Background Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. Objective The aim of this study was to evaluate a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. Methods A total of 99 participants diagnosed with dementia, mild cognitive impairment (MCI), or normal cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. Results BrainCheck battery performance showed significant differences between the NC, MCI, and dementia groups, achieving 88% or higher sensitivity and specificity (ie, true positive and true negative rates) for separating dementia from NC, and 77% or higher sensitivity and specificity in separating the MCI group from the NC and dementia groups. Three-group classification found true positive rates of 80% or higher for the NC and dementia groups and true positive rates of 64% or higher for the MCI group. Conclusions BrainCheck was able to distinguish between diagnoses of dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

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Despite its high frequency of occurrence, mild traumatic brain injury (mTBI), or concussion, is difficult to recognize and diagnose, particularly in pediatric populations. Conventional methods to diagnose mTBI primarily rely on clinical questionnaires and sometimes include imaging such as computed tomography (CT) or pencil and paper neuropsychological testing. However, these methods are time consuming, require administration/interpretation from health professionals, and lack adequate test sensitivity and specificity. We explore the use of BrainCheck, a computerized neurocognitive test that is available on iPad, iPhone or computer desktop, for mTBI assessment. The BrainCheck battery consists of 6 gamified traditional neurocognitive tests that assess areas of cognition vulnerable to mTBI such as attention, processing speed, executing functioning, and coordination. We administered BrainCheck to 25 participants diagnosed with mTBI at the emergency department (ED) of Children's hospital within 96 hours of admittance to the ED, and 153 normal controls at a local high school. Statistical analysis included Chi-Square tests, Analysis of Variance (ANOVA), independent sample t-tests, and Hochberg tests to examine differences between mTBI, diagnoses by current gold standard clinical exam, and control groups on each assessment in the battery. Significant metrics from these assessments were used to build a logistic regression model that distinguishes mTBI from non-mTBI participants. Receiver operator score (ROC) analysis of our logistic regression model found a sensitivity of 84% and specificity of 80%. BrainCheck has potential in distinguishing mTBI from non-mTBI participants, by providing a shorter, gamified test battery to assess cognitive function after brain injury, while also providing a method for tracking recovery with the opportunity to do so remotely from a patient's home.

BACKGROUND Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. OBJECTIVE This study evaluated a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. METHODS 99 participants diagnosed with Dementia, Mild Cognitive Impairment (MCI), or Normal Cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. RESULTS BrainCheck battery performance showed significant differences between the NC, MCI, and Dementia groups, achieving >88% sensitivity/specificity for separating NC from Dementia, and >77% sensitivity/specificity in separating the MCI group from NC/Dementia groups. Three-group classification found true positive rates >80% for the NC and Dementia groups and >64% for the MCI group. CONCLUSIONS BrainCheck was able to distinguish between diagnoses of Dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

Background: Acute ingestion of alcohol impairs cognitive function and poses significant threat to public health and safety with impaired operation of motor vehicles. However, there is a lack of access to tools to assess one's cognitive impairment due to alcohol. The purpose of this study was to explore the use of a neuropsychological assessment software, BrainCheck, to assess levels of alcohol impairment based on performance on the neuropsychological assessments. Methods: We administered the BrainCheck battery to 91 volunteer participants. Participants were required to take a baseline battery prior to any alcohol ingestion, and another testing battery after a voluntary drinking period. Blood alcohol concentration (BAC) for the participant was obtained using a breathalyzer. We performed statistical analysis comparing alcohol vs. non-alcohol performance on the BrainCheck battery, and used significant metrics of these assessments to generate predictive models. Results: Statistical analyses were performed comparing participants performance on the BrainCheck battery before and after alcohol consumption. Comparison was also done comparing performance between an intoxicated group with a BAC > 0.05, and a sober group with a BAC ≤ 0.05. Two assessment metrics were found to be significant among comparison groups after P-value correction, and four test metrics were observed to moderately correlate (|r| > 0.40) with BAC levels. Three linear regression models (least-squares, ridge and LASSO) were built to predict participant BAC levels, with the best performing model being the least-squares model with a RMSE of 0.027. We also built a predictive logistic regression model to detect whether the participant is intoxicated or not, with 80.6% accuracy, 73.3% sensitivity, and 75.0% specificity. Discussion: The BrainCheck battery has potential to predict alcohol impairment, including participant BAC levels and if the participant is intoxicated or not. BrainCheck provides another option to assess an individual's cognitive impairment due to alcohol, with the utility of being portable and available on one's smartphone.