Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.// METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.// RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.// DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Wrist-worn actigraphy in agitated late-stage dementia patients:A feasibility study on digital inclusion

BACKGROUND: Wrist-worn actigraphy can be an objective tool to assess sleep and other behavioral and psychological symptoms in dementia (BPSD). We investigated the feasibility of using wearable actigraphy in agitated late-stage dementia patients.METHODS: Agitated, late-stage Alzheimer's dementia care home residents in Greater London area (n = 29; 14 females, mean age ± SD: 80.8 ± 8.2; 93.1% White) were recruited to wear an actigraphy watch for 4 weeks. Wearing time was extracted to evaluate compliance, and factors influencing compliance were explored.RESULTS: A high watch-acceptance (96.6%) and compliance rate (88.0%) was noted. Non-compliance was not associated with age or BPSD symptomatology. However, participants with "better" cognitive function (R = 0.42, p = 0.022) and during nightshift (F 1.240, 33.475 = 8.075, p = 0.005) were less compliant. Female participants were also marginally less compliant (F 1, 26 = 3.790, p = 0.062). DISCUSSIONS: Wrist-worn actigraphy appears acceptable and feasible in late-stage agitated dementia patients. Accommodating the needs of both the patients and their carers may further improve compliance.</p

Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence and machine learning may refine understanding. METHODS: Machine learning approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate current applications and limitations in the dementia prevention field. RESULTS: Risk profiling tools may help identify high risk populations for clinical trials, however their performance needs improvement. New risk profiling and trial recruitment tools underpinned by machine learning models may be effective in reducing costs and improving future trials. Machine learning can inform drug repurposing efforts and prioritisation of disease-modifying therapeutics. DISCUSSION: Machine learning is not yet widely used but have considerable potential to enhance precision in dementia prevention