Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation

Abstract

Background: With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer's disease (AD) and prediction of mild cognitive impairment (MCI) progression.Methods: We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively.Results: BAAD's SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aβ positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD.Conclusion: Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice.

Quantification of Brain β-Amyloid Load in Parkinson's Disease With Mild Cognitive Impairment: A PET/MRI Study

“…The Brain Anatomical Analysis using Diffeomorphic deformation (BAAD 4.31-http://www.shiga-med.ac.jp/ $\sim$hqbioph/BAAD/Welcome_to_BAAD.html) (31) and Statistical Parametric Mapping tool (SPM12, https://www.fil. ion.ucl.ac.uk/spm/software/spm12/) were used to calculate the cortical alteration patterns.…”

Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population

“…Given that the hippocampal atrophy is relatively identifiable visually, cortical atrophy may be even more difficult to assess visually in the early stages of AD. VSRAD supports radiologists by providing information of hippocampal atrophy, but our ML outperformed radiologists even when supported by VSRAD, 6 our ML also outperformed VSRAD in the Japanese population (Table 1 ). Of interest, the ML was also effective in predicting cerebral Aβ accumulation (Table 3 ).…”

“…The optimal cutoff values determined by the Youden index to classify AD and NL in the JADNI database were 0.42, 0.23, and 1.26 for ADLS, ADLSc, and VSRAD, respectively. Because the optimal cutoff value for ADLS validated using the North American ADNI database was 0.48, 6 the cutoff points for ADLS and ADLSc were set at 0.5, and for VSRAD at 1.3 for the subsequent analysis. Accuracy, sensitivity, and specificity are influenced by prevalence (eg., the percentage of AD and NL patients of the data); therefore, the MCC and F1 scores were also expressed.…”

“…More information about BAAD can be found elsewhere. 6 We inducted anatomical region of interest (ROI) as a unit to reduce dimensionality, and created an algorithm with a support vector machine (SVM). Technically, the introduction of standardized variables that have been adjusted for covariates improves ML performance.…”

Transfer-Based Deep Neural Network for Fault Diagnosis of New Energy Vehicles

“…On the one hand, after predefining the features, faults could be classified by conventional machine learning (ML) algorithms. Popularly used conventional ML algorithms include the support vector machine (SVM) (Feng et al, 2020), logistic regression (LR), nearest neighbor algorithm (KNN) (Syaifullah et al, 2021), and random forest (RF). The characteristics of these algorithms are easy training and good computing performance.…”