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.

Background: To investigate the association between step counts and brain volumes (BVs)—global and 6 a priori selected cognition-related regions of interest—in Japanese men aged 40–79 years. Methods: The authors analyzed data from 680 cognitively intact participants of the Shiga Epidemiological Study of Subclinical Atherosclerosis—a population-based observational study. Using multivariable linear regression, the authors assessed cross-sectional associations between 7-day step counts at baseline (2006–2008) and BVs at follow-up (2012–2015) for age-stratified groups (<60 y and ≥60 y). Results: In the older adults ≥60 years, step counts at baseline (per 1000 steps) were associated with total BV at follow-up (β = 1.42, P = .022) while adjusted for potential covariates. Regions of interest-based analyses yielded an association of step counts with both prefrontal cortexes (P < .05) in older adults, while the left entorhinal cortex showed marginally significant association (P = .05). No association was observed with hippocampus, parahippocampal, cingulum, and cerebellum. No association was observed in younger adults (<60 y). Conclusions: The authors found a positive association between 7-day step counts and BVs, including prefrontal cortexes, and left entorhinal cortex in apparently healthy Japanese men.

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