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For: Shaikh TA, Ali R. Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images. Magnetic Resonance Imaging 2019;62:167-73. [DOI: 10.1016/j.mri.2019.06.019] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 3.3] [Reference Citation Analysis]
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2 Gordon E, Bocchetta M, Nicholas J, Cash DM, Rohrer JD. A comparison of automated atrophy measures across the frontotemporal dementia spectrum: Implications for trials. Neuroimage Clin 2021;32:102842. [PMID: 34626889 DOI: 10.1016/j.nicl.2021.102842] [Reference Citation Analysis]
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4 Khagi B, Lee KH, Choi KY, Lee JJ, Kwon G, Yang H. VBM-Based Alzheimer’s Disease Detection from the Region of Interest of T1 MRI with Supportive Gaussian Smoothing and a Bayesian Regularized Neural Network. Applied Sciences 2021;11:6175. [DOI: 10.3390/app11136175] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybernetics and Biomedical Engineering 2021;41:456-73. [DOI: 10.1016/j.bbe.2021.02.006] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
6 Zhang J, Li Y, Gao Y, Hu J, Huang B, Rong S, Chen J, Zhang Y, Wang L, Feng S, Wang L, Nie K. An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease. J Neurol Sci 2020;418:117077. [PMID: 32798842 DOI: 10.1016/j.jns.2020.117077] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]