Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease

Executive Summary of 2023 International Conference of the Korean Dementia Association (IC-KDA 2023): A Report From the Academic Committee of the Korean Dementia Association

The Korean Dementia Association (KDA) has been organizing biennial international academic conferences since 2019, with the International Conference of the KDA (IC-KDA) 2023 held in Busan under the theme 'Beyond Boundaries: Advancing Global Dementia Solutions.' The conference comprised 6 scientific sessions, 3 plenary lectures, and 4 luncheon symposiums, drawing 804 participants from 35 countries. Notably, a Korea-Taiwan Joint Symposium addressed insights into Alzheimer's disease (AD). Plenary lectures by renowned scholars explored topics such as microbiome-related AD pathogenesis, social cognition in neurodegenerative diseases, and genetic frontotemporal dementia (FTD). On the first day, specific presentations covered subjects like the gut-brain axis and neuroinflammation in dementia, blood-based biomarkers in AD, and updates in AD therapeutics. The second day's presentations addressed recent issues in clinical neuropsychology, FTD cohort studies, and the pathogenesis of non-AD dementia. The Academic Committee of the KDA compiles lecture summaries to provide comprehensive understanding of the advanced dementia knowledge presented at IC-KDA 2023.

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician's workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians' roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals

BACKGROUND

Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations.

METHODS

Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance.

RESULTS

The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors.

CONCLUSION

Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

Current Understanding of Verbal Fluency in Alzheimer's Disease: Evidence to Date

Since their development, verbal fluency tests (VFTs) have been used extensively throughout research and in clinical settings to assess a variety of cognitive functions in diverse populations. In Alzheimer's disease (AD), these tasks have proven particularly valuable in identifying the earliest forms of cognitive decline in semantic processing and have been shown to relate specifically to brain regions associated with the initial stages of pathological change. In recent years, researchers have developed more nuanced techniques to evaluate verbal fluency performance, extracting a wide range of cognitive metrics from these simple neuropsychological tests. Such novel techniques allow for a more detailed exploration of the cognitive processes underlying successful task performance beyond the raw test score. The versatility of VFTs and the richness of data they may provide, in light of their low cost and speed of administration, therefore, highlight their potential value both in future research as outcome measures for clinical trials and in a clinical setting as a screening measure for early detection of neurodegenerative diseases.

Integrating Technology in Neuropsychological Assessment

Abstract. Although neuropsychological assessments include some measures that are administered, scored, or interpreted using new technologies, most researchers in this area advocate that more technology should be integrated. The current situation in neuropsychological assessment may be conceptualized as triggering a crisis leading to a paradigm shift, as there is some resistance to adopting more technology. In this paper, the context of the present crisis in neuropsychological assessment, the main obstacles, and new developments will be discussed. An example of a new computerized assessment tool, the NIH Toolbox, is highlighted. Also addressed are potential issues: in the assessment with tablets illustrating it with the older adult population and how to ensure the compatibility of data collected through these devices within the framework of the European General Data Protection Regulation (GDPR). Recommendations for research, test development, and clinical practice are also provided.