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Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET
World Health Organization. Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia Accessed 30 September 2022, (2022).
Ahmed, M. R. et al. Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects. IEEE Rev. Biomed. Eng. 12, 19–33 (2018).
Lazli, L., Boukadoum, M. & Mohamed, O. A. A survey on computer-aided diagnosis of brain disorders through MRI based on machine learning and data mining methodologies with an emphasis on Alzheimer disease diagnosis and the contribution of the multimodal fusion. Appl. Sci. 10, 1894 (2020).
Lella, E., Pazienza, A., Lofù, D., Anglani, R. & Vitulano, F. An ensemble learning approach based on diffusion tensor imaging measures for Alzheimer’s disease classification. Electronics 10, 249 (2021).
Chételat, G. et al. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias. Lancet Neurol. 19, 951–962 (2020).
Bao, W., Xie, F., Zuo, C., Guan, Y. & Huang, Y. H. PET neuroimaging of Alzheimer’s disease: Radiotracers and their utility in clinical research. Front. Aging Neurosci. 13, 624330 (2021).
Rice, L. & Bisdas, S. The diagnostic value of FDG and amyloid PET in Alzheimer’s disease-A systematic review. Eur. J. Radiol. 94, 16–24 (2017).
Lesman-Segev, O. H. et al. Diagnostic accuracy of amyloid versus 18F-fluorodeoxyglucose positron emission tomography in autopsy-confirmed dementia. Ann. Neurol. 89, 389–401 (2021).
Shirbandi, K. et al. 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 (2021).
LaMontagne, P. J. et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv (2019).
Islam, M. R., Ahmed, M. U., Barua, S. & Begum, S. A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Appl. Sci.https://doi.org/10.3390/app12031353 (2022).
Chakrobartty, S. & El-Gayar, O. Explainable Artificial Intelligence in the Medical Domain: A Systematic Review. AMCIS 2021 Proceedings1 (2021).
Selvaraju, R. R. et al. Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization. CoRRabs/1610.02391, arXiv:1610.02391 (2016).
Altay, F. et al. Preclinical stage Alzheimer’s disease detection using magnetic resonance image scans. Proc. AAAI Conf. Artif. Intell. 35, 15088–15097 (2021).
Helaly, H. A., Badawy, M. & Haikal, A. Y. Deep learning approach for early detection of Alzheimer’s disease. Cognitive computation 1–17 (2021).
de Vries, B. M. et al. Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network. Eur. J. Nucl. Med. Mol. Imaging 48, 721–728 (2021).
Reith, F., Koran, M., Davidzon, G. & Zaharchuk, G. Application of deep learning to predict standardized uptake value ratio and amyloid status on 18F-florbetapir PET using ADNI data. Am. J. Neuroradiol. 41, 980–986 (2020).
Tufail, A. B. et al. On improved 3D-CNN-based binary and multiclass classification of Alzheimer’s disease using neuroimaging modalities and data augmentation methods. J. Healthc. Eng. 2022, 9769464 (2022).
Castellano, G., Esposito, A., Mirizio, M., Montanaro, G. & Vessio, G. Detection of dementia through 3D convolutional neural networks based on amyloid PET. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (ed. Castellano, G.) 1–6 (IEEE, 2021).
Zhou, T., Thung, K.-H., Zhu, X. & Shen, D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Hum. Brain Mapp. 40, 1001–1016 (2019).
Lu, D., Popuri, K., Ding, G. W., Balachandar, R. & Beg, M. F. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci. Rep. 8, 1–13 (2018).
Liu, M., Cheng, D., Wang, K. & Wang, Y. Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 16, 295–308 (2018).
Huang, Y. et al. Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Front. Neurosci. 13, 509 (2019).
Song, J. et al. An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis. Front. Digit. Health 3, 637386 (2021).
Qiu, S. et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat. Commun. 13, 3404 (2022).
Kong, Z. et al. Multi-modal data Alzheimer’s disease detection based on 3D convolution. Biomed. Signal Process. Control 75, 103565 (2022).
Rallabandi, V. S. & Seetharaman, K. Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer’s disease using fusion of MRI-PET imaging. Biomed. Signal Process. Control 80, 104312 (2023).
Gravina, M., García-Pedrero, A., Gonzalo-Martín, C., Sansone, C. & Soda, P. Multi input-Multi output 3D CNN for dementia severity assessment with incomplete multimodal data. Artif. Intell. Med. 149, 102774. https://doi.org/10.1016/j.artmed.2024.102774 (2024).
Adarsh, V., Gangadharan, G., Fiore, U. & Zanetti, P. Multimodal classification of Alzheimer’s disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis. Sci. Rep. 14, 1774 (2024).
Thyreau, B. & Taki, Y. Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks. Med. Image Anal. 61, 101639 (2020).
Kim, B. et al. CycleMorph: Cycle consistent unsupervised deformable image registration. Med. Image Anal. 71, 102036 (2021).
Madan, C. R. Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset. Eur. J. Neurosci. 53, 1661–1671 (2021).
Han, C. et al. MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinform. 22, 1–20 (2021).
Rolls, E. T., Joliot, M. & Tzourio-Mazoyer, N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage 122, 1–5 (2015).
Convit, A. et al. Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease. Neurobiol. Aging 21, 19–26 (2000).
Migliaccio, R. & Cacciamani, F. The temporal lobe in typical and atypical Alzheimer disease. In Handbook of Clinical Neurology Vol. 187 (eds Migliaccio, R. & Cacciamani, F.) 449–466 (Elsevier, 2022).
Chan, D. et al. Patterns of temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann. Neurol. 49, 433–442 (2001).
Zhong, J., Pan, P., Dai, Z. & Shi, H. Voxelwise meta-analysis of gray matter abnormalities in dementia with Lewy bodies. Eur. J. Radiol. 83, 1870–1874 (2014).
Pasquini, L. et al. Medial temporal lobe disconnection and hyperexcitability across Alzheimer’s disease stages. J. Alzheimer’s Dis. Rep. 3, 103–112 (2019).
Jack, C. R. et al. Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology 51, 993–999 (1998).
Jobst, K. et al. Rapidly progressing atrophy of medial temporal lobe in Alzheimer’s disease. Lancet 343, 829–830 (1994).
Rusinek, H. et al. Atrophy rate in medial temporal lobe during progression of Alzheimer disease. Neurology 63, 2354–2359 (2004).
Korf, E. S., Wahlund, L.-O., Visser, P. J. & Scheltens, P. Medial temporal lobe atrophy on MRI predicts dementia in patients with mild cognitive impairment. Neurology 63, 94–100 (2004).
Bouwman, F. et al. CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment. Neurobiol. Aging 28, 1070–1074 (2007).
Lella, E. et al. Communicability disruption in Alzheimer’s disease connectivity networks. J. Complex Netw. 7, 83–100 (2019).
Canu, E. et al. Mapping the structural brain changes in Alzheimer’s disease: The independent contribution of two imaging modalities. J. Alzheimers Dis. 26, 263–274 (2011).
Zhang, Y. et al. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci. 9, 66 (2015).
Karas, G. et al. Precuneus atrophy in early-onset Alzheimer’s disease: A morphometric structural MRI study. Neuroradiology 49, 967–976 (2007).
Bailly, M. et al. Precuneus and cingulate cortex atrophy and hypometabolism in patients with Alzheimer’s disease and mild cognitive impairment: MRI and 18F-FDG PET quantitative analysis using FreeSurfer. BioMed research international2015 (2015).
Herholz, K. et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17, 302–316 (2002).
He, W. et al. Meta-analytic comparison between PIB-PET and FDG-PET results in Alzheimer’s disease and MCI. Cell Biochem. Biophys. 71, 17–26 (2015).
Hirono, N. et al. Frontal lobe hypometabolism and depression in Alzheimer’s disease. Neurology 50, 380–383 (1998).
Honea, R., Swerdlow, R., Vidoni, E., Goodwin, J. & Burns, J. Reduced gray matter volume in normal adults with a maternal family history of Alzheimer disease. Neurology 74, 113–120 (2010).
Neufang, S. et al. Disconnection of frontal and parietal areas contributes to impaired attention in very early Alzheimer’s disease. J. Alzheimers Dis. 25, 309–321 (2011).
Arvanitakis, Z. et al. Brain insulin signaling, Alzheimer disease pathology, and cognitive function. Ann. Neurol. 88, 513–525 (2020).
Diehl-Schmid, J. et al. Decline of cerebral glucose metabolism in frontotemporal dementia: a longitudinal 18F-FDG-PET-study. Neurobiol. Aging 28, 42–50 (2007).
Diehl, J. et al. Cerebral metabolic patterns at early stages of frontotemporal dementia and semantic dementia. A PET study. Neurobiol. Aging 25, 1051–1056 (2004).
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What Are The Main Benefits Of Comparing Car Insurance Quotes Online
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