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Siette J, Adam PJ, Harris CB. Acceptability of virtual reality to screen for dementia in older adults. BMC Geriatr 2024; 24:493. [PMID: 38840041 PMCID: PMC11151481 DOI: 10.1186/s12877-024-05115-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Early detection of dementia and cognitive decline is crucial for effective interventions and overall wellbeing. Although virtual reality (VR) tools offer potential advantages to traditional dementia screening tools, there is a lack of knowledge regarding older adults' acceptance of VR tools, as well as the predictors and features influencing their adoption. This study aims to (i) explore older adults' perceptions of the acceptability and usefulness of VR diagnostic tools for dementia, and (ii) identify demographic predictors of adoption and features of VR applications that contribute to future adoption among older adults. METHODS A cross-sectional study was conducted involving community-dwelling older adults who completed online questionnaires covering demographics, medical history, technology acceptance, previous usage, and perceived usefulness and barriers to VR adoption. Multiple linear regression was employed to assess relationships between sociodemographic factors, prior technology use, perceived ease, usefulness, and intention to adopt VR-based diagnostic tools. RESULTS Older adults (N = 77, Mage = 73.74, SD = 6.4) were predominantly female and born in English-speaking countries. Perceived usefulness of VR applications and educational attainment emerged as significant predictors of the likelihood to use VR applications for dementia screening. Generally, older adults showed acceptance of VR applications for healthcare and dementia screening. Fully immersive applications were preferred, and older adults were mostly willing to share electronic information from screening with their healthcare providers. CONCLUSIONS The field of research on VR applications in healthcare is expanding. Understanding the demographic characteristics of populations that stand to benefit from healthcare innovations is critical for promoting adoption of digital health technologies and mitigating its barriers to access.
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Affiliation(s)
- Joyce Siette
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Westmead, NSW, 2145, Australia.
| | - Patrick J Adam
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Westmead, NSW, 2145, Australia
| | - Celia B Harris
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Westmead, NSW, 2145, Australia
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Pluta R, Bogucka-Kocka A, Bogucki J, Kocki J, Czuczwar SJ. Apoptosis, Autophagy, and Mitophagy Genes in the CA3 Area in an Ischemic Model of Alzheimer's Disease with 2-Year Survival. J Alzheimers Dis 2024:JAD240401. [PMID: 38759019 DOI: 10.3233/jad-240401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
Background Currently, no evidence exists on the expression of apoptosis (CASP3), autophagy (BECN1), and mitophagy (BNIP3) genes in the CA3 area after ischemia with long-term survival. Objective The goal of the paper was to study changes in above genes expression in CA3 area after ischemia in the period of 6-24 months. Methods In this study, using quantitative RT-PCR, we present the expression of genes associated with neuronal death in a rat ischemic model of Alzheimer's disease. Results First time, we demonstrated overexpression of the CASP3 gene in CA3 area after ischemia with survival ranging from 0.5 to 2 years. Overexpression of the CASP3 gene was accompanied by a decrease in the activity level of the BECN1 and BNIP3 genes over a period of 0.5 year. Then, during 1-2 years, BNIP3 gene expression increased significantly and coincided with an increase in CASP3 gene expression. However, BECN1 gene expression was variable, increased significantly at 1 and 2 years and was below control values 1.5 years post-ischemia. Conclusions Our observations suggest that ischemia with long-term survival induces neuronal death in CA3 through activation of caspase 3 in cooperation with the pro-apoptotic gene BNIP3. This study also suggests that the BNIP3 gene regulates caspase-independent pyramidal neuronal death post-ischemia. Thus, caspase-dependent and -independent death of neuronal cells occur post-ischemia in the CA3 area. Our data suggest new role of the BNIP3 gene in the regulation of post-ischemic neuronal death in CA3. This suggests the involvement of the BNIP3 together with the CASP3 in the CA3 in neuronal death post-ischemia.
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Affiliation(s)
- Ryszard Pluta
- Department of Pathophysiology, Medical University of Lublin, Lublin, Poland
| | - Anna Bogucka-Kocka
- Department of Biology and Genetics, Medical University of Lublin, Lublin, Poland
| | - Jacek Bogucki
- Faculty of Medicine, Johon Paul II Catholic University of Lublin, Lublin, Poland
| | - Janusz Kocki
- Department of Clinical Genetics, Medical University of Lublin, Lublin, Poland
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Kaser AN, Lacritz LH, Winiarski HR, Gabirondo P, Schaffert J, Coca AJ, Jiménez-Raboso J, Rojo T, Zaldua C, Honorato I, Gallego D, Nieves ER, Rosenstein LD, Cullum CM. A novel speech analysis algorithm to detect cognitive impairment in a Spanish population. Front Neurol 2024; 15:1342907. [PMID: 38638311 PMCID: PMC11024431 DOI: 10.3389/fneur.2024.1342907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Objective Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Method Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Results Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Conclusion Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.
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Affiliation(s)
- Alyssa N. Kaser
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Laura H. Lacritz
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Holly R. Winiarski
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Jeff Schaffert
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Alberto J. Coca
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
- Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, United Kingdom
| | | | - Tomas Rojo
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | - Carla Zaldua
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | | | | | - Emmanuel Rosario Nieves
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - Leslie D. Rosenstein
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - C. Munro Cullum
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurological Surgery, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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Zhao X, Wen H, Xu G, Pang T, Zhang Y, He X, Hu R, Yan M, Chen C, Wu X, Xu X. Validity, feasibility, and effectiveness of a voice-recognition based digital cognitive screener for dementia and mild cognitive impairment in community-dwelling older Chinese adults: A large-scale implementation study. Alzheimers Dement 2024; 20:2384-2396. [PMID: 38299756 PMCID: PMC11032546 DOI: 10.1002/alz.13668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/03/2023] [Accepted: 12/03/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION We investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants. METHODS Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening. HIGHLIGHTS To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.
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Affiliation(s)
- Xuhao Zhao
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Haoxuan Wen
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Guohai Xu
- DAMO Academy, Alibaba GroupHangzhouZhejiangP. R. China
| | - Ting Pang
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Yaping Zhang
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Xindi He
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Ruofei Hu
- DAMO Academy, Alibaba GroupHangzhouZhejiangP. R. China
| | - Ming Yan
- DAMO Academy, Alibaba GroupHangzhouZhejiangP. R. China
| | - Christopher Chen
- Department of PharmacologyYong Loo Lin School of MedicineMemory, Ageing, and Cognition Centre (MACC)National University of SingaporeSingaporeSingapore
| | - Xifeng Wu
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Xin Xu
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
- Department of PharmacologyYong Loo Lin School of MedicineMemory, Ageing, and Cognition Centre (MACC)National University of SingaporeSingaporeSingapore
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Bergschöld JM, Gunnes M, Eide AH, Lassemo E. Characteristics and Range of Reviews About Technologies for Aging in Place: Scoping Review of Reviews. JMIR Aging 2024; 7:e50286. [PMID: 38252472 PMCID: PMC10845034 DOI: 10.2196/50286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 09/25/2023] [Accepted: 10/30/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND It is a contemporary and global challenge that the increasing number of older people requiring care will surpass the available caregivers. Solutions are needed to help older people maintain their health, prevent disability, and delay or avoid dependency on others. Technology can enable older people to age in place while maintaining their dignity and quality of life. Literature reviews on this topic have become important tools for researchers, practitioners, policy makers, and decision makers who need to navigate and access the extensive available evidence. Due to the large number and diversity of existing reviews, there is a need for a review of reviews that provides an overview of the range and characteristics of the evidence on technology for aging in place. OBJECTIVE This study aimed to explore the characteristics and the range of evidence on technologies for aging in place by conducting a scoping review of reviews and presenting an evidence map that researchers, policy makers, and practitioners may use to identify gaps and reviews of interest. METHODS The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Literature searches were conducted in Web of Science, PubMed, and Scopus using a search string that consisted of the terms "older people" and "technology for ageing in place," with alternate terms using Boolean operators and truncation, adapted to the rules for each database. RESULTS A total of 5447 studies were screened, with 344 studies included after full-text screening. The number of reviews on this topic has increased dramatically over time, and the literature is scattered across a variety of journals. Vocabularies and approaches used to describe technology, populations, and problems are highly heterogeneous. We have identified 3 principal ways that reviews have dealt with populations, 5 strategies that the reviews draw on to conceptualize technology, and 4 principal types of problems that they have dealt with. These may be understood as methods that can inform future reviews on this topic. The relationships among populations, technologies, and problems studied in the reviews are presented in an evidence map that includes pertinent gaps. CONCLUSIONS Redundancies and unexploited synergies between bodies of evidence on technology for aging in place are highly likely. These results can be used to decrease this risk if they are used to inform the design of future reviews on this topic. There is a need for an examination of the current state of the art in knowledge on technology for aging in place in low- and middle-income countries, especially in Africa.
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Affiliation(s)
| | - Mari Gunnes
- Department of Health, SINTEF Digital, Trondheim, Norway
| | - Arne H Eide
- Department of Health, SINTEF Digital, Oslo, Norway
| | - Eva Lassemo
- Department of Health, SINTEF Digital, Trondheim, Norway
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Li R, Wang X, Lawler K, Garg S, St George RJ, Bindoff AD, Bartlett L, Roccati E, King AE, Vickers JC, Bai Q, Alty J. Brief webcam test of hand movements predicts episodic memory, executive function, and working memory in a community sample of cognitively asymptomatic older adults. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12520. [PMID: 38274411 PMCID: PMC10809289 DOI: 10.1002/dad2.12520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Low-cost simple tests for preclinical Alzheimer's disease are a research priority. We evaluated whether remote unsupervised webcam recordings of finger-tapping were associated with cognitive performance in older adults. METHODS A total of 404 cognitively-asymptomatic participants (64.6 [6.77] years; 70.8% female) completed 10-second finger-tapping tests (Tasmanian [TAS] Test) and cognitive tests (Cambridge Neuropsychological Test Automated Battery [CANTAB]) online at home. Regression models including hand movement features were compared with null models (comprising age, sex, and education level); change in Akaike Information Criterion greater than 2 (ΔAIC > 2) denoted statistical difference. RESULTS Hand movement features improved prediction of episodic memory, executive function, and working memory scores (ΔAIC > 2). Dominant hand features outperformed nondominant hand features for episodic memory (ΔAIC = 2.5), executive function (ΔAIC = 4.8), and working memory (ΔAIC = 2.2). DISCUSSION This brief webcam test improved prediction of cognitive performance compared to age, sex, and education. Finger-tapping holds potential as a remote language-agnostic screening tool to stratify community cohorts at risk for cognitive decline.
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Affiliation(s)
- Renjie Li
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
- School of ICTUniversity of TasmaniaHobartTasmaniaAustralia
| | - Xinyi Wang
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Katherine Lawler
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
- School of Allied HealthHuman Services and SportLa Trobe UniversityMelbourneVictoriaAustralia
| | - Saurabh Garg
- School of ICTUniversity of TasmaniaHobartTasmaniaAustralia
| | | | - Aidan D. Bindoff
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Larissa Bartlett
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Eddy Roccati
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Anna E. King
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - James C. Vickers
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Quan Bai
- School of ICTUniversity of TasmaniaHobartTasmaniaAustralia
| | - Jane Alty
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
- School of MedicineUniversity of TasmaniaHobartTasmaniaAustralia
- Neurology DepartmentRoyal Hobart HospitalHobartTasmaniaAustralia
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Chen L, Zhang M, Yu W, Yu J, Cui Q, Chen C, Liu J, Huang L, Liu J, Yu W, Li W, Zhang W, Yan M, Wu J, Wang X, Song J, Zhong F, Liu X, Wang X, Li C, Tan Y, Sun J, Li W, Lü Y. A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening. J Alzheimers Dis 2024; 97:1661-1672. [PMID: 38306031 DOI: 10.3233/jad-230518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Background Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and -3.26 points. Conclusions Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy.
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Affiliation(s)
- Lihua Chen
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Meiwei Zhang
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Weihua Yu
- Department of Human Anatomy, Institute of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Juan Yu
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Qiushi Cui
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Chenxi Chen
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junjin Liu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lihong Huang
- Department of Human Anatomy, Institute of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Jiarui Liu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wuhan Yu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenjie Li
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenbo Zhang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengyu Yan
- Department of Human Anatomy, Institute of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Jiani Wu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoqin Wang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiaqi Song
- Department of Human Anatomy, Institute of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Fuxing Zhong
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xintong Liu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xianglin Wang
- College of Computer Science, Chongqing University, Chongqing, China
| | - Chengxing Li
- College of Computer Science, Chongqing University, Chongqing, China
| | - Yuantao Tan
- College of Computer Science, Chongqing University, Chongqing, China
| | - Jiangshan Sun
- College of Computer Science, Chongqing University, Chongqing, China
| | - Wenyuan Li
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Diaz-Asper C, Chandler C, Elvevåg B. Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions. J Alzheimers Dis 2024; 99:869-876. [PMID: 38728193 DOI: 10.3233/jad-240293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.
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Affiliation(s)
- Catherine Diaz-Asper
- Department of Psychology & Center for Optimal Aging, Marymount University, Arlington, VA, USA
| | - Chelsea Chandler
- Institute of Cognitive Science, University of Colorado, Boulder, CO, USA
| | - Brita Elvevåg
- Department of Clinical Medicine, University of Tromsø-the Arctic University of Norway, Tromsø-, Norway
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Ivanova O, Martínez-Nicolás I, Meilán JJG. Speech changes in old age: Methodological considerations for speech-based discrimination of healthy ageing and Alzheimer's disease. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:13-37. [PMID: 37140204 DOI: 10.1111/1460-6984.12888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/03/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Recent evidence suggests that speech substantially changes in ageing. As a complex neurophysiological process, it can accurately reflect changes in the motor and cognitive systems underpinning human speech. Since healthy ageing is not always easily discriminable from early stages of dementia based on cognitive and behavioural hallmarks, speech is explored as a preclinical biomarker of pathological itineraries in old age. A greater and more specific impairment of neuromuscular activation, as well as a specific cognitive and linguistic impairment in dementia, unchain discriminating changes in speech. Yet, there is no consensus on such discriminatory speech parameters, neither on how they should be elicited and assessed. AIMS To provide a state-of-the-art on speech parameters that allow for early discrimination between healthy and pathological ageing; the aetiology of these parameters; the effect of the type of experimental stimuli on speech elicitation and the predictive power of different speech parameters; and the most promising methods for speech analysis and their clinical implications. METHODS & PROCEDURES A scoping review methodology is used in accordance with the PRISMA model. Following a systematic search of PubMed, PsycINFO and CINAHL, 24 studies are included and analysed in the review. MAIN CONTRIBUTION The results of this review yield three key questions for the clinical assessment of speech in ageing. First, acoustic and temporal parameters are more sensitive to changes in pathological ageing and, of these two, temporal variables are more affected by cognitive impairment. Second, different types of stimuli can trigger speech parameters with different degree of accuracy for the discrimination of clinical groups. Tasks with higher cognitive load are more precise in eliciting higher levels of accuracy. Finally, automatic speech analysis for the discrimination of healthy and pathological ageing should be improved for both research and clinical practice. CONCLUSIONS & IMPLICATIONS Speech analysis is a promising non-invasive tool for the preclinical screening of healthy and pathological ageing. The main current challenges of speech analysis in ageing are the automatization of its clinical assessment and the consideration of the speaker's cognitive background during evaluation. WHAT THIS PAPER ADDS What is already known on the subject Societal aging goes hand in hand with the rising incidence of ageing-related neurodegenerations, mainly Alzheimer's disease (AD). This is particularly noteworthy in countries with longer life expectancies. Healthy ageing and early stages of AD share a set of cognitive and behavioural characteristics. Since there is no cure for dementias, developing methods for accurate discrimination of healthy ageing and early AD is currently a priority. Speech has been described as one of the most significantly impaired features in AD. Neuropathological alterations in motor and cognitive systems would underlie specific speech impairment in dementia. Since speech can be evaluated quickly, non-invasively and inexpensively, its value for the clinical assessment of ageing itineraries may be particularly high. What this paper adds to existing knowledge Theoretical and experimental advances in the assessment of speech as a marker of AD have developed rapidly over the last decade. Yet, they are not always known to clinicians. Furthermore, there is a need to provide an updated state-of-the-art on which speech features are discriminatory to AD, how they can be assessed, what kind of results they can yield, and how such results should be interpreted. This article provides an updated overview of speech profiling, methods of speech measurement and analysis, and the clinical power of speech assessment for early discrimination of AD as the most common cause of dementia. What are the potential or actual clinical implications of this work? This article provides an overview of the predictive potential of different speech parameters in relation to AD cognitive impairment. In addition, it discusses the effect that the cognitive state, the type of elicitation task and the type of assessment method may have on the results of the speech-based analysis in ageing.
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Affiliation(s)
- Olga Ivanova
- Spanish Language Department, Faculty of Philology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Israel Martínez-Nicolás
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Juan José García Meilán
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
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10
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Cabrera-León Y, Báez PG, Fernández-López P, Suárez-Araujo CP. Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review. J Alzheimers Dis 2024; 98:793-823. [PMID: 38489188 DOI: 10.3233/jad-231271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too. Background Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
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Affiliation(s)
- Ylermi Cabrera-León
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Patricio García Báez
- Departamento de Ingeniería Informática y de Sistemas, Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna, San Cristóbal de La Laguna, Canary Islands, Spain
| | - Pablo Fernández-López
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Carmen Paz Suárez-Araujo
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
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11
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Huang G, Li R, Bai Q, Alty J. Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review. Health Inf Sci Syst 2023; 11:32. [PMID: 37489153 PMCID: PMC10363100 DOI: 10.1007/s13755-023-00231-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/04/2023] [Indexed: 07/26/2023] Open
Abstract
With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer's disease (AD) and Parkinson's disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.
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Affiliation(s)
- Guan Huang
- School of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia
| | - Renjie Li
- School of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia
| | - Quan Bai
- School of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia
| | - Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia
- School of Medicine, University of Tasmania, Hobart, TAS 7000 Australia
- Neurology Department, Royal Hobart Hospital, Hobart, 7000 Australia
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12
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Newby D, Orgeta V, Marshall CR, Lourida I, Albertyn CP, Tamburin S, Raymont V, Veldsman M, Koychev I, Bauermeister S, Weisman D, Foote IF, Bucholc M, Leist AK, Tang EYH, Tai XY, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia prevention. Alzheimers Dement 2023; 19:5952-5969. [PMID: 37837420 PMCID: PMC10843720 DOI: 10.1002/alz.13463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/16/2023]
Abstract
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. HIGHLIGHTS Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
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Affiliation(s)
- Danielle Newby
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Department of Neurology, Royal London Hospital, London, E1 1BB, UK
| | - Ilianna Lourida
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
| | - Christopher P Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, 37129, Italy
| | - Vanessa Raymont
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
| | - Ivan Koychev
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Sarah Bauermeister
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - David Weisman
- Abington Neurological Associates, Abington, PA 19001, USA
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, BT48 7JL, UK
| | - Anja K Leist
- Institute for Research on Socio-Economic Inequality (IRSEI), Department of Social Sciences, University of Luxembourg, L-4365, Luxembourg
| | - Eugene Y H Tang
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
| | - Xin You Tai
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK
| | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
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13
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Parsapoor M. AI-based assessments of speech and language impairments in dementia. Alzheimers Dement 2023; 19:4675-4687. [PMID: 37578167 DOI: 10.1002/alz.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 08/15/2023]
Abstract
Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.
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Affiliation(s)
- Mahboobeh Parsapoor
- Centre de Recherche Informatique de Montréal: CRIM, Montreal, Quebec, Canada
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14
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Wilson S, Tolley C, Mc Ardle R, Beswick E, Slight SP. Key Considerations When Developing and Implementing Digital Technology for Early Detection of Dementia-Causing Diseases Among Health Care Professionals: Qualitative Study. J Med Internet Res 2023; 25:e46711. [PMID: 37606986 PMCID: PMC10481214 DOI: 10.2196/46711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND The World Health Organization (WHO) promotes using digital technologies to accelerate global attainment of health and well-being. This has led to a growth in research exploring the use of digital technology to aid early detection and preventative interventions for dementia-causing diseases such as Alzheimer disease. The opinions and perspectives of health care professionals must be incorporated into the development and implementation of technology to promote its successful adoption in clinical practice. OBJECTIVE This study aimed to explore health care professionals' perspectives on the key considerations of developing and implementing digital technologies for the early detection of dementia-causing diseases in the National Health Service (NHS). METHODS Health care professionals with patient-facing roles in primary or secondary care settings in the NHS were recruited through various web-based NHS clinical networks. Participants were interviewed to explore their experiences of the current dementia diagnostic practices, views on early detection and use of digital technology to aid these practices, and the challenges of implementing such interventions in health care. An inductive thematic analysis approach was applied to identify central concepts and themes in the interviews, allowing the data to determine our themes. A list of central concepts and themes was applied systematically to the whole data set using NVivo (version 1.6.1; QSR International). Using the constant comparison technique, the researchers moved backward and forward between these data and evolving explanations until a fit was made. RESULTS Eighteen semistructured interviews were conducted, with 11 primary and 7 secondary care health care professionals. We identified 3 main categories of considerations relevant to health care service users, health care professionals, and the digital health technology itself. Health care professionals recognized the potential of using digital technology to collect real-time data and the possible benefits of detecting dementia-causing diseases earlier if an effective intervention were available. However, some were concerned about postdetection management, questioning the point of an early detection of dementia-causing diseases if an effective intervention cannot be provided and feared this would only lead to increased anxiety in patients. Health care professionals also expressed mixed opinions on who should be screened for early detection. Some suggested it should be available to everyone to mitigate the chance of excluding those who are not in touch with their health care or are digitally excluded. Others were concerned about the resources that would be required to make the technology available to everyone. CONCLUSIONS This study highlights the need to design digital health technology in a way that is accessible to all and does not add burden to health care professionals. Further work is needed to ensure inclusive strategies are used in digital research to promote health equity.
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Affiliation(s)
- Sarah Wilson
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Clare Tolley
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Riona Mc Ardle
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Beswick
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sarah P Slight
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
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15
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Tang L, Zhang Z, Feng F, Yang LZ, Li H. Explainable Alzheimer's Disease Detection Using Linguistic Features from Automatic Speech Recognition. Dement Geriatr Cogn Disord 2023; 52:240-248. [PMID: 37433284 DOI: 10.1159/000531818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection. METHODS We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance. RESULTS Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively. CONCLUSION Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.
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Affiliation(s)
- Lijuan Tang
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Zhenglin Zhang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
- University of Science and Technology of China, Hefei, China
| | - Feifan Feng
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Department of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Li-Zhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
- University of Science and Technology of China, Hefei, China
| | - Hai Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
- University of Science and Technology of China, Hefei, China
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16
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Donaghy PC, Carrarini C, Ferreira D, Habich A, Aarsland D, Babiloni C, Bayram E, Kane JP, Lewis SJ, Pilotto A, Thomas AJ, Bonanni L. Research diagnostic criteria for mild cognitive impairment with Lewy bodies: A systematic review and meta-analysis. Alzheimers Dement 2023; 19:3186-3202. [PMID: 37096339 PMCID: PMC10695683 DOI: 10.1002/alz.13105] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/26/2023]
Abstract
INTRODUCTION Operationalized research criteria for mild cognitive impairment with Lewy bodies (MCI-LB) were published in 2020. The aim of this systematic review and meta-analysis was to review the evidence for the diagnostic clinical features and biomarkers in MCI-LB set out in the criteria. METHODS MEDLINE, PubMed, and Embase were searched on 9/28/22 for relevant articles. Articles were included if they presented original data reporting the rates of diagnostic features in MCI-LB. RESULTS Fifty-seven articles were included. The meta-analysis supported the inclusion of the current clinical features in the diagnostic criteria. Evidence for striatal dopaminergic imaging and meta-iodobenzylguanidine cardiac scintigraphy, though limited, supports their inclusion. Quantitative electroencephalogram (EEG) and fluorodeoxyglucose positron emission tomography (PET) show promise as diagnostic biomarkers. DISCUSSION The available evidence largely supports the current diagnostic criteria for MCI-LB. Further evidence will help refine the diagnostic criteria and understand how best to apply them in clinical practice and research. HIGHLIGHTS A meta-analysis of the diagnostic features of MCI-LB was carried out. The four core clinical features were more common in MCI-LB than MCI-AD/stable MCI. Neuropsychiatric and autonomic features were also more common in MCI-LB. More evidence is needed for the proposed biomarkers. FDG-PET and quantitative EEG show promise as diagnostic biomarkers in MCI-LB.
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Affiliation(s)
- Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Claudia Carrarini
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
- IRCCS San Raffaele Pisana, Rome, Italy
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Centre for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
- Hospital San Raffaele of Cassino, Cassino, Italy
| | - Ece Bayram
- Parkinson and Other Movement Disorders Center, Department of Neurosciences, University of California San Diego, California, USA
| | - Joseph Pm Kane
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Simon Jg Lewis
- Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, Australia
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, Neurology Unit, University of Brescia, Brescia, Italy
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
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Wu CC, Su CH, Islam MM, Liao MH. Artificial Intelligence in Dementia: A Bibliometric Study. Diagnostics (Basel) 2023; 13:2109. [PMID: 37371004 DOI: 10.3390/diagnostics13122109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/10/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer's Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018-2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.
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Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health Technology, Ming Chuan University, Taipei 333, Taiwan
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan
| | - Chun-Hsien Su
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan
- Graduate Institute of Sports Coaching Science, College of Kinesiology and Health, Chinese Culture University, Taipei 11114, Taiwan
| | | | - Mao-Hung Liao
- Superintendent Office, Yonghe Cardinal Tien Hospital, New Taipei City 23148, Taiwan
- Department of Healthcare Administration, Asia Eastern University of Science and Technology, Banciao District, New Taipei City 220303, Taiwan
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18
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Assi AA, Farrag MMY, Badary DM, Allam EAH, Nicola MA. Protective effects of curcumin and Ginkgo biloba extract combination on a new model of Alzheimer's disease. Inflammopharmacology 2023; 31:1449-1464. [PMID: 36856916 PMCID: PMC10229698 DOI: 10.1007/s10787-023-01164-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023]
Abstract
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative illnesses, and yet, no workable treatments have been discovered to prevent or reverse AD. Curcumin (CUR), the major polyphenolic compound of turmeric (Curcuma longa) rhizomes, and Ginkgo biloba extract (GBE) are natural substances derived from conventional Chinese herbs that have long been shown to provide therapeutic advantages for AD. The uptake of curcumin into the brain is severely restricted by its low ability to cross the blood-brain barrier (BBB). Meanwhile, GBE has been shown to improve BBB permeability. The present study evaluated the neuroprotective effects and pharmacokinetic profile of curcumin and GBE combination to find out whether GBE can enhance curcumin's beneficial effects in AD by raising its brain concentration. Results revealed that CUR + GBE achieved significantly higher levels of curcumin in the brain and plasma after 30 min and 1 h of oral administration, compared to curcumin alone, and this was confirmed by reversed phase high-performance liquid chromatography (RP-HPLC). The effect of combined oral treatment, for 28 successive days, on cognitive function and other AD-like alterations was studied in scopolamine-heavy metal mixtures (SCO + HMM) AD model in rats. The combination reversed at least, partially on the learning and memory impairment induced by SCO + HMM. This was associated with a more pronounced inhibitory effect on acetylcholinesterase (AChE), caspase-3, hippocampal amyloid beta (Aβ1-42), and phosphorylated tau protein (p-tau) count, and pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukine-1beta (IL-1β), as compared to the curcumin alone-treated group. Additionally, the combined treatment significantly decreased lipid peroxidation (MDA) and increased levels of reduced glutathione (GSH), when compared with the curcumin alone. These findings support the concept that the combination strategy might be an alternative therapy in the management/prevention of neurological disorders. This study sheds light on a new approach for exploring new phyto-therapies for AD and emphasizes that more research should focus on the synergic effects of herbal drugs in future.
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Affiliation(s)
- Abdel-Azim Assi
- Department of Pharmacology, Faculty of Medicine, Assiut University, Assiut, Egypt, 71524
| | - Magda M Y Farrag
- Department of Pharmacology, Faculty of Medicine, Assiut University, Assiut, Egypt, 71524
| | - Dalia M Badary
- Pathology Department, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Essmat A H Allam
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, 71526, Egypt
| | - Mariam A Nicola
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, 71526, Egypt.
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19
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Xu X, Lin L, Sun S, Wu S. A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer's disease using neuroimaging. Rev Neurosci 2023:revneuro-2022-0122. [PMID: 36729918 DOI: 10.1515/revneuro-2022-0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/02/2023] [Indexed: 02/03/2023]
Abstract
Alzheimer's disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.
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Affiliation(s)
- Xinze Xu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
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Di X, Yin Y, Fu Y, Mo Z, Lo SH, DiGuiseppi C, Eby DW, Hill L, Mielenz TJ, Strogatz D, Kim M, Li G. Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score. Artif Intell Med 2023; 138:102510. [PMID: 36990588 DOI: 10.1016/j.artmed.2023.102510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/04/2023] [Accepted: 02/09/2023] [Indexed: 02/22/2023]
Abstract
Several recent studies indicate that atypical changes in driving behaviors appear to be early signs of mild cognitive impairment (MCI) and dementia. These studies, however, are limited by small sample sizes and short follow-up duration. This study aims to develop an interaction-based classification method building on a statistic named Influence Score (i.e., I-score) for prediction of MCI and dementia using naturalistic driving data collected from the Longitudinal Research on Aging Drivers (LongROAD) project. Naturalistic driving trajectories were collected through in-vehicle recording devices for up to 44 months from 2977 participants who were cognitively intact at the time of enrollment. These data were further processed and aggregated to generate 31 time-series driving variables. Because of high dimensional time-series features for driving variables, we used I-score for variable selection. I-score is a measure to evaluate variables' ability to predict and is proven to be effective in differentiating between noisy and predictive variables in big data. It is introduced here to select influential variable modules or groups that account for compound interactions among explanatory variables. It is explainable regarding to what extent variables and their interactions contribute to the predictiveness of a classifier. In addition, I-score boosts the performance of classifiers over imbalanced datasets due to its association with the F1 score. Using predictive variables selected by I-score, interaction-based residual blocks are constructed over top I-score modules to generate predictors and ensemble learning aggregates these predictors to boost the prediction of the overall classifier. Experiments using naturalistic driving data show that our proposed classification method achieves the best accuracy (96%) for predicting MCI and dementia, followed by random forest (93%) and logistic regression (88%). In terms of F1 score and AUC, our proposed classifier achieves 98% and 87%, respectively, followed by random forest (with an F1 score of 96% and an AUC of 79%) and logistic regression (with an F1 score of 92% and an AUC of 77%). The results indicate that incorporating I-score into machine learning algorithms could considerably improve the model performance for predicting MCI and dementia in older drivers. We also performed the feature importance analysis and found that the right to left turn ratio and the number of hard braking events are the most important driving variables to predict MCI and dementia.
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21
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Korivand S, Jalili N, Gong J. Experiment protocols for brain-body imaging of locomotion: A systematic review. Front Neurosci 2023; 17:1051500. [PMID: 36937690 PMCID: PMC10014824 DOI: 10.3389/fnins.2023.1051500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/06/2023] [Indexed: 03/05/2023] Open
Abstract
Introduction Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings. Methods This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols. Results The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments. Discussion Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.
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Affiliation(s)
- Soroush Korivand
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, United States
- Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States
| | - Nader Jalili
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, United States
| | - Jiaqi Gong
- Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States
- *Correspondence: Jiaqi Gong
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22
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Clipped DeepControl: Deep neural network two-dimensional pulse design with an amplitude constraint layer. Artif Intell Med 2023; 135:102460. [PMID: 36628795 DOI: 10.1016/j.artmed.2022.102460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
Advanced radio-frequency pulse design used in magnetic resonance imaging has recently been demonstrated with deep learning of (convolutional) neural networks and reinforcement learning. For two-dimensionally selective radio-frequency pulses, the (convolutional) neural network pulse prediction time (a few milliseconds) was in comparison more than three orders of magnitude faster than the conventional optimal control computation. The network pulses were from the supervised training capable of compensating scan-subject dependent inhomogeneities of B0 and B1+ fields. Unfortunately, the network presented with a small percentage of pulse amplitude overshoots in the test subset, despite the optimal control pulses used in training were fully constrained. Here, we have extended the convolutional neural network with a custom-made clipping layer that completely eliminates the risk of pulse amplitude overshoots, while preserving the ability to compensate for the inhomogeneous field conditions.
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23
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Huang L, Li Y, Wu J, Chen N, Xia H, Guo Q. Shanghai Cognitive Screening: A Mobile Cognitive Assessment Tool Using Voice Recognition to Detect Mild Cognitive Impairment and Dementia in the Community. J Alzheimers Dis 2023; 95:227-236. [PMID: 37482999 DOI: 10.3233/jad-230277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
BACKGROUND A rapid digital instrument is needed to facilitate community-based screening of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in China. OBJECTIVE We developed a voice recognition-based cognitive assessment (Shanghai Cognitive Screening, SCS) on mobile devices and evaluated its diagnostic performance. METHODS Participants (N = 251) including healthy controls (N = 98), subjective cognitive decline (SCD, N = 42), MCI (N = 80), and mild AD (N = 31) were recruited from the memory clinic at Shanghai Sixth People's Hospital. The SCS is fully self-administered, takes about six minutes and measures the function of visual memory, language, and executive function. Participants were instructed to complete SCS tests, gold-standard neuropsychological tests and standardized structural 3T brain MRI. RESULTS The Cronbach's alpha was 0.910 of the overall scale, indicating high internal consistency. The SCS total score had an AUC of 0.921 to detect AD (sensitivity = 0.903, specificity = 0.945, positive predictive value = 0.700, negative predictive value = 0.986, likelihood ratio = 16.42, number needed for screening utility = 0.639), and an AUC of 0.838 to detect MCI (sensitivity = 0.793, specificity = 0.671, positive predictive value = 0.657, negative predictive value = 0.803, likelihood ratio = 2.41, number needed for screening utility = 0.944). The subtests demonstrated moderate to high correlations with the gold-standard tests from their respective cognitive domains. The SCS total score and its memory scores all correlated positively with relative volumes of the whole hippocampus and almost all subregions, after controlling for age, sex, and education. CONCLUSION The SCS has good diagnostic accuracy for detecting MCI and AD dementia and has the potential to facilitate large-scale screening in the general community.
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Affiliation(s)
- Lin Huang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yatian Li
- Center for Brain Science, Shanghai BestCovered Limited, Shanghai, China
| | - Jingnan Wu
- Center for Brain Science, Shanghai BestCovered Limited, Shanghai, China
| | - Nan Chen
- Center for Brain Science, Shanghai BestCovered Limited, Shanghai, China
| | - Huanhuan Xia
- Center for Brain Science, Shanghai BestCovered Limited, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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24
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Alty J, Bai Q, Li R, Lawler K, St George RJ, Hill E, Bindoff A, Garg S, Wang X, Huang G, Zhang K, Rudd KD, Bartlett L, Goldberg LR, Collins JM, Hinder MR, Naismith SL, Hogg DC, King AE, Vickers JC. The TAS Test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer's disease and estimate 5-year risks of cognitive decline and dementia. BMC Neurol 2022; 22:266. [PMID: 35850660 PMCID: PMC9289357 DOI: 10.1186/s12883-022-02772-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The worldwide prevalence of dementia is rapidly rising. Alzheimer's disease (AD), accounts for 70% of cases and has a 10-20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors. To optimise dementia prevention effectiveness, there is urgent need to identify individuals with preclinical AD for targeted risk reduction. Current preclinical AD tests are too invasive, specialist or costly for population-level assessments. We have developed a new online test, TAS Test, that assesses a range of motor-cognitive functions and has capacity to be delivered at significant scale. TAS Test combines two innovations: using hand movement analysis to detect preclinical AD, and computer-human interface technologies to enable robust 'self-testing' data collection. The aims are to validate TAS Test to [1] identify preclinical AD, and [2] predict risk of cognitive decline and AD dementia. METHODS Aim 1 will be addressed through a cross-sectional study of 500 cognitively healthy older adults, who will complete TAS Test items comprising measures of motor control, processing speed, attention, visuospatial ability, memory and language. TAS Test measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 (p-tau181). Aim 2 will be addressed through a 5-year prospective cohort study of 10,000 older adults. Participants will complete TAS Test annually and subtests of the Cambridge Neuropsychological Test Battery (CANTAB) biennially. 300 participants will undergo in-person clinical assessments. We will use machine learning of motor-cognitive performance on TAS Test to develop an algorithm that classifies preclinical AD risk (p-tau181-defined) and determine the precision to prospectively estimate 5-year risks of cognitive decline and AD. DISCUSSION This study will establish the precision of TAS Test to identify preclinical AD and estimate risk of cognitive decline and AD. If accurate, TAS Test will provide a low-cost, accessible enrichment strategy to pre-screen individuals for their likelihood of AD pathology prior to more expensive tests such as blood or imaging biomarkers. This would have wide applications in public health initiatives and clinical trials. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT05194787 , 18 January 2022. Retrospectively registered.
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Affiliation(s)
- Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia. .,School of Medicine, University of Tasmania, Hobart, Australia. .,Royal Hobart Hospital, Hobart, Tasmania, Australia.
| | - Quan Bai
- School of Information and Communication Technologies, University of Tasmania, Hobart, Australia
| | - Renjie Li
- School of Information and Communication Technologies, University of Tasmania, Hobart, Australia
| | - Katherine Lawler
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia.,Royal Hobart Hospital, Hobart, Tasmania, Australia
| | - Rebecca J St George
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia.,School of Psychological Sciences, University of Tasmania, Hobart, Australia
| | - Edward Hill
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Aidan Bindoff
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Saurabh Garg
- School of Information and Communication Technologies, University of Tasmania, Hobart, Australia
| | - Xinyi Wang
- School of Information and Communication Technologies, University of Tasmania, Hobart, Australia
| | - Guan Huang
- School of Information and Communication Technologies, University of Tasmania, Hobart, Australia
| | - Kaining Zhang
- School of Information and Communication Technologies, University of Tasmania, Hobart, Australia
| | - Kaylee D Rudd
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Larissa Bartlett
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Lynette R Goldberg
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Jessica M Collins
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - Mark R Hinder
- School of Psychological Sciences, University of Tasmania, Hobart, Australia
| | - Sharon L Naismith
- Healthy Brain Ageing Program, University of Sydney, Sydney, Australia
| | - David C Hogg
- School of Computing, University of Leeds, Leeds, UK
| | - Anna E King
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
| | - James C Vickers
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Australia
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25
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Liu H. Applications of Artificial Intelligence to Popularize Legal Knowledge and Publicize the Impact on Adolescents' Mental Health Status. Front Psychiatry 2022; 13:902456. [PMID: 35722558 PMCID: PMC9199859 DOI: 10.3389/fpsyt.2022.902456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI) advancements have radically altered human production and daily living. When it comes to AI's quick rise, it facilitates the growth of China's citizens, and at the same moment, a lack of intelligence has led to several concerns regarding regulations and laws. Current investigations regarding AI on legal knowledge do not have consistent benefits in predicting adolescents' psychological status, performance, etc. The study's primary purpose is to examine the influence of AI on the legal profession and adolescent mental health using a novel cognitive fuzzy K-nearest neighbor (CF-KNN). Initially, the legal education datasets are gathered and are standardized in the pre-processing stage through the normalization technique to retrieve the unwanted noises or outliers. When normalized data are transformed into numerical features, they can be analyzed using a variational autoencoder (VAE) approach. Multi-gradient ant colony optimization (MG-ACO) is applied to select a proper subset of the features. Tree C4.5 (T-C4.5) and fitness-based logistic regression analysis (F-LRA) techniques assess the adolescent's mental health conditions. Finally, our proposed work's performance is examined and compared with classical techniques to gain our work with the greatest effectiveness. Findings are depicted in chart formation by employing the MATLAB tool.
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Affiliation(s)
- Hao Liu
- School of Law, Chongqing University, Chongqing, China
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