Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Alzheimer s & Dementia, Journal Year: 2023, Volume and Issue: 19(12), P. 5885 - 5904

Published: Aug. 10, 2023

Abstract Introduction Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis prognosis of dementia. Methods We systematically reviewed studies reporting AI in and/or cognitive neurodegenerative diseases. Results A total 255 were identified. Most relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers most commonly used method (48%) discriminative models performed best differentiating disease from controls. The accuracy algorithms varied with patient cohort, imaging modalities, stratifiers used. Few validation an independent cohort. Discussion literature has several methodological limitations including lack sufficient algorithm development descriptions standard definitions. make recommendations to improve model addressing key clinical questions, providing description methods validating findings datasets. Collaborative approaches between experts medicine will help achieve promising potential tools practice. Highlights There been a rapid expansion use machine learning (71%) (ADNI) dataset no other individual more than five times recent rise complex (e.g., neural networks) that better classification AD vs healthy controls address considerations, also field broadly standardize outcome measures, gaps literature, monitor sources bias

Language: Английский

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Artificial intelligence for dementia research methods optimization

Alzheimer s & Dementia, Journal Year: 2023, Volume and Issue: 19(12), P. 5934 - 5951

Published: Aug. 28, 2023

Abstract Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data our ability to translate these findings into improved patient outcomes. To improve reproducibility replicability, researchers should make their well‐documented code modeling pipelines openly available. Data also be shared where appropriate. enhance acceptability of models AI‐enabled systems users, prioritize interpretable methods provide how decisions generated. Models developed using multiple, diverse datasets robustness, generalizability, reduce potentially harmful bias. clarity reproducibility, adhere reporting guidelines co‐produced with multiple stakeholders. If overcome, AI ML hold enormous promise for changing landscape research care. Highlights Machine diagnosis, prevention, management dementia. Inadequate procedures affects reproduction/replication results. built on unrepresentative do not generalize new datasets. Obligatory metrics certain model structures use cases have been defined. Interpretability trust predictions barriers clinical translation.

Language: Английский

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A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction

Healthcare Analytics, Journal Year: 2024, Volume and Issue: unknown, P. 100362 - 100362

Published: Sept. 1, 2024

Language: Английский

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Artificial intelligence for neurodegenerative experimental models

Alzheimer s & Dementia, Journal Year: 2023, Volume and Issue: 19(12), P. 5970 - 5987

Published: Sept. 28, 2023

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered model systems has proven immensely challenging, marred by high failure rates human clinical trials.

Language: Английский

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Artificial intelligence for dementia prevention

Alzheimer s & Dementia, Journal Year: 2023, Volume and Issue: 19(12), P. 5952 - 5969

Published: Oct. 14, 2023

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

Language: Английский

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Machine Learning Approach to Identify Case-Control Studies on ApoE Gene Mutations Linked to Alzheimer’s Disease in Italy

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(1), P. 600 - 622

Published: Feb. 23, 2024

Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed evaluate the performance MySLR learning platform, implements Latent Dirichlet Allocation (LDA) algorithm in identification screening papers present literature that focus on mutations apolipoprotein E (ApoE) gene Italian Alzheimer’s Disease patients. Results: excludes duplicates creates topics. was applied analyze a set 164 scientific publications. After duplicate removal, results allowed us identify 92 divided into two relevant topics characterizing investigated research area. Topic 1 contains 70 papers, topic 2 remaining 22. Despite current limitations, available evidence suggests articles containing studies (AD) patients were 65.22% (n = 60). Furthermore, presence about mutations, including single nucleotide polymorphisms (SNPs) ApoE gene, primary genetic risk factor AD, for population 5.4% 5). Conclusion: The show platform helped case-control SNPs, but not only conducted Italy.

Language: Английский

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LD‐informed deep learning for Alzheimer's gene loci detection using WGS data

Alzheimer s & Dementia Translational Research & Clinical Interventions, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

Language: Английский

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Circuit training intervention for cognitive function, gut microbiota, and aging control: study protocol for a longitudinal, open-label randomized controlled trial

Trials, Journal Year: 2025, Volume and Issue: 26(1)

Published: March 18, 2025

Abstract Background Long-term exercise is increasingly considered an effective strategy to counteract cognitive decline associated with aging. Previous studies have indicated that circuit training exercises integrating aerobic and resistance modalities positively affect function. Furthermore, a growing body of evidence suggests long-term alters the gut microbiota, leading optimal environment for enhancement. Recent empirical plays significant role in modulating aging-control factors at protein level. Although interaction between function multifaceted, most only examined direct pathway from Therefore, this study aims elucidate effects on through comprehensive analysis such as microbiota proteins related aging control. Methods A total fifty-one participants will be randomly assigned either or waitlist control group. The intervention group participate program developed by Curves Japan Co., Ltd. two three times weekly 16 weeks. continue their usual daily routines without participating any new active lifestyle program. undergo assessments baseline after intervention. Fecal blood samples collected before effect cognition analyzed comparing measured outcomes associations among these assessed using linear mixed model structural equation modeling approaches. Discussion This provide first insights into perspectives findings are expected contribute improving brain health combating age-related decline. may help establish guidelines future relationship

Language: Английский

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Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e57830 - e57830

Published: Aug. 8, 2024

Background With the rise of artificial intelligence (AI) in field dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes assesses key contributions development scale AI research. Objective The aim this study was to comprehensively evaluate state, hot topics, future globally. Methods thoroughly analyzed application biomarkers across various dimensions, such as publication volume, authors, institutions, journals, countries, based on Web Science Core Collection. In addition, scales, trends, potential connections between were extracted deeply through multiple expert panels. Results To date, includes 1070 publications 362 involving 74 countries 1793 major with a total 6455 researchers. Notably, 69.41% (994/1432) researchers ceased their studies before 2019. most prevalent algorithms used are support vector machines, random forests, neural networks. Current frequently imaging biomarkers, cerebrospinal fluid genetic blood biomarkers. Recent advances have highlighted significant discoveries related imaging, genetics, blood, growth digital ophthalmic Conclusions is currently phase stable development, receiving widespread attention from numerous worldwide. Despite this, clusters collaborative yet be established, there pressing need enhance interdisciplinary collaboration. Algorithm shown prominence, especially machines networks studies. Looking forward, newly discovered expected undergo further validation, new types, will garner increased interest attention.

Language: Английский

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Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Frontiers in Neurology, Journal Year: 2023, Volume and Issue: 14

Published: Nov. 22, 2023

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in context neurotrauma research. Neuroproteomics involves quantitative qualitative analysis nervous system components, essential for understanding dynamic events involved vast areas neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative mental illness, traumatic brain injury, chronic encephalopathy, other diseases. With advancements mass spectrometry coupled with bioinformatics systems biology, neuroproteomics led to development innovative techniques such as microproteomics, single-cell imaging spectrometry, which have significantly impacted neuronal biomarker By analyzing complex protein interactions alterations that occur injured brain, provides valuable insights into pathophysiological mechanisms underlying neurotrauma. This review explores how can be harnessed advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight potential future prospects neuroproteomics, identifying novel biomarkers developing targeted therapies by employing artificial intelligence (AI) machine learning (ML). shedding light neurotrauma's current state directions, this aims stimulate further research collaboration promising transformative field.

Language: Английский

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