Background Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method. Objective This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices. Methods This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant’s hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment–screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22). Results We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data. Conclusions The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants’ cognitive impairment compared to conventional paper-based feature assessment.

Background: Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. Method:We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. Results:The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. Conclusion:For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.

BACKGROUND Computer-aided detection used in screening and diagnostic cognitive impairment becomes increasingly the focus of attention, which allows objective, ecologically valid, and convenient assessment. Among that, sensor technology based on digital devices is potential method for detection. v OBJECTIVE The aim of this study was to develop and validate a novel trail-making test (TMT) based on a mixing of paper and electronic devices. METHODS In this research, a community-dwelling sample of older adults (n = 297) was included, classified as cognitively normal control (NC, n = 100), diagnosed with mild cognitive impairment (MCI, n = 98) or Alzheimer's disease (AD, n = 99). We used an electromagnetic tablet to record each participant's hand-painted strokes. Meanwhile, a sheet of A4 paper was placed on top of the tablet to keep the traditional interaction style for subjects who are not familiar or comfortable with electronic devices, e.g., touchscreen. In this way, all participants were instructed to perform the TMT-square and circle (TMT-S&C). Furthermore, we developed an efficient and interpretable cognitive impairment screening model to automated analyse cognitive impairment level, which was dependent on demographic, time-related, pressure-related, jerk-related, and template-related features. Among that, the novel template-based features were based on vector quantization (VQ) algorithm. Firstly, the model singled out a candidate trajectory as the standard answer (template) from the normal group. Then the distance between the recorded trajectories and the reference was computed as the important evaluation index. To verify the effectiveness of our method, we compared the performance of well-trained machine learning model using the extracted evaluation index with conventional demographic characters and time related features. In addition, the well-trained model also was validated on follow-ups’ data (NC: 38, MCI: 32, and AD: 22). RESULTS We compared five candidate machine learning methods, screened out the random forest as the ideal model with the best performance (Accuracy: 0.777 for NC vs. MCI, 0.929 for NC vs. AD, and 0.805 for AD vs. MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method and high stability and accuracy on follow-ups’ data. CONCLUSIONS The study demonstrated that the mixing of paper and electronic TMT produced more reliable and valid data for evaluating subjects' cognitive impairment compared with conventional paper-based assessment. CLINICALTRIAL The study was approved by Medical Ethics Committee of Shanghai Tongji Hospital (ChiCTR2000039550)