learning. Phys. Med. 83: 9–24, https://doi.org/10.1016/j.ejmp.2021.02.006.Search in Google Scholar PubMed
Cao, P., Gao, J., and Zhang, Z. (2020). Multi-view based multi-model learning for MCI diagnosis. Brain Sci. 10: 181, https://doi.org/10.3390/brainsci10030181.Search in Google Scholar PubMed PubMed Central
Chen, Y. and Xia, Y. (2021). Iterative sparse and deep learning for accurate diagnosis of Alzheimer’s disease. Pattern Recogn. 116: 107944, https://doi.org/10.1016/j.patcog.2021.107944.Search in Google Scholar
Choi, H. and Jin, K.H., and Alzheimer’s Disease Neuroimaging Initiative (2018). Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav. Brain Res. 344: 103–109, https://doi.org/10.1016/j.bbr.2018.02.017.Search in Google Scholar PubMed
Cui, R. and Liu, M., and Alzheimer’s Disease Neuroimaging Initiative (2019). RNN-based longitudinal analysis for diagnosis of Alzheimer’s Disease. Comput. Med. Imag. Graph. 73: 1–10, https://doi.org/10.1016/j.compmedimag.2019.01.005.Search in Google Scholar PubMed
Cummings, J.L. (2010). Integrating ADNI results into Alzheimer’s disease drug development programs. Neurobiology 31: 1481–1492, https://doi.org/10.1016/j.neurobiolaging.2010.03.016.Search in Google Scholar PubMed PubMed Central
Dias, R. and Torkamani, A. (2019). Artificial intelligence in clinical and genomic diagnostics. Genome Med. 11: 70, https://doi.org/10.1186/s13073-019-0689-8.Search in Google Scholar PubMed PubMed Central
Dolz, J., Desrosiers, C., and Ben Ayed, I. (2018). 3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study. Neuroimage 170: 456–470, https://doi.org/10.1016/j.neuroimage.2017.04.039.Search in Google Scholar PubMed
Dong, A., Toledo, J.B., Honnorat, N., Doshi, J., Varol, E., Sotiras, A., Wolk, D., Trojanowski, J.Q., and Davatzikos, C., and Alzheimer’s Disease Neuroimaging Initiativ (2017). Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers. Brain 140: 735–747, https://doi.org/10.1093/brain/aww319.Search in Google Scholar PubMed PubMed Central
Duc, N.T., Ryu, S., Qureshi, M., Choi, M., Lee, K.H., and Lee, B. (2020). 3D-deep learning based automatic diagnosis of Alzheimer’s disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics 18: 71–86, https://doi.org/10.1007/s12021-019-09419-w.Search in Google Scholar PubMed
Dyrba, M., Hanzig, M., Altenstein, S., Bader, S., Ballarini, T., Brosseron, F., Buerger, K., Cantré, D., Dechent, P., Dobisch, L., et al.. (2021). DELCODE study groupsimproving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease. Alzheimer’s Res. Ther. 13: 191, https://doi.org/10.1186/s13195-021-00924-2.Search in Google Scholar PubMed PubMed Central
Ebrahimighahnavieh, M.A., Luo, S., and Chiong, R. (2020). Deep learning to detect Alzheimer’s disease from neuroimaging: a systematic literature review. Comput. Methods Progr. Biomed. 187: 105242, https://doi.org/10.1016/j.cmpb.2019.105242.Search in Google Scholar PubMed
Etminani, K., Soliman, A., Davidsson, A., Chang, J.R., Martínez-Sanchis, B., Byttner, S., Camacho, V., Bauckneht, M., Stegeran, R., Ressner, M., et al.. (2022). A 3D deep learning model to predict the diagnosis of dementia with lewy bodies, Alzheimer’s Disease, and mild cognitive impairment using brain 18F-FDG PET. Eur. J. Nucl. Med. Mol. Imag. 49: 563–584, https://doi.org/10.1007/s00259-021-05483-0.Search in Google Scholar PubMed PubMed Central
Feng, W., Van Halm-Lutterodt, N., Tang, H., Mecum, A., Mesregah, M.K., Ma, Y., Li, H., Zhang, F., Wu, Z., Yao, E., et al.. (2020). Automated MRI-based deep learning model for detection of Alzheimer’s disease process. Int. J. Neural Syst. 30: 2050032, https://doi.org/10.1142/s012906572050032x.Search in Google Scholar PubMed
Gao, S. and Lima, D. (2021). A review of the application of deep learning in the detection of Alzheimer’s disease. Int. J. Impact Eng. 3: 1–8, https://doi.org/10.1016/j.ijcce.2021.12.002.Search in Google Scholar
Garg, N., Choudhry, M.S., and Bodade, R.M. (2023). A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR images. J. Neurosci. Methods 384: 109745, https://doi.org/10.1016/j.jneumeth.2022.109745.Search in Google Scholar PubMed
Gaser, C., Franke, K., Klöppel, S., Koutsouleris, N., and Sauer, H., and Alzheimer’s Disease Neuroimaging Initiative (2013). BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer’s disease. PLoS One 8: e67346, https://doi.org/10.1371/journal.pone.0067346.Search in Google Scholar PubMed PubMed Central
Ge, C., Qu, Q., Gu, I.Y., and Jakola, A.S. (2019). Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images. Neurocomputing 350: 60–69, https://doi.org/10.1016/j.neucom.2019.04.023.Search in Google Scholar
Geng, Z. and Wang, Y. (2020). Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification. Nat. Commun. 11: 3311, https://doi.org/10.1038/s41467-020-17123-6.Search in Google Scholar PubMed PubMed Central
Han, R., Liu, Z., and Chen, C.P. (2022). Multi-scale 3D convolution feature-based broad learning system for Alzheimer’s disease diagnosis via MRI images. Appl. Soft Comput. 120: 108660, https://doi.org/10.1016/j.asoc.2022.108660.Search in Google Scholar
Helaly, H.A., Badawy, M., and Haikal, A.Y. (2022). Deep learning approach for early detection of Alzheimer’s disease. Cognit. Comput. 3: 1–17.Search in Google Scholar
Huang, Y., Xu, J., Zhou, Y., Tong, T., Zhuang, X., and Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2019). Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Front. Neurosci. 13: 509, https://doi.org/10.3389/fnins.2019.00509.Search in Google Scholar PubMed PubMed Central
Huang, Z., Sun, M., and Guo, C. (2021). Automatic diagnosis of Alzheimer’s disease and mild cognitive impairment based on CNN + SVM networks with end-to-end training. Comput. Intell. Neurosci. 2021: 9121770, https://doi.org/10.1155/2021/9121770.Search in Google Scholar PubMed PubMed Central
Jack, C.R.Jr, Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., Holtzman, D.M., Jagust, W., Jessen, F., Karlawish, J., et al.. (2018). NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14: 535–562, https://doi.org/10.1016/j.jalz.2018.02.018.Search in Google Scholar PubMed PubMed Central
Jack, C.R.Jr, Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., and Trojanowski, J.Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9: 119–128, https://doi.org/10.1016/s1474-4422(09)70299-6.Search in Google Scholar
Jia, X., Ren, L., and Cai, J. (2020). Clinical implementation of AI technologies will require interpretable AI models. Med. Phys. 47: 1–4, https://doi.org/10.1002/mp.13891.Search in Google Scholar PubMed
Jo, T., Nho, K., Risacher, S.L., and Saykin, A.J., and Alzheimer’s Neuroimaging Initiative (2020). Deep learning detection of informative features in Tau PET for Alzheimer’s disease classification. BMC Bioinf. 21: 496, https://doi.org/10.1186/s12859-020-03848-0.Search in Google Scholar PubMed PubMed Central
Kam, T.E., Zhang, H., Jiao, Z., and Shen, D. (2020). Deep learning of static and dynamic brain functional networks for early MCI detection. IEEE Trans. Med. Imag. 39: 478–487, https://doi.org/10.1109/tmi.2019.2928790.Search in Google Scholar
Kang, W., Lin, L., Zhang, B., Shen, X., and Wu, S., and Alzheimer’s Disease Neuroimaging Initiative (2021). Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer’s disease diagnosis. Comput. Biol. Med. 136: 104678, https://doi.org/10.1016/j.compbiomed.2021.104678.Search in Google Scholar PubMed
Klyucherev, T.O., Olszewski, P., Shalimova, A.A., Chubarev, V.N., Tarasov, V.V., Attwood, M.M., Syvänen, S., and Schiöth, H.B. (2022). Advances in the development of new biomarkers for Alzheimer’s disease. Transl. Neurodegener. 11: 25, https://doi.org/10.1186/s40035-022-00296-z.Search in Google Scholar PubMed PubMed Central
Kong, Z., Zhang, M., Zhu, W., Yi, Y., Wang, T., and Zhang, B. (2022). Multi-modal data Alzheimer’s disease detection based on 3D convolution. Biomed. Signal Process. Control 75: 103565, https://doi.org/10.1016/j.bspc.2022.103565.Search in Google Scholar
Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Commun. ACM 60: 84–90, https://doi.org/10.1145/3065386.Search in Google Scholar
Kruthika, K.R., Rajeswari, Maheshappa, H.D., and Initiative, A.D. (2019). CBIR system using capsule networks and 3D CNN for Alzheimer’s disease diagnosis. Inform. Med. Unlocked 14: 59–68, https://doi.org/10.1016/j.imu.2018.12.001.Search in Google Scholar
Krstajic, D., Buturovic, L.J., Leahy, D.E., and Thomas, S. (2014). Cross-validation pitfalls when selecting and assessing regression and classification models. J. Cheminf. 6: 10, https://doi.org/10.1186/1758-2946-6-10.Search in Google Scholar PubMed PubMed Central
Li, A., Li, F., Elahifasaee, F., Liu, M., and Zhang, L., and Alzheimer’s Disease Neuroimaging Initiative (2021). Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Brain Imaging Behav. 15: 2330–2339, https://doi.org/10.1007/s11682-020-00427-y.Search in Google Scholar PubMed
Li, F. and Liu, M., and Alzheimer’s Disease Neuroimaging Initiative (2019). A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer’s disease. J. Neurosci. Methods 323: 108–118, https://doi.org/10.1016/j.jneumeth.2019.05.006.Search in Google Scholar PubMed
Li, R., Wang, X., Lawler, K., Garg, S., Bai, Q., and Alty, J. (2022). Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions. J Biomed. Informat. 127: 104030, https://doi.org/10.1016/j.jbi.2022.104030.Search in Google Scholar PubMed
Lin, L., Zhang, G., Wang, J., Tian, M., and Wu, S. (2021a). Utilizing transfer learning of pre-trained AlexNet and relevance vector machine for regression for predicting healthy older adult’s brain age from structural MRI. Multimed. Tool. Appl. 80: 24719–24735, https://doi.org/10.1007/s11042-020-10377-8.Search in Google Scholar
Lin, W., Lin, W., Chen, G., Zhang, H., Gao, Q., Huang, Y., Tong, T., and Du, M., and Alzheimer’s Disease Neuroimaging Initiative (2021b). Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of alzheimer’s disease. Front. Neurosci. 15: 646013, https://doi.org/10.3389/fnins.2021.646013.Search in Google Scholar PubMed PubMed Central
Liu, M., Cheng, D., Wang, K., and Wang, Y., and Alzheimer’s Disease Neuroimaging Initiative (2018). Multi-modality cascaded convolutional neural networks for alzheimer’s disease diagnosis. Neuroinformatics 16: 295–308, https://doi.org/10.1007/s12021-018-9370-4.Search in Google Scholar PubMed
Liu, M., Li, F., Yan, H., Wang, K., Xu, M., and Shen, L. (2020). A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage 208: 116459, https://doi.org/10.1016/j.neuroimage.2019.116459.Search in Google Scholar PubMed
Lu, P., Hu, L., Zhang, N., Liang, H., Tian, T., and Lu, L. (2022). A two-stage model for predicting mild cognitive impairment to Alzheimer’s disease conversion. Front. Aging Neurosci. 14: 826622, https://doi.org/10.3389/fnagi.2022.826622.Search in Google Scholar PubMed PubMed Central
Mantzavinos, V. and Alexiou, A. (2017). Biomarkers for Alzheimer’s disease diagnosis. Curr. Alzheimer Res. 14: 1149–1154, https://doi.org/10.2174/1567205014666170203125942.Search in Google Scholar PubMed PubMed Central
Mirzaei, G. and Adeli, H. (2022). Machine learning techniques for diagnosis of Alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed. Signal Process. Control 72: 103293, https://doi.org/10.1016/j.bspc.2021.103293.Search in Google Scholar
Murray, M.E., Graff-Radford, N.R., Ross, O.A., Petersen, R.C., Duara, R., and Dickson, D.W. (2011). Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 10: 785–796, https://doi.org/10.1016/s1474-4422(11)70156-9.Search in Google Scholar PubMed PubMed Central
Mutasa, S., Sun, S., and Ha, R. (2020). Understanding artificial intelligence based radiology studies: what is overfitting? Clin. Imag. 65: 96–99, https://doi.org/10.1016/j.clinimag.2020.04.025.Search in Google Scholar PubMed PubMed Central
Nguyen, D., Nguyen, H., Ong, H., Le, H.H., Ha, H., Duc, N.T., and Ngo, H.T. (2022). Ensemble learning using traditional machine learning and deep neural networks for diagnosis of Alzheimer’s disease. IBRO Rep. 13: 255–263, https://doi.org/10.1016/j.ibneur.2022.08.010.Search in Google Scholar PubMed PubMed Central
Nussberger, A.M., Luo, L., Celis, L.E., and Crockett, M.J. (2022). Public attitudes value interpretability but prioritize accuracy in artificial intelligence. Nat. Commun. 13: 5821, https://doi.org/10.1038/s41467-022-33417-3.Search in Google Scholar PubMed PubMed Central
Ocasio, E. and Duong, T.Q. (2021). Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI. PeerJ Comput. Sci. 7: e560, https://doi.org/10.7717/peerj-cs.560.Search in Google Scholar PubMed PubMed Central
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., and 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.Search in Google Scholar PubMed PubMed Central
Park, J. and Jung, Y. (2022). A review and comparison of convolution neural network models under a unified framework. Commun. Stat. Appl. Methods 29: 161–176, https://doi.org/10.29220/csam.2022.29.2.161.Search in Google Scholar
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., and Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56: 303–308, https://doi.org/10.1001/archneur.56.3.303.Search in Google Scholar PubMed
Poloni, K.M., Ferrari, R.J., and Initiative, A.D. (2022). A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer’s diagnosis. Expert Syst. Appl. 195: 116622, https://doi.org/10.1016/j.eswa.2022.116622.Search in Google Scholar
Prakash, D., Madusanka, N., Bhattacharjee, S., Kim, C.H., Park, H.G., and Choi, H.K. (2021). Diagnosing Alzheimer’s disease based on multiclass MRI scans using transfer learning techniques. Curr. Med. Imag. Rev. 17: 1460–1472, https://doi.org/10.2174/1573405617666210127161812.Search in Google Scholar PubMed
Qiu, A., Xu, L., and Liu, C., and Alzheimer’s Disease Neuroimaging Initiative (2022). Predicting diagnosis 4 years prior to Alzheimer’s disease incident. Neuroimage Clin 34: 102993, https://doi.org/10.1016/j.nicl.2022.102993.Search in Google Scholar PubMed PubMed Central
Rangaswamy, U., Dharshini, S., Yesudhas, D., and Gromiha, M.M. (2020). VEPAD – predicting the effect of variants associated with Alzheimer’s disease using machine learning. Comput. Biol. Med. 124: 103933, https://doi.org/10.1016/j.compbiomed.2020.103933.Search in Google Scholar PubMed
Reyes, M., Meier, R., Pereira, S., Silva, C.A., Dahlweid, F.M., von Tengg-Kobligk, H., Summers, R.M., and Wiest, R. (2020). On the interpretability of artificial intelligence in radiology: challenges and opportunities. Artif. Intell. 2: e190043, https://doi.org/10.1148/ryai.2020190043.Search in Google Scholar PubMed PubMed Central
Rezaee, N., Fernando, W., Hone, E., Sohrabi, H.R., Johnson, S.K., Gunzburg, S., and Martins, R.N. (2021). Potential of Sorghum polyphenols to prevent and treat Alzheimer’s disease: a review article. Front. Aging Neurosci. 13: 729949, https://doi.org/10.3389/fnagi.2021.729949.Search in Google Scholar PubMed PubMed Central
Sanford, A.M. (2017). Mild cognitive impairment. Clin. Geriatr. Med. 33: 325–337, https://doi.org/10.1016/j.cger.2017.02.005.Search in Google Scholar PubMed
Sethi, M., Ahuja, S., Rani, S., Bawa, P., and Zaguia, A. (2021). Classification of Alzheimer’s disease using Gaussian-Based Bayesian parameter optimization for deep convolutional LSTM network. Comput. Math. Methods Med. 4: 4186666, https://doi.org/10.1155/2021/4186666.Search in Google Scholar PubMed PubMed Central
Shahamat, H. and Saniee Abadeh, M. (2020). Brain MRI analysis using a deep learning based evolutionary approach. Neural Network. 126: 218–234, https://doi.org/10.1016/j.neunet.2020.03.017.Search in Google Scholar PubMed
Sharma, A. and Dey, P. (2021). A machine learning approach to Unmask Novel Gene signatures and prediction of Alzheimer’s disease within different brain regions. Genomics 113: 1778–1789, https://doi.org/10.1016/j.ygeno.2021.04.028.Search in Google Scholar PubMed
Shirbandi, K., Khalafi, M., Mirza-Aghazadeh-Attari, M., Tahmasbi, M., Kiani, H., Shahvandi, H.K., Javanmardi, P., and Rahim, F. (2021). Accuracy of deep learning model-assisted amyloid positron emission tomography scan in predicting Alzheimer’s disease: a systematic review and meta-analysis. Inform. Med. Unlocked 25: 100710, https://doi.org/10.1016/j.imu.2021.100710.Search in Google Scholar
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556.Search in Google Scholar
Singh, S.P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., and Gulyás, B. (2020). 3D deep learning on medical images: a review. Sensors 20: 5097, https://doi.org/10.3390/s20185097.Search in Google Scholar PubMed PubMed Central
Smialowski, P., Frishman, D., and Kramer, S. (2009). Pitfalls of supervised feature selection. Bioinformatics 26: 440–443, https://doi.org/10.1093/bioinformatics/btp621.Search in Google Scholar PubMed PubMed Central
Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., and Shen, P. (2021). An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis. Front. Public Health 3: 637386, https://doi.org/10.3389/fdgth.2021.637386.Search in Google Scholar PubMed PubMed Central
Soria Lopez, J.A., González, H.M., and Léger, G.C. (2019). Alzheimer’s disease. Handb. Clin. Neurol. 167: 231–255, https://doi.org/10.1016/B978-0-12-804766-8.00013-3.Search in Google Scholar PubMed
Sui, J., He, H., Pearlson, G.D., Adali, T., Kiehl, K.A., Yu, Q., Clark, V.P., Castro, E., White, T., Mueller, B.A., et al.. (2013). Three-way (N-Way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia. Neuroimage 66: 119–132, https://doi.org/10.1016/j.neuroimage.2012.10.051.Search in Google Scholar PubMed PubMed Central
Toga, A.W., Bhatt, P., and Ashish, N. (2016). Global data sharing in Alzheimer’s disease research. Alzheimer Dis. Assoc. Disord. 30: 160, https://doi.org/10.1097/wad.0000000000000121.Search in Google Scholar
Tufail, A.B., Ullah, K., Khan, R.A., Shakir, M., Khan, M.A., Ullah, I., Ma, Y.K., and Ali, M.S. (2022a). On improved 3D-CNN-based binary and multiclass classification of Alzheimer’s disease using neuroimaging modalities and data augmentation methods. J. Healthc. Eng. 2022: 1302170, https://doi.org/10.1155/2022/1302170.Search in Google Scholar PubMed PubMed Central
Tufail, A.B., Anwar, N., Othman, M.T.B., Ullah, I., Khan, R.A., Ma, Y.K., Adhikari, D., Rehman, A.U., Shafiq, M., and Hamam, H. (2022b). Early-stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains. Sensors 22: 4609, https://doi.org/10.3390/s22124609.Search in Google Scholar PubMed PubMed Central
Uysal, G. and Ozturk, M. (2020). Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods. J. Neurosci. Methods 337: 108669, https://doi.org/10.1016/j.jneumeth.2020.108669.Search in Google Scholar PubMed
Varoquaux, G. (2018). Cross-validation failure: small sample sizes lead to large error bars. Neuroimage 180: 68–77, https://doi.org/10.1016/j.neuroimage.2017.06.061.Search in Google Scholar PubMed
Wachinger, C., Nho, K., Saykin, A.J., Reuter, M., and Rieckmann, A. (2018). A longitudinal imaging genetics study of neuroanatomical asymmetry in Alzheimer’s disease. Biol. Psychiatr. 84: 7, https://doi.org/10.1016/j.biopsych.2018.04.017.Search in Google Scholar PubMed PubMed Central
Wang, S., Wang, H., Cheung, A.C., Shen, Y., Gan, M., Wang, X., and Zhao, X. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing 333: 145–156, https://doi.org/10.1016/j.neucom.2018.12.018.Search in Google Scholar
Wang, J.X., Li, Y., Li, X., and Lu, Z.H. (2022). Alzheimer’s disease classification through imaging genetic data with IGnet. Front. Neurosci. 16: 846638, https://doi.org/10.3389/fnins.2022.846638.Search in Google Scholar PubMed PubMed Central
Weller, J. and Budson, A. (2018). Current understanding of Alzheimer’s disease diagnosis and treatment. F1000research 7, Rev-1161, https://doi.org/10.12688/f1000research.14506.1.Search in Google Scholar PubMed PubMed Central
Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., and Colliot, O. (2020). Alzheimer’s disease neuroimaging initiative, and Australian imaging biomarkers and lifestyle flagship study of ageingconvolutional 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.Search in Google Scholar PubMed
Weninger, S., Carrillo, M.C., Dunn, B., Aisen, P.S., Bateman, R.J., Kotz, J.D., Langbaum, J.B., Mills, S.L., Reiman, E.M., Sperling, R., et al.. (2016). Collaboration for Alzheimer’s prevention: principles to guide data and sample sharing in preclinical Alzheimer’s disease trials. Alzheimers Dement 12: 631–632, https://doi.org/10.1016/j.jalz.2016.04.001.Search in Google Scholar PubMed PubMed Central
Whitwell, J.L., Dickson, D.W., Murray, M.E., Weigand, S.D., Tosakulwong, N., Senjem, M.L., Knopman, D.S., Boeve, B.F., Parisi, J.E., Petersen, R.C., et al.. (2012). Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study. Lancet Neurol. 11: 868–877, https://doi.org/10.1016/s1474-4422(12)70200-4.Search in Google Scholar
Wu, Y., Zhou, Y., Zeng, W., Qian, Q., and Song, M. (2022). An attention-based 3D CNN with multi-scale integration block for Alzheimer’s disease classification. IEEE J. Biomed. Health Inform. 26: 5665–5673, https://doi.org/10.1109/jbhi.2022.3197331.Search in Google Scholar PubMed
Xu, Y., Jack, C.R., Jr O’Brien, P.C., Kokmen, E., Smith, G.E., Ivnik, R.J., Boeve, B.F., Tangalos, R.G., and Petersen, R.C. (2000). Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology. 54: 1760–1767, https://doi.org/10.1212/wnl.54.9.1760.Search in Google Scholar PubMed
Yagis, E., Atnafu, S.W., García Seco de Herrera, A., Marzi, C., Scheda, R., Giannelli, M., Tessa, C., Citi, L., and Diciotti, S. (2021). Effect of data leakage in brain MRI classification using 2D convolutional neural networks. Sci. Rep. 11: 22544, https://doi.org/10.1038/s41598-021-01681-w.Search in Google Scholar PubMed PubMed Central
Yee, E., Popuri, K., and Beg, M.F., and Alzheimer’s Disease Neuroimaging Initiative (2020). Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer’s dementia score. Hum. Brain Mapp. 41: 5–16, https://doi.org/10.1002/hbm.24783.Search in Google Scholar PubMed PubMed Central
Zhang, P., Lin, S., Qiao, J., and Tu, Y. (2021a). Diagnosis of Alzheimer’s disease with ensemble learning classifier and 3D convolutional neural network. Sensors 21: 7634, https://doi.org/10.3390/s21227634.Search in Google Scholar PubMed PubMed Central
Zhang, J., Zheng, B., Gao, A., Feng, X., Liang, D., and Long, X. (2021b). A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer’s disease classification. Magn. Reson. Imaging 78: 119–126, https://doi.org/10.1016/j.mri.2021.02.001.Search in Google Scholar PubMed
Zhao, X. and Zhao, X.M. (2021). Deep learning of brain magnetic resonance images: a brief review. Methods 192: 131–140, https://doi.org/10.1016/j.ymeth.2020.09.007.Search in Google Scholar PubMed
Zhao, X., Ang, C., Acharya, U.R., and Cheong, K.H. (2021). Application of artificial intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern. Biomed. Eng. 41: 456–473, https://doi.org/10.1016/j.bbe.2021.02.006.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- An update to pain management after spinal cord injury: from pharmacology to circRNAs
- Current advances in stem cell therapy in the treatment of multiple sclerosis
- Targeting NMDA receptor signaling for therapeutic intervention in brain disorders
- A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer’s disease using neuroimaging
- A systematic review of the effects of transcranial photobiomodulation on brain activity in humans
- Microbiota–gut–brain axis and related therapeutics in Alzheimer’s disease: prospects for multitherapy and inflammation control
Articles in the same Issue
- Frontmatter
- An update to pain management after spinal cord injury: from pharmacology to circRNAs
- Current advances in stem cell therapy in the treatment of multiple sclerosis
- Targeting NMDA receptor signaling for therapeutic intervention in brain disorders
- A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer’s disease using neuroimaging
- A systematic review of the effects of transcranial photobiomodulation on brain activity in humans
- Microbiota–gut–brain axis and related therapeutics in Alzheimer’s disease: prospects for multitherapy and inflammation control