Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline

Background The Rey Complex Figure Test (RCFT) has been widely used to evaluate the neurocognitive functions in various clinical groups with a broad range of ages. However, despite its usefulness, the scoring method is as complex as the figure. Such a complicated scoring system can lead to the risk of reducing the extent of agreement among raters. Although several attempts have been made to use RCFT in clinical settings in a digitalized format, little attention has been given to develop direct automatic scoring that is comparable to experienced psychologists. Therefore, we aimed to develop an artificial intelligence (AI) scoring system for RCFT using a deep learning (DL) algorithm and confirmed its validity. Methods A total of 6680 subjects were enrolled in the Gwangju Alzheimers and Related Dementia cohort registry, Korea, from January 2015 to June 2021. We obtained 20,040 scanned images using three images per subject (copy, immediate recall, and delayed recall) and scores rated by 32 experienced psychologists. We trained the automated scoring system using the DenseNet architecture. To increase the model performance, we improved the quality of training data by re-examining some images with poor results (mean absolute error (MAE) ≥ 5 [points]) and re-trained our model. Finally, we conducted an external validation with 150 images scored by five experienced psychologists. Results For fivefold cross-validation, our first model obtained MAE = 1.24 [points] and R-squared (R2 ) = 0.977. However, after evaluating and updating the model, the performance of the final model was improved (MAE = 0.95 [points], R2 = 0.986). Predicted scores among cognitively normal, mild cognitive impairment, and dementia were significantly different. For the 150 independent test sets, the MAE and R2 between AI and average scores by five human experts were 0.64 [points] and 0.994, respectively. Conclusion We concluded that there was no fundamental difference between the rating scores of experienced psychologists and those of our AI scoring system. We expect that our AI psychologist will be able to contribute to screen the early stages of Alzheimers disease pathology in medical checkup centers or large-scale community-based research institutes in a faster and cost-effective way.This research was supported by the Technology Innovation Program (20022810, Development and Demonstration of a Digital System for the evaluation of geriatric Cognitive impairment) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea), by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1F1A1052932), by the Healthcare AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea (NIPA) funded by the Ministry of Science and ICT(No.1711120216), by the KBRI basic research program through the Korea Brain Research Institute funded by the Ministry of Science and ICT (22-BR-03–05), and by the Korea National Institute of Health research project (project No. 2021-ER1007-01)

Current advances in digital cognitive assessment for preclinical Alzheimer's disease

There is a pressing need to capture and track subtle cognitive change at the preclinical stage of Alzheimer's disease (AD) rapidly, cost-effectively, and with high sensitivity. Concurrently, the landscape of digital cognitive assessment is rapidly evolving as technology advances, older adult tech-adoption increases, and external events (i.e., COVID-19) necessitate remote digital assessment. Here, we provide a snapshot review of the current state of digital cognitive assessment for preclinical AD including different device platforms/assessment approaches, levels of validation, and implementation challenges. We focus on articles, grants, and recent conference proceedings specifically querying the relationship between digital cognitive assessments and established biomarkers for preclinical AD (e.g., amyloid beta and tau) in clinically normal (CN) individuals. Several digital assessments were identified across platforms (e.g., digital pens, smartphones). Digital assessments varied by intended setting (e.g., remote vs. in-clinic), level of supervision (e.g., self vs. supervised), and device origin (personal vs. study-provided). At least 11 publications characterize digital cognitive assessment against AD biomarkers among CN. First available data demonstrate promising validity of this approach against both conventional assessment methods (moderate to large effect sizes) and relevant biomarkers (predominantly weak to moderate effect sizes). We discuss levels of validation and issues relating to usability, data quality, data protection, and attrition. While still in its infancy, digital cognitive assessment, especially when administered remotely, will undoubtedly play a major future role in screening for and tracking preclinical AD

Current advances in digital cognitive assessment for preclinical Alzheimer\u27s disease

There is a pressing need to capture and track subtle cognitive change at the preclinical stage of Alzheimer\u27s disease (AD) rapidly, cost-effectively, and with high sensitivity. Concurrently, the landscape of digital cognitive assessment is rapidly evolving as technology advances, older adult tech-adoption increases, and external events (i.e., COVID-19) necessitate remote digital assessment. Here, we provide a snapshot review of the current state of digital cognitive assessment for preclinical AD including different device platforms/assessment approaches, levels of validation, and implementation challenges. We focus on articles, grants, and recent conference proceedings specifically querying the relationship between digital cognitive assessments and established biomarkers for preclinical AD (e.g., amyloid beta and tau) in clinically normal (CN) individuals. Several digital assessments were identified across platforms (e.g., digital pens, smartphones). Digital assessments varied by intended setting (e.g., remote vs. in-clinic), level of supervision (e.g., self vs. supervised), and device origin (personal vs. study-provided). At least 11 publications characterize digital cognitive assessment against AD biomarkers among CN. First available data demonstrate promising validity of this approach against both conventional assessment methods (moderate to large effect sizes) and relevant biomarkers (predominantly weak to moderate effect sizes). We discuss levels of validation and issues relating to usability, data quality, data protection, and attrition. While still in its infancy, digital cognitive assessment, especially when administered remotely, will undoubtedly play a major future role in screening for and tracking preclinical AD

Automatic interpretation of clock drawings for computerised assessment of dementia

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test has potential to improve test accessibility and accuracy. CDT sketch interpretation is one of the first stages in the analysis of the computerised test. It produces a set of recognised digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current interpretation systems perform less well on this kind of data due to its unintelligibility. In this thesis, a novel automatic interpretation system for CDT sketch is proposed and developed. The proposed interpretation system and all the related algorithms developed in this thesis are evaluated using a CDT data set collected for this study. This data consist of two sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from drawings of dementia patients. This thesis has four main contributions. The first is a conceptual model of the proposed CDT sketch interpretation system based on integrating prior knowledge of the expected CDT sketch structure and human reasoning into the drawing interpretation system. The second is a novel CDT sketch segmentation algorithm based on supervised machine learning and a new set of temporal and spatial features automatically extracted from the CDT data. The evaluation of the proposed method shows that it outperforms the current state-of-the-art method for CDT drawing segmentation. The third contribution is a new v handwritten digit recognition algorithm based on a set of static and dynamic features extracted from handwritten data. The algorithm combines two classifiers, fuzzy k-nearest neighbour’s classifier with a Convolutional Neural Network (CNN), which take advantage both of static and dynamic data representation. The proposed digit recognition algorithm is shown to outperform each classifier individually in terms of recognition accuracy. The final contribution of this study is the probabilistic Situational Bayesian Network (SBN), which is a new hierarchical probabilistic model for addressing the problem of fusing diverse data sources, such as CDT sketches created by healthy volunteers and dementia patients, in a probabilistic Bayesian network. The evaluation of the proposed SBN-based CDT sketch interpretation system on CDT data shows highly promising results, with 100% recognition accuracy for heathy CDT drawings and 97.15% for dementia data. To conclude, the proposed automatic CDT sketch interpretation system shows high accuracy in terms of recognising different sketch objects and thus paves the way for further research in dementia and clinical computer-assisted diagnosis of dementia

Is Online Motor Control Really Impaired In Parkinson\u27s Disease?

Patients with Parkinson’s disease (PD) are thought to be selectively impaired in consciously-mediated online automatic motor control, whereas the ability to perform subconscious online adjustments remains intact. This present study evaluates the hypothesis that the previously alleged deficits in online motor control in PD are not due to the consciousness of the correction, but rather are attributable to aspects of the prior experimental designs disproportionately penalizing patients for PD-related bradykinesia. Here, we implemented a modified traditional double-step paradigm to investigate consciously-mediated online motor control in PD, in a manner that would be unconfounded by disease-related bradykinesia. Further, we investigated the effects of dopamine-replacement therapy on performance. We found that PD patients (n=12) and healthy-matched controls (n=12) were equal in performing automatic online corrections whether or not these corrections were consciously perceived, and their performance was unaffected by dopaminergic therapy. These findings inform our understanding of automatic motor control in PD

Episodic memory and executive function in familial longevity

Successful aging, the ability to resist age-associated illnesses and functional disability, is of increasing importance as the population ages. Studies have shown that exceptionally long-lived individuals fit the successful aging paradigm by compressing disability toward the end of life. This study investigated whether there is evidence of successful cognitive aging in a familial longevity cohort, the Long Life Family Study (LLFS). Part 1 describes the feasibility of conducting a 2.5 hour neuropsychological battery emphasizing episodic memory and executive function, cognitive domains that elicit signs of cognitive dysfunction in relation to normal aging and dementia. The rationale for the selected tests is discussed within the context of minimizing effects from sensory impairments in an aged cohort and optimizing qualitative and quantitative data. In Part 2, the testing of 70 proband generation and 100 offspring generation LLFS participants and 140 generation-matched referent participants without familial longevity is described. Comparison of LLFS proband generation participants with their referent cohort revealed no significant differences in test scores. However, the referent cohort also had more years of education (an important exposure which is discussed in Part 3). LLFS offspring generation participants had borderline significant better performance on a test of executive function (Clock Drawing Test) and attention (Digits Forward) compared with referents. These findings suggest that familial longevity is associated with better cognitive function even at relatively young ages. Continuing to follow these cohorts to older ages may reveal differences in rate of change in cognitive function. Part 3 examines the role of indicators of cognitive reserve. In the proband generation education and participation in mid- and late-life cognitively stimulating activities were found to be higher in the referent cohort. This suggests that people without familial longevity may be more reliant on higher cognitive reserve in order to achieve similar cognitive performance to those from long-lived families. Implications of preserved cognitive function in long-lived families and the effect of cognitive reserve in those without familial longevity are discussed in terms of compression of disability and successful cognitive aging

Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Article reviews opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments