BackgroundWe have developed a new screening test for dementia that runs on an iPad and can be used for mass screening, known as the Cognitive Assessment for Dementia, iPad version (CADi). The CADi consists of items involving immediate recognition memory for three words, semantic memory, categorization of six objects, subtraction, backward repetition of digits, cube rotation, pyramid rotation, trail making A, trail making B, and delayed recognition memory for three words. The present study examined the reliability and validity of the CADi.MethodsCADi evaluations were conducted for patients with dementia, healthy subjects selected from a brain checkup system, and community-dwelling elderly people participating in health checkups.ResultsCADi scores were lower for dementia patients than for healthy elderly individuals and correlated significantly with Mini-Mental State Examination scores. Cronbach’s alpha values for the CADi were acceptable (over 0.7), and test–retest reliability was confirmed via a significant correlation between scores separated by a one-year interval.ConclusionThese results suggest that the CADi is a useful tool for mass screening of dementia in Japanese populations.
The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer’s disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer’s disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distribution. Our model achieved a maximum concordance index of 0.835, which was higher than that yielded by the Cox model and comparable to that of the DeepHit model. To our best knowledge, this is the first report to describe the application of a deep survival model to brain magnetic resonance imaging data. Our results demonstrate that this type of analysis could successfully predict the time of an individual’s conversion to Alzheimer’s disease.
Atherosclerotic stenosis of major intracranial arteries is a leading cause of ischemic stroke in Asia. However, the long-term prognosis of asymptomatic intracranial atherosclerotic stenosis (ICAS) in healthy volunteers has not been fully examined. Here, we conducted a longitudinal study to examine the prognosis of healthy volunteers with asymptomatic ICAS and to determine the risk factors for ICAS, including asymptomatic brain parenchymal lesions. We studied 2,807 healthy Japanese volunteers with no history of stroke (mean age, 62.0 years). They were followed for a mean interval of 64.5 months. The degree of ICAS and the presence of asymptomatic brain lesions were assessed by using magnetic resonance imaging. Asymptomatic ICAS was detected in 166 volunteers (5.9%) at the initial examination. Moderate and mild stenoses were observed in 1.5 and 4.4% of patients, respectively. Significant risk factors for ICAS were older age and a history of hypertension and/or dyslipidemia. During follow-up, ischemic stroke developed in 32 volunteers. Seven strokes occurred in the ICAS group, whose stroke incidence rate was higher than that in the non-ICAS group (0.78 vs. 0.18% per year). According to a Cox regression analysis, asymptomatic ICAS was an independent risk factor for future ischemic stroke after adjustment for age. Furthermore, after asymptomatic brain lesions were taken into account, ICAS was still a significant risk factor for stroke onset. In conclusion, even mild to moderate asymptomatic ICAS was a significant risk factor for future stroke, independent of asymptomatic brain lesions, in a healthy Japanese population. Mild to moderate ICAS might be a therapeutic target for stroke prevention.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.