Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease with unknown pathogenesis that manifests with a common type of dementia. With a persistent increase in the aging population worldwide, AD has become a public health concern. Early diagnosis of AD is challenging due to its insidious onset and irreversible progression. The analysis of multiple brain features combined with artificial intelligence has been widely used for the intelligent diagnosis (ID) of AD in recent years. This study aimed to comprehensively review the relevant studies on the ID of AD from the following five aspects: clinical scales, gene and cerebrospinal fluid, brain neuroimaging, text mining, and combined features, paving a path for developing the prospects of ID in AD.
Y. Yang and X. Yao---These authors contributed equally to this work.
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References
Association, A.: 2019 Alzheimer’s disease facts and figures. Alzheimers Dement. 15(3), 321–387 (2019)
Fabrizio, C., Termine, A., Caltagirone, C., et al.: Artificial intelligence for alzheimer's disease: Promise or challenge?. Diagnostics (Basel) 11(8), (2021)
Bucholc, M., Ding, X., Wang, H., et al.: A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual. Expert Syst. Appl. 130, 157–171 (2019)
Graham, D.P., Cully, J.A., Snow, A.L., et al.: The alzheimer's disease assessment scale - cognitive subscale: Normative data for older adult controls. Alzheimer Disease & Associated Disorders 18(4), 236–240 (2004)
Na, H.R., Park, M.H., Cho, S.T., et al.: Urinary incontinence in alzheimer’s disease is associated with clinical dementia rating-sum of boxes and barthel activities of daily living. Asia Pac. Psychiatry 7(1), 113–120 (2015)
Becker, S., Boettinger, O., Sulzer, P., et al.: Everyday function in alzheimer’s and parkinson’s patients with mild cognitive impairment. J. Alzheimers Dis. 79(1), 197–209 (2021)
Chen, D., Yi, F., Qin, Y., et al.: A stacking framework for multi-classification of alzheimer’s disease using neuroimaging and clinical features. J. Alzheimers Dis. 87, 1627–1636 (2022)
Andrews, S.J., Fulton-Howard, B., Goate, A.: Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. The Lancet Neurol. 19(4), 326–335 (2020)
Bellenguez, C., Küçükali, F., Jansen, I.E., et al.: New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54(4), 412–436 (2022)
Gaetani, L., Höglund, K., Parnetti, L., et al.: A new enzyme-linked immunosorbent assay for neurofilament light in cerebrospinal fluid: analytical validation and clinical evaluation. Alzheimer’s Res. Therapy 10(1), 8 (2018)
Blennow, K., Zetterberg, H.: Biomarkers for Alzheimer’s disease: current status and prospects for the future. J. Intern. Med. 284(6), 643–663 (2018)
De Velasco Oriol, J., Vallejo, E.E., Estrada, K., et al.: Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data. BMC Bioinform. 20(1), 709 (2019)
Castillo-Barnes, D., Su, L., Ramírez, J., et al.: Autosomal dominantly inherited Alzheimer disease: analysis of genetic subgroups by machine learning. Inform. Fusion 58, 153–167 (2020)
Mahendran, N., PM, D.R.V.: A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer’s disease. Comput. Biol. Med. (Vellore) 141, 105056 (2022)
Jin, Y., Yao, X.F., Han, L.T., et al.: Application of machine learning based on genetic data in the study of Alzheimer’s disease. PR0G. Biochem. Biophys. 48(8), 888–897 (2021)
Durgamahanthi, V., Anita Christaline, J., Shirly Edward, A.: Glcm and glrlm based texture analysis: Application to brain cancer diagnosis using histopathology images. In: Dash, S.S., Das, S., Panigrahi, B.K. (eds.) Intelligent Computing and Applications. AISC, vol. 1172, pp. 691–706. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5566-4_61
So, J.H., Madusanka, N., Choi, H.K., et al.: Deep learning for alzheimer’s disease classification using texture features. Curr. Med. Imag. 15(7), 689–698 (2019)
Raghavaiah, P., Varadarajan, S.: A cad system design for Alzheimer’s disease diagnosis using temporally consistent clustering and hybrid deep learning models. Biomed. Signal Process. Control 75, 103571 (2022)
Rohini, P., Sundar, S., Ramakrishnan, S.: Characterization of alzheimer conditions in MR images using volumetric and sagittal brainstem texture features. Comput. Methods Programs Biomed. 173, 147–155 (2019)
Leandrou, S., Lamnisos, D., Mamais, I., et al.: Assessment of alzheimer’s disease based on texture analysis of the entorhinal cortex. Front Aging Neurosci 12, 176 (2020)
Trejo-Castro, A.I., Caballero-Luna, R.A., Garnica-López, J.A., et al.: Signal and texture features from t2 maps for the prediction of mild cognitive impairment to Alzheimer’s disease progression. Healthcare 9(8), 941 (2021)
Lee, S., Kim, K.W.: Alzheimer's disease neuroimaging, i.: associations between texture of t1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer's Disease. Eur J Neurol 28(3), 735–744 (2021)
Gomar, J.J., Ragland, J.D., Uluğ, A.M., et al.: Differential medial temporal lobe morphometric predictors of item- and relational-encoded memories in healthy individuals and in individuals with mild cognitive impairment and alzheimer’s disease. Alzheimer’s Dementia: Translational Res. Clin. Interventions 3(2), 238–246 (2017)
Lee, S.H., Bachman, A.H., Yu, D., et al.: Predicting progression from mild cognitive impairment to alzheimer’s disease using longitudinal callosal atrophy. Alzheimer’s & Dementia: Diag., Assess. Disease Monitor. 2, 68–74 (2016)
Zhang, Y., Wang, S., Dong, Z.: Classification of alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog. Electromagnet. Res. 144, 171–184 (2014)
Pan, D., Zeng, A., Jia, L., et al.: Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front. Neurosci. 14, 259 (2020)
Khagi, B., Kwon, G.-R., Lama, R.: Comparative analysis of Alzheimer’s disease classification by CDR level using CNN, feature selection, and machine-learning techniques. Int. J. Imaging Syst. Technol. 29(3), 297–310 (2019)
Phillips, O.R., Joshi, S.H., Piras, F., et al.: The superficial white matter in Alzheimer’s disease. Hum. Brain Mapp. 37(4), 1321–1334 (2016)
Reas, E.T., Hagler, D.J., White, N.S., et al.: Microstructural brain changes track cognitive decline in mild cognitive impairment. NeuroImage: Clinical 20, 883–891 (2018)
Farrar, D.C., Mian, A.Z., Budson, A.E., et al.: Retained executive abilities in mild cognitive impairment are associated with increased white matter network connectivity. Eur. Radiol. 28(1), 340–347 (2018)
Ebadi, A., Dalboni da Rocha, J.L., Nagaraju, D.B., et al.: Ensemble classification of Alzheimer's disease and mild cognitive impairment based on complex graph measures from diffusion tensor images. Frontiers in neuroscience 11, 56 (2017)
Marzban, E.N., Eldeib, A.M., Yassine, I.A., et al.: Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks. PLoS ONE 15(3), e0230409 (2020)
Lim, B., van der Schaar, M.: Forecasting disease trajectories in alzheimer's disease using deep learning. https://arxiv.org/abs/1807.03159 Accessed 18 Sept 2023
Crofts, J.J., Forrester, M., O’Dea, R.D.: Structure-function clustering in multiplex brain networks. Europhys. Lett. 116(1), 18003 (2016)
DeYoe, E.A., Bandettini, P., Neitz, J., et al.: Functional magnetic resonance imaging (fmri) of the human brain. J. Neurosci. Methods 54(2), 171–187 (1994)
Hong, L., Jie, X.: Research on classification of brain functional network in mci. Comput. Eng. Design 35(04), 1390–1394 (2014)
Ding, X.: Feature extraction and classification research of brain network based on resting state fmri for alzheimer’s disease. https://doi.org/10.27061/d.cnki.ghgdu.2019.001917 Accessed 18 Sept 2023
Wang, Y.: Brain Network Study of Alzheimer Disease Based on Multimodal MRI. Harbin Institute Of Technology. https://doi.org/10.27061/d.cnki.ghgdu.2019.001917 Accessed 18 Sept 2023
Zhou, H., Vallières, M., Bai, H.X., et al.: MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol. 19(6), 862–870 (2017)
Li, Y., Liu, X., Qian, Z., et al.: Genotype prediction of atrx mutation in lower-grade gliomas using an MRI radiomics signature. Eur. Radiol. 28(7), 2960–2968 (2018)
Li, Y., Jiang, J., Lu, J., et al.: Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18f-fdg pet imaging and its implementation for Alzheimer’s disease and mild cognitive impairment. Therapeutic Adv. Neurol. Disorders 12, 1756286419838682 (2019)
Feng, F., Wang, P., Zhao, K., et al.: Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment. Front. Aging Neurosci. 10, 290 (2018)
Wang, M., Jiang, J., Yan, Z., et al.: Individual brain metabolic connectome indicator based on kullback-leibler divergence similarity estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia. Eur. J. Nucl. Med. Mol. Imaging 47(12), 2753–2764 (2020)
Yao, Z., Hu, B., Huailiang, N., et al.: Individual metabolic network for the accurate detection of Alzheimer's disease based on fdgpet imaging. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1328–1335. IEEE, Shenzhen (2016)
Roque, F.S., Jensen, P.B., Schmock, H., et al.: Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput. Biol. 7(8), e1002141 (2011)
Greco, I., Day, N., Riddoch-Contreras, J., et al.: Alzheimer’s disease biomarker discovery using in silico literature mining and clinical validation. J. Transl. Med. 10(1), 1–10 (2012)
Urabe, M., Miyata, C., Fukuda, H., et al.: Individual analysis of changes in a single reminiscence session from nostalgic music videos using text mining. Alzheimer’s Dementia 19, e065037 (2023)
Lin, W., Gao, Q., Du, M., et al.: Multiclass diagnosis of stages of Alzheimer’s disease using linear discriminant analysis scoring for multimodal data. Comput. Biol. Med. 134, 104478 (2021)
Ahmed, O.B., Benois-Pineau, J., Allard, M., et al.: Recognition of Alzheimer’s disease and mild cognitive impairment with multimodal image-derived biomarkers and multiple kernel learning. Neurocomputing 220, 98–110 (2017)
Liu, F., Wee, C.-Y., Chen, H., et al.: Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. Neuroimage 84, 466–475 (2014)
Ning, K., Chen, B., Sun, F., et al.: Classifying Alzheimer’s disease with brain imaging and genetic data using a neural network framework. Neurobiol. Aging 68, 151–158 (2018)
Lu, D., Popuri, K., Ding, G.W., et al.: Multimodal and multiscale deep neural networks for the early diagnosis of alzheimer’s disease using structural MR and FDG-PET images. Scientific Reports 8(1), 5697 (2018)
Liu, M., Cheng, D., Wang, K., et al.: Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 16(3–4), 295–308 (2018)
Shi, J., Zheng, X., Li, Y., et al.: 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 (2018)
Zhang, F., Li, Z., Zhang, B., et al.: Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease. Neurocomputing 361, 185–195 (2019)
Zhou, T., Liu, M., Thung, K.H., et al.: Latent representation learning for Alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans. Med. Imaging 38(10), 2411–2422 (2019)
Korolev, I.O., Symonds, L.L., Bozoki, A.C.: Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, mri, and plasma biomarkers via probabilistic pattern classification. PLoS ONE 11(2), e0138866 (2016)
Da, X., Toledo, J.B., Zee, J., et al.: Integration and relative value of biomarkers for prediction of mci to ad progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage: Clin. 4, 164–173 (2014)
This work was funded by the grants of the National Key Research and Development Program of China (2020YFC2008700), the National Natural Science Foundation of China (Nos 61971275, 81830052, and 82072228), and the Shanghai Municipal Commission of Science and Technology for Capacity Building for Local Universities (No. 23010502700).
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Yang, Y., Yao, X., Wu, T. (2024). Progress of Intelligent Diagnosis via Multiple Brain Features in Alzheimer’s Disease. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_19
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