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Fromm D, Dalton SG, Brick A, Olaiya G, Hill S, Greenhouse J, MacWhinney B. The Case of the Cookie Jar: Differences in Typical Language Use in Dementia. J Alzheimers Dis 2024:JAD230844. [PMID: 38995772 DOI: 10.3233/jad-230844] [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: 07/14/2024]
Abstract
Background Findings from language sample analyses can provide efficient and effective indicators of cognitive impairment in older adults. Objective This study used newly automated core lexicon analyses of Cookie Theft picture descriptions to assess differences in typical use across three groups. Methods Participants included adults without diagnosed cognitive impairments (Control), adults diagnosed with Alzheimer's disease (ProbableAD), and adults diagnosed with mild cognitive impairment (MCI). Cookie Theft picture descriptions were transcribed and analyzed using CLAN. Results Results showed that the ProbableAD group used significantly fewer core lexicon words overall than the MCI and Control groups. For core lexicon content words (nouns, verbs), however, both the MCI and ProbableAD groups produced significantly fewer words than the Control group. The groups did not differ in their use of core lexicon function words. The ProbableAD group was also slower to produce most of the core lexicon words than the MCI and Control groups. The MCI group was slower than the Control group for only two of the core lexicon content words. All groups mentioned a core lexicon word in the top left quadrant of the picture early in the description. The ProbableAD group was then significantly slower than the other groups to mention a core lexicon word in the other quadrants. Conclusions This standard and simple-to-administer task reveals group differences in overall core lexicon scores and the amount of time until the speaker produces the key items. Clinicians and researchers can use these tools for both early assessment and measurement of change over time.
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Affiliation(s)
- Davida Fromm
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sarah Grace Dalton
- Department of Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA
| | - Alexander Brick
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Gbenuola Olaiya
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sophia Hill
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joel Greenhouse
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Brian MacWhinney
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
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Lopes da Cunha P, Ruiz F, Ferrante F, Sterpin LF, Ibáñez A, Slachevsky A, Matallana D, Martínez Á, Hesse E, García AM. Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia. PLoS One 2024; 19:e0304272. [PMID: 38843210 PMCID: PMC11156374 DOI: 10.1371/journal.pone.0304272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/09/2024] [Indexed: 06/09/2024] Open
Abstract
Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients' scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.
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Affiliation(s)
- Pamela Lopes da Cunha
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Fabián Ruiz
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
| | - Franco Ferrante
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires (FIUBA), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Lucas Federico Sterpin
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
| | - Agustín Ibáñez
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
- Latin American Brain Health (BrainLat) Institute, Universidad Adolfo Ibáñez, Santiago, Peñalolén, Región Metropolitana, Chile
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, United States of America
- Trinity College Dublin, Dublin, Ireland
| | - Andrea Slachevsky
- Faculty of Medicine, Neuroscience and East Neuroscience Departments, Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Program – Institute of Biomedical Sciences (ICBM), University of Chile, Santiago, Chile
- Geroscience Center for Brain Health and Metabolism (GERO), Providencia, Santiago, Chile
- Hospital del Salvador and Faculty of Medicine, Memory and Neuropsychiatric Center (CMYN), Neurology Department, University of Chile, Providencia, Santiago, Chile
- Departamento de Medicina, Servicio de Neurología, Clínica Alemana-Universidad del Desarrollo, Las Condes, Región Metropolitana, Chile
| | - Diana Matallana
- Facultad de Medicina, Departamento de Psiquiatría (Programa PhD Neurociencias), Instituto de Envejecimiento, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición, Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
- Departamento de Salud Mental, Hospital Universitario Santa Fe de Bogotá, Bogotá, Colombia
| | - Ángela Martínez
- Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia
| | - Eugenia Hesse
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
- Departamento de Matemática, Universidad de San Andres, Victoria, Buenos Aires, Argentina
| | - Adolfo M. García
- Cognitive Neuroscience Center, Universidad de San Andrés, Victoria, Buenos Aires, Argentina
- Latin American Brain Health (BrainLat) Institute, Universidad Adolfo Ibáñez, Santiago, Peñalolén, Región Metropolitana, Chile
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, United States of America
- Facultad de Humanidades, Departamento de Lingüística y Literatura, Universidad de Santiago de Chile, Estación Central, Santiago, Chile
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Ceyhan B, Bek S, Önal-Süzek T. Machine Learning-Based Prediction Models for Cognitive Decline Progression: A Comparative Study in Multilingual Settings Using Speech Analysis. JAR LIFE 2024; 13:43-50. [PMID: 38774270 PMCID: PMC11106089 DOI: 10.14283/jarlife.2024.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/02/2024] [Indexed: 05/24/2024]
Abstract
Background Mild cognitive impairment (MCI) is a condition commonly associated with dementia. Therefore, early prediction of progression from MCI to dementia is essential for preventing or alleviating cognitive decline. Given that dementia affects cognitive functions like language and speech, detecting disease progression through speech analysis can provide a cost-effective solution for patients and caregivers. Design-Participants In our study, we examined spontaneous speech (SS) and written Mini Mental Status Examination (MMSE) scores from a 60-patient dataset obtained from the Mugla University Dementia Outpatient Clinic (MUDC) and a 153-patient dataset from the Alzheimer's Dementia Recognition through Spontaneous Speech (ADRess) challenge. Our study, for the first time, analyzed the impact of audio features extracted from SS in distinguishing between different degrees of cognitive impairment using both an Indo-European language and a Turkic language, which exhibit distinct word order, agglutination, noun cases, and grammatical markers. Results When each machine learning model was tested on its respective trained language, we attained a 95% accuracy using the random forest classifier on the ADRess dataset and a 94% accuracy on the MUDC dataset employing the multilayer perceptron (MLP) neural network algorithm. In our second experiment, we evaluated the effectiveness of each language-specific machine learning model on the dataset of the other language. We achieved accuracies of 72% for English and 76% for Turkish, respectively. Conclusion These findings underscore the cross-language potential of audio features for automated tracking of cognitive impairment progression in MCI patients, offering a convenient and cost-effective option for clinicians or patients.
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Affiliation(s)
- B. Ceyhan
- Department of Bioinformatics, Graduate School of Natural and Applied Sciences, Mugla Sitki Kocman University, Mugla 48000, Türkiye
| | - S. Bek
- Department of Neurology, Faculty of Medicine, Mugla Sitki Kocman University, Mugla 48000, Türkiye
| | - T. Önal-Süzek
- Department of Bioinformatics, Graduate School of Natural and Applied Sciences, Mugla Sitki Kocman University, Mugla 48000, Türkiye
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Bonifazi G, Luchena C, Gaminde-Blasco A, Ortiz-Sanz C, Capetillo-Zarate E, Matute C, Alberdi E, De Pittà M. A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity. Neurobiol Dis 2024; 194:106473. [PMID: 38493903 DOI: 10.1016/j.nbd.2024.106473] [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: 10/26/2023] [Revised: 03/10/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024] Open
Abstract
The pathophysiological process of Alzheimer's disease (AD) is believed to begin many years before the formal diagnosis of AD dementia. This protracted preclinical phase offers a crucial window for potential therapeutic interventions, yet its comprehensive characterization remains elusive. Accumulating evidence suggests that amyloid-β (Aβ) may mediate neuronal hyperactivity in circuit dysfunction in the early stages of AD. At the same time, neural activity can also facilitate Aβ accumulation through intricate feed-forward interactions, complicating elucidating the conditions governing Aβ-dependent hyperactivity and its diagnostic utility. In this study, we use biophysical modeling to shed light on such conditions. Our analysis reveals that the inherently nonlinear nature of the underlying molecular interactions can give rise to the emergence of various modes of hyperactivity. This diversity in the mechanisms of hyperactivity may ultimately account for a spectrum of AD manifestations.
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Affiliation(s)
- Giulio Bonifazi
- Basque Center for Applied Mathematics, Alameda Mazarredo 14, Bilbao 48009, Bizkaia, Spain; Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto M5T 0S8, ON, Canada
| | - Celia Luchena
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Adhara Gaminde-Blasco
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Carolina Ortiz-Sanz
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Estibaliz Capetillo-Zarate
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Carlos Matute
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Elena Alberdi
- Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Achucarro Basque Center for Neuroscience, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain
| | - Maurizio De Pittà
- Basque Center for Applied Mathematics, Alameda Mazarredo 14, Bilbao 48009, Bizkaia, Spain; Department of Neurosciences, University of the Basque Country, Barrio Sarriena, s/n, Leioa 48940, Bizkaia, Spain; Krembil Research Institute, University Health Network, 60 Leonard Ave, Toronto M5T 0S8, ON, Canada; Department of Physiology, University of Toronto, 1 King's College Circle, Toronto M5S 1A8, ON, Canada.
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5
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García AM, Johann F, Echegoyen R, Calcaterra C, Riera P, Belloli L, Carrillo F. Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration. Behav Res Methods 2024; 56:2886-2900. [PMID: 37759106 PMCID: PMC11200269 DOI: 10.3758/s13428-023-02240-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here: https://demo.sci.tellapp.org/ .
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Affiliation(s)
- Adolfo M García
- Global Brain Health Institute, University of California, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina.
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.
- TELL Toolkit SA, Beethovenstraat, Netherlands.
| | - Fernando Johann
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- TELL Toolkit SA, Beethovenstraat, Netherlands
| | - Raúl Echegoyen
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- TELL Toolkit SA, Beethovenstraat, Netherlands
| | - Cecilia Calcaterra
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- TELL Toolkit SA, Beethovenstraat, Netherlands
| | - Pablo Riera
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Laouen Belloli
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Facundo Carrillo
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
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Lukic S, Fan Z, García AM, Welch AE, Ratnasiri BM, Wilson SM, Henry ML, Vonk J, Deleon J, Miller BL, Miller Z, Mandelli ML, Gorno-Tempini ML. Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech. Cortex 2024; 173:34-48. [PMID: 38359511 PMCID: PMC11246552 DOI: 10.1016/j.cortex.2023.12.013] [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: 05/24/2023] [Revised: 10/15/2023] [Accepted: 12/12/2023] [Indexed: 02/17/2024]
Abstract
Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.
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Affiliation(s)
- Sladjana Lukic
- University of California, San Francisco Memory and Aging Center, CA, USA; Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA.
| | - Zekai Fan
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Ariane E Welch
- Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA
| | | | - Stephen M Wilson
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Maya L Henry
- University of Texas at Austin Moody College of Communication, Austin, TX, USA
| | - Jet Vonk
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Jessica Deleon
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Bruce L Miller
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Zachary Miller
- University of California, San Francisco Memory and Aging Center, CA, USA
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Saunders S, Haider F, Ritchie CW, Muniz Terrera G, Luz S. Longitudinal observational cohort study: Speech for Intelligent cognition change tracking and DEtection of Alzheimer's Disease (SIDE-AD). BMJ Open 2024; 14:e082388. [PMID: 38548356 PMCID: PMC10982798 DOI: 10.1136/bmjopen-2023-082388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/19/2024] [Indexed: 04/02/2024] Open
Abstract
INTRODUCTION There is emerging evidence that speech may be a potential indicator and manifestation of early Alzheimer's disease (AD) pathology. Therefore, the University of Edinburgh and Sony Research have partnered to create the Speech for Intelligent cognition change tracking and DEtection of Alzheimer's Disease (SIDE-AD) study, which aims to develop digital speech-based biomarkers for use in neurodegenerative disease. METHODS AND ANALYSIS SIDE-AD is an observational longitudinal study, collecting samples of spontaneous speech. Participants are recruited from existing cohort studies as well as from the National Health Service (NHS)memory clinics in Scotland. Using an online platform, participants record a voice sample talking about their brain health and rate their mood, anxiety and apathy. The speech biomarkers will be analysed longitudinally, and we will use machine learning and natural language processing technology to automate the assessment of the respondents' speech patterns. ETHICS AND DISSEMINATION The SIDE-AD study has been approved by the NHS Research Ethics Committee (REC reference: 23/WM/0153, protocol number AC23046, IRAS Project ID 323311) and received NHS management approvals from Lothian, Fife and Forth Valley NHS boards. Our main ethical considerations pertain to the remote administration of the study, such as taking remote consent. To address this, we implemented a consent process, whereby the first step of the consent is done entirely remotely but a member of the research team contacts the participant over the phone to consent participants to the optional, most sensitive, elements of the study. Results will be presented at conferences, published in peer-reviewed journals and communicated to study participants.
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Affiliation(s)
| | - Fasih Haider
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Craig W Ritchie
- Department of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Scottish Brain Sciences, Edinburgh, UK
| | - Graciela Muniz Terrera
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Heritage College of Osteopathic Medicine, Ohio University, Athens, Ohio, USA
| | - Saturnino Luz
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh School of Molecular Genetic and Population Health Sciences, Edinburgh, UK
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Bayat S, Santai M, Panahi MM, Khodadadi A, Ghassimi M, Rezaei S, Besharat S, Mahboubi Z, Almasi M, Sanei Taheri M, Dickerson BC, Rezaii N. Language Abnormalities in Alzheimer's Disease Arise from Reduced Informativeness: A Cross-Linguistic Study in English and Persian. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304407. [PMID: 38562858 PMCID: PMC10984049 DOI: 10.1101/2024.03.19.24304407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
INTRODUCTION This research investigates the psycholinguistic origins of language impairments in Alzheimer's Disease (AD), questioning if these impairments result from language-specific structural disruptions or from a universal deficit in generating meaningful content. METHODS Cross-linguistic analysis was conducted on language samples from 184 English and 52 Persian speakers, comprising both AD patients and healthy controls, to extract various language features. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify informativeness. RESULTS Indicators of AD in English were found to be highly predictive of AD in Persian, with a 92.3% classification accuracy. Additionally, we found robust correlations between the typical linguistic abnormalities of AD and language emptiness (low LII) across both languages. DISCUSSION Findings suggest AD linguistics impairments are attributed to a core universal difficulty in generating informative messages. Our approach underscores the importance of incorporating biocultural diversity into research, fostering the development of inclusive diagnostic tools.
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LUZ SATURNINO, HAIDER FASIH, FROMM DAVIDA, LAZAROU IOULIETTA, KOMPATSIARIS IOANNIS, MACWHINNEY BRIAN. An Overview of the ADReSS-M Signal Processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition Through Spontaneous Speech. IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2024; 5:738-749. [PMID: 38957540 PMCID: PMC11218814 DOI: 10.1109/ojsp.2024.3378595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.
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Affiliation(s)
- SATURNINO LUZ
- Usher Institute, Edinburgh Medical School, The University of Edinburgh, EH16 4UX Edinburgh, U.K
| | - FASIH HAIDER
- School of Engineering, The University of Edinburgh, EH9 3JW Edinburgh, U.K
| | - DAVIDA FROMM
- Department of Psychology, Carnegie Mellon University, Pittsburgh 15213, PA USA
| | - IOULIETTA LAZAROU
- Information Technologies Institute, CERTH, Thessaloniki, Thermi-Thessaloniki 57001, Greece
| | - IOANNIS KOMPATSIARIS
- Information Technologies Institute, CERTH, Thessaloniki, Thermi-Thessaloniki 57001, Greece
| | - BRIAN MACWHINNEY
- Department of Psychology, Carnegie Mellon University, Pittsburgh 15213, PA USA
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10
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Gagliardi G. Natural language processing techniques for studying language in pathological ageing: A scoping review. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:110-122. [PMID: 36960885 DOI: 10.1111/1460-6984.12870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). AIMS To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. METHODS & PROCEDURES We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. MAIN CONTRIBUTION The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). CONCLUSIONS & IMPLICATIONS Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice. WHAT THIS PAPER ADDS What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.
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Affiliation(s)
- Gloria Gagliardi
- Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy
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11
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Park S, Lee ES, Shin KS, Lee JE, Ye JC. Self-supervised multi-modal training from uncurated images and reports enables monitoring AI in radiology. Med Image Anal 2024; 91:103021. [PMID: 37952385 DOI: 10.1016/j.media.2023.103021] [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: 05/11/2023] [Revised: 08/14/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
The escalating demand for artificial intelligence (AI) systems that can monitor and supervise human errors and abnormalities in healthcare presents unique challenges. Recent advances in vision-language models reveal the challenges of monitoring AI by understanding both visual and textual concepts and their semantic correspondences. However, there has been limited success in the application of vision-language models in the medical domain. Current vision-language models and learning strategies for photographic images and captions call for a web-scale data corpus of image and text pairs which is not often feasible in the medical domain. To address this, we present a model named medical cross-attention vision-language model (Medical X-VL), which leverages key components to be tailored for the medical domain. The model is based on the following components: self-supervised unimodal models in medical domain and a fusion encoder to bridge them, momentum distillation, sentencewise contrastive learning for medical reports, and sentence similarity-adjusted hard negative mining. We experimentally demonstrated that our model enables various zero-shot tasks for monitoring AI, ranging from the zero-shot classification to zero-shot error correction. Our model outperformed current state-of-the-art models in two medical image datasets, suggesting a novel clinical application of our monitoring AI model to alleviate human errors. Our method demonstrates a more specialized capacity for fine-grained understanding, which presents a distinct advantage particularly applicable to the medical domain.
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Affiliation(s)
- Sangjoon Park
- Department of Radiation Oncology, Yonsei College of Medicine, Seoul, Republic of Korea
| | - Eun Sun Lee
- Chung-Ang University Hospital, Seoul, Republic of Korea.
| | - Kyung Sook Shin
- Department of Radiology, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Jeong Eun Lee
- Department of Radiology, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea.
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
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Xu F, Zheng Q, Shi J, Yan K, Wang M. Pre-training and ensembling based Alzheimer's disease detection. Technol Health Care 2024; 32:379-395. [PMID: 37545287 DOI: 10.3233/thc-230571] [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: 08/08/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) endangers the physical and mental health of the elderly, constituting one of the most crucial social challenges. Due to lack of effective AD intervention drugs, it is very important to diagnose AD in the early stage, especially in the Mild Cognitive Impairment (MCI) phase. OBJECTIVE At present, an automatic classification technology is urgently needed to assist doctors in analyzing the status of the candidate patient. The artificial intelligence enhanced Alzheimer's disease detection can reduce costs to detect Alzheimer's disease. METHODS In this paper, a novel pre-trained ensemble-based AD detection (PEADD) framework with three base learners (i.e., ResNet, VGG, and EfficientNet) for both the audio-based and PET (Positron Emission Tomography)-based AD detection is proposed under a unified image modality. Specifically, the effectiveness of context-enriched image modalities instead of the traditional speech modality (i.e., context-free audio matrix) for the audio-based AD detection, along with simple and efficient image denoising strategy has been inspected comprehensively. Meanwhile, the PET-based AD detection based on the denoised PET image has been described. Furthermore, different voting methods for applying an ensemble strategy (i.e., hard voting and soft voting) has been investigated in detail. RESULTS The results showed that the classification accuracy was 92% and 99% on the audio-based and PET-based AD datasets, respectively. Our extensive experimental results demonstrate that our PEADD outperforms the state-of-the-art methods on both audio-based and PET-based AD datasets simultaneously. CONCLUSIONS The network model can provide an objective basis for doctors to detect Alzheimer's Disease.
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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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Al-Hammadi M, Fleyeh H, Åberg AC, Halvorsen K, Thomas I. Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review. J Alzheimers Dis 2024; 100:1-27. [PMID: 38848181 DOI: 10.3233/jad-231459] [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: 06/09/2024]
Abstract
Background Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.
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Affiliation(s)
- Mustafa Al-Hammadi
- School of Information and Engineering, Dalarna University, Falun, Sweden
| | - Hasan Fleyeh
- School of Information and Engineering, Dalarna University, Falun, Sweden
| | - Anna Cristina Åberg
- School of Health and Welfare, Dalarna University, Falun, Sweden
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | | | - Ilias Thomas
- School of Information and Engineering, Dalarna University, Falun, Sweden
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Halder D, Das S, R S J, Joseph A. Role of multi-targeted bioactive natural molecules and their derivatives in the treatment of Alzheimer's disease: an insight into structure-activity relationship. J Biomol Struct Dyn 2023; 41:11286-11323. [PMID: 36579430 DOI: 10.1080/07391102.2022.2158136] [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: 09/13/2022] [Accepted: 12/07/2022] [Indexed: 12/30/2022]
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder involving cognitive dysfunction like short-term memory and behavioral changes as the disease progresses due to other unaltered physiological factors. The solution for this problem is Multi-targeted Drugs (MTDs), which can affect multiple determinants to realize the multifunctional effects. Acetylcholinesterase (AChE) inhibitors donepezil, rivastigmine, galantamine, and N-methyl-D-aspartate (NMDA) receptor antagonist memantine are FDA-approved drugs used to treat AD symptomatically. The key objective of this review is to understand multitargeted bioactive natural molecules that could be considered as leads for further development as effective drugs for treating AD, along with understanding its pharmacology and structure-activity relationship (SAR). Understanding the molecular mechanism of the AD pathophysiology, the role of existing drugs, treatment of AD via amyloid beta (Aβ) plaque, and neurofibrillary tangle (NFT) inhibition by natural bioactive molecules were also discussed in the review. The current quest and recent advancements with natural bioactive compounds like physostigmine, resveratrol, curcumin, and catechins, along with the study of in silico SAR, were reported in the present study. This review summarises the structural properties required for bioactive natural molecules to show anti-Alzheimer's activity by emphasizing on SAR of several bioactive natural molecules targeting various AD pathologies, their key molecular interactions that are critical for target specificity, their role as multitargeted ligands, used with adjunctive therapy for AD followed by related US patents granted recently. This article highlights the significance of the structural features of natural bioactive molecules in the treatment of AD and establishes a connection between them.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Debojyoti Halder
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Subham Das
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Jeyaprakash R S
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Alex Joseph
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
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García AM, de Leon J, Tee BL, Blasi DE, Gorno-Tempini ML. Speech and language markers of neurodegeneration: a call for global equity. Brain 2023; 146:4870-4879. [PMID: 37497623 PMCID: PMC10690018 DOI: 10.1093/brain/awad253] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/29/2023] [Accepted: 07/15/2023] [Indexed: 07/28/2023] Open
Abstract
In the field of neurodegeneration, speech and language assessments are useful for diagnosing aphasic syndromes and for characterizing other disorders. As a complement to classic tests, scalable and low-cost digital tools can capture relevant anomalies automatically, potentially supporting the quest for globally equitable markers of brain health. However, this promise remains unfulfilled due to limited linguistic diversity in scientific works and clinical instruments. Here we argue for cross-linguistic research as a core strategy to counter this problem. First, we survey the contributions of linguistic assessments in the study of primary progressive aphasia and the three most prevalent neurodegenerative disorders worldwide-Alzheimer's disease, Parkinson's disease, and behavioural variant frontotemporal dementia. Second, we address two forms of linguistic unfairness in the literature: the neglect of most of the world's 7000 languages and the preponderance of English-speaking cohorts. Third, we review studies showing that linguistic dysfunctions in a given disorder may vary depending on the patient's language and that English speakers offer a suboptimal benchmark for other language groups. Finally, we highlight different approaches, tools and initiatives for cross-linguistic research, identifying core challenges for their deployment. Overall, we seek to inspire timely actions to counter a looming source of inequity in behavioural neurology.
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Affiliation(s)
- Adolfo M García
- Global Brain Health Institute, University of California, San Francisco, CA 94143, USA
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires B1644BID, Argentina
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago 9160000, Chile
- Latin American Brain Health (BrainLat) Institute, Universidad Adolfo Ibáñez, Avenida Diagonal Las Torres 2640 (7941169), Santiago, Peñalolén, Región Metropolitana, Chile
| | - Jessica de Leon
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94143, USA
| | - Boon Lead Tee
- Global Brain Health Institute, University of California, San Francisco, CA 94143, USA
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94143, USA
| | - Damián E Blasi
- Data Science Initiative, Harvard University, Cambridge, MA 02138, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena 07745, Germany
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94143, USA
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Shi M, Cheung G, Shahamiri SR. Speech and language processing with deep learning for dementia diagnosis: A systematic review. Psychiatry Res 2023; 329:115538. [PMID: 37864994 DOI: 10.1016/j.psychres.2023.115538] [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: 04/20/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its progression or reduce its burdens. However, the diagnostic process of dementia is often complex and resource intensive. Access to diagnostic services is also an issue in low and middle-income countries. The abundance and easy accessibility of speech and language data have created new possibilities for utilizing Deep Learning (DL) technologies to be part of the dementia diagnostic process. This systematic review included studies published between 2012-2022 that utilized such technologies to aid in diagnosing dementia. We identified 72 studies using the PRISMA 2020 protocol, extracted and analyzed data from these studies and reported the related DL technologies. We found these technologies effectively differentiated between healthy individuals and those with a dementia diagnosis, highlighting their potential in the diagnosis of dementia. This systematic review provides insights into the contributions of DL-based speech and language techniques to support the dementia diagnostic process. It also offers an understanding of the advancements made in this field thus far and highlights some challenges that still need to be addressed.
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Affiliation(s)
- Mengke Shi
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand
| | - Gary Cheung
- Department of Psychological Medicine, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand
| | - Seyed Reza Shahamiri
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand.
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Asllani B, Mullen DM. Using personal writings to detect dementia: A text mining approach. Health Informatics J 2023; 29:14604582231204409. [PMID: 37800542 DOI: 10.1177/14604582231204409] [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: 10/07/2023]
Abstract
A novel text mining pilot for dementia detection using Linguistic Inquiry and Word Count (LIWC) was tested on public figures' writings looking at word choice and affect compared to those with and without dementia. The differences found in this analysis mirror the expected patterns where writings of people with dementia reflect significantly more analytical thinking words, but significantly less authentic and emotional tone. In addition, the analysis found that people with dementia use significantly less functional words, such as grammar, and affections (happiness, sadness, anger, sadness), but tend to use significantly more pronouns in their writings. Written samples of those with dementia also use significantly less time-oriented words that indicate past, present, or future. The analysis of free form text suggests a potential avenue for detecting early changes that correlate with dementia, allowing for early preventative treatment before noticeable cognitive impairment occurs.
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Affiliation(s)
- Beni Asllani
- Department of Management, The University of Tennessee at Chattanooga, Gary W. Rollins College of Business, Chattanooga, TN, USA
| | - Deborah M Mullen
- Department of Management, The University of Tennessee at Chattanooga, Gary W. Rollins College of Business, Chattanooga, TN, USA
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Nashwan AJ, Gharib S, Alhadidi M, El-Ashry AM, Alamgir A, Al-Hassan M, Khedr MA, Dawood S, Abufarsakh B. Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care. Issues Ment Health Nurs 2023; 44:1020-1034. [PMID: 37850937 DOI: 10.1080/01612840.2023.2263579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.
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Affiliation(s)
- Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation, Doha, Qatar
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Suzan Gharib
- Nursing Department, Al-Khaldi Hospital, Amman, Jordan
| | - Majdi Alhadidi
- Psychiatric & Mental Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | | | | | | | | | - Shaimaa Dawood
- Faculty of Nursing, Alexandria University, Alexandria, Egypt
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Triantafyllopoulos A, Kathan A, Baird A, Christ L, Gebhard A, Gerczuk M, Karas V, Hübner T, Jing X, Liu S, Mallol-Ragolta A, Milling M, Ottl S, Semertzidou A, Rajamani ST, Yan T, Yang Z, Dineley J, Amiriparian S, Bartl-Pokorny KD, Batliner A, Pokorny FB, Schuller BW. HEAR4Health: a blueprint for making computer audition a staple of modern healthcare. Front Digit Health 2023; 5:1196079. [PMID: 37767523 PMCID: PMC10520966 DOI: 10.3389/fdgth.2023.1196079] [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: 03/29/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.
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Affiliation(s)
- Andreas Triantafyllopoulos
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alexander Kathan
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alice Baird
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Lukas Christ
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alexander Gebhard
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Maurice Gerczuk
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Vincent Karas
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Tobias Hübner
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Xin Jing
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Shuo Liu
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Adria Mallol-Ragolta
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
| | - Manuel Milling
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Sandra Ottl
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Anastasia Semertzidou
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | | | - Tianhao Yan
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Zijiang Yang
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Judith Dineley
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Shahin Amiriparian
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Katrin D. Bartl-Pokorny
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Anton Batliner
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Florian B. Pokorny
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
| | - Björn W. Schuller
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
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21
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He R, Chapin K, Al-Tamimi J, Bel N, Marquié M, Rosende-Roca M, Pytel V, Tartari JP, Alegret M, Sanabria A, Ruiz A, Boada M, Valero S, Hinzen W. Automated Classification of Cognitive Decline and Probable Alzheimer's Dementia Across Multiple Speech and Language Domains. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2023; 32:2075-2086. [PMID: 37486774 DOI: 10.1044/2023_ajslp-22-00403] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
BACKGROUND Decline in language has emerged as a new potential biomarker for the early detection of Alzheimer's disease (AD). It remains unclear how sensitive language measures are across different tasks, language domains, and languages, and to what extent changes can be reliably detected in early stages such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHOD Using a scene construction task for speech elicitation in a new Spanish/Catalan speaking cohort (N = 119), we automatically extracted features across seven domains, three acoustic (spectral, cepstral, and voice quality), one prosodic, and three from text (morpholexical, semantic, and syntactic). They were forwarded to a random forest classifier to evaluate the discriminability of participants with probable AD dementia, amnestic and nonamnestic MCI, SCD, and cognitively healthy controls. Repeated-measures analyses of variance and paired-samples Wilcoxon signed-ranks test were used to assess whether and how performance differs significantly across groups and linguistic domains. RESULTS The performance scores of the machine learning classifier were generally satisfactorily high, with the highest scores over .9. Model performance was significantly different for linguistic domains (p < .001), and speech versus text (p = .043), with speech features outperforming textual features, and voice quality performing best. High diagnostic classification accuracies were seen even within both cognitively healthy (controls vs. SCD) and MCI (amnestic and nonamnestic) groups. CONCLUSION Speech-based machine learning is powerful in detecting cognitive decline and probable AD dementia across a range of different feature domains, though important differences exist between these domains as well. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.23699733.
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Affiliation(s)
- Rui He
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Kayla Chapin
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jalal Al-Tamimi
- Laboratoire de Linguistique Formelle (LLF), CNRS, Université Paris Cité, France
| | - Núria Bel
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Maitee Rosende-Roca
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
| | - Juan Pablo Tartari
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
| | - Montse Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Angela Sanabria
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Wolfram Hinzen
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
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22
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Ghasempour Dabbaghi K, Khosravirad Z, Jamalnia S, GhorbaniNia R, Mahmoudikohani F, Zakeri H, Khastehband S. The Use of Artificial Intelligence in the Management of Neurodegenerative Disorders; Focus on Alzheimer's Disease. Galen Med J 2023; 12:1-7. [PMID: 38827644 PMCID: PMC11144027 DOI: 10.31661/gmj.v12i.3061] [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: 04/20/2023] [Indexed: 06/04/2024] Open
Abstract
Recent advances in artificial intelligence (AI) have shown great promise in the diagnosis, prediction, treatment plans, and monitoring of neurodegenerative disorders. AI algorithms can analyze huge quantities of data from numerous sources, including medical images, quantifiable proteins in urine, blood, and cerebrospinal fluid (CSF), genetic information, clinical records, electroencephalography (EEG) signals, driving behaviors, and so forth. Alzheimer's disease (AD) is one of the most common neurodegenerative disorders that progressively damage cognitive abilities and memory. This study specifically explores the possible application of AI in the diagnosis, prediction, monitoring, biomarker or drug discovery, and classification of AD.
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Affiliation(s)
| | | | - Sheida Jamalnia
- Department of Nursing and Midwifery, Kazeroun Branch, Islamic Azad University,
Kazeroun, Iran
| | - Rahil GhorbaniNia
- Noncommunicable Disease Research Center, Bam University of Medical Science, Bam, Iran
| | - Fatemeh Mahmoudikohani
- Department of Midwifery, School of Nursing and Midwifery, Bam University of Medical
Science, Bam, Iran
| | - Habib Zakeri
- Research Center for Neuromodulation and Pain, NAB Pain Clinic, Shiraz University of
Medical Sciences, Shiraz, Iran
| | - Solmaz Khastehband
- Department of Educational Management, Islamic Azad University-south Tehran Branch,
Tehran, Iran
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23
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Qi X, Zhou Q, Dong J, Bao W. Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review. Front Aging Neurosci 2023; 15:1224723. [PMID: 37693647 PMCID: PMC10484224 DOI: 10.3389/fnagi.2023.1224723] [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: 05/18/2023] [Accepted: 08/04/2023] [Indexed: 09/12/2023] Open
Abstract
Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.
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Affiliation(s)
- Xiaoke Qi
- School of Information Management for Law, China University of Political Science and Law, Beijing, China
| | | | - Jian Dong
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
| | - Wei Bao
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
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24
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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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25
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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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26
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Opwonya J, Ku B, Lee KH, Kim JI, Kim JU. Eye movement changes as an indicator of mild cognitive impairment. Front Neurosci 2023; 17:1171417. [PMID: 37397453 PMCID: PMC10307957 DOI: 10.3389/fnins.2023.1171417] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 05/23/2023] [Indexed: 07/04/2023] Open
Abstract
Background Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data. Methods We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics' odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC). Results LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840. Conclusion Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.
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Affiliation(s)
- Julius Opwonya
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
- KM Convergence Science, University of Science and Technology, Daejeon, South Korea
| | - Boncho Ku
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s Disease and Related Dementias (GARD) Cohort Research Center, Chosun University, Gwangju, South Korea
- Department of Biomedical Science, Chosun University, Gwangju, South Korea
- Dementia Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Joong Il Kim
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Jaeuk U. Kim
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
- KM Convergence Science, University of Science and Technology, Daejeon, South Korea
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27
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Eisenstein M. Listening for neurological symptoms. Nature 2023:10.1038/d41586-023-01650-5. [PMID: 37225807 DOI: 10.1038/d41586-023-01650-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
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28
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Robin J, Xu M, Balagopalan A, Novikova J, Kahn L, Oday A, Hejrati M, Hashemifar S, Negahdar M, Simpson W, Teng E. Automated detection of progressive speech changes in early Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12445. [PMID: 37361261 PMCID: PMC10286224 DOI: 10.1002/dad2.12445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/21/2023] [Accepted: 04/27/2023] [Indexed: 06/28/2023]
Abstract
Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech-based composite scores could be used to monitor change and detect response to treatment in future research. HIGHLIGHTS Longitudinal speech samples were analyzed to characterize speech changes in early AD.Acoustic and linguistic measures showed significant change over 18 months.A novel speech composite score was computed to characterize longitudinal change.The speech composite correlated with primary and secondary trial endpoints.Automated speech analysis could facilitate remote, high frequency monitoring in AD.
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Affiliation(s)
| | - Mengdan Xu
- Winterlight Labs Inc.TorontoOntarioCanada
| | - Aparna Balagopalan
- Winterlight Labs Inc.TorontoOntarioCanada
- Massachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
| | | | - Laura Kahn
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
- Present address:
ReCode Therapeutics, Menlo ParkCaliforniaUSA
| | - Abdi Oday
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
| | - Mohsen Hejrati
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
| | | | | | | | - Edmond Teng
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
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29
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Petti U, Baker S, Korhonen A, Robin J. How Much Speech Data Is Needed for Tracking Language Change in Alzheimer's Disease? A Comparison of Random Length, 5-Min, and 1-Min Spontaneous Speech Samples. Digit Biomark 2023; 7:157-166. [PMID: 38029002 PMCID: PMC10673351 DOI: 10.1159/000533423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 07/28/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Changes in speech can act as biomarkers of cognitive decline in Alzheimer's disease (AD). While shorter speech samples would promote data collection and analysis, the minimum length of informative speech samples remains debated. This study aims to provide insight into the effect of sample length in analyzing longitudinal recordings of spontaneous speech in AD by comparing the original random length, 5- and 1-minute-long samples. We hope to understand whether capping the audio improves the accuracy of the analysis, and whether an extra 4 min conveys necessary information. Methods 110 spontaneous speech samples were collected from decades of Youtube videos of 17 public figures, 9 of whom eventually developed AD. 456 language features were extracted and their text-length-sensitivity, comparability, and ability to capture change over time were analyzed across three different sample lengths. Results Capped audio files had advantages over the random length ones. While most extracted features were statistically comparable or highly correlated across the datasets, potential effects of sample length should be acknowledged for some features. The 5-min dataset presented the highest reliability in tracking the evolution of the disease, suggesting that the 4 extra minutes do convey informative data. Conclusion Sample length seems to play an important role in extracting the language feature values from speech and tracking disease progress over time. We highlight the importance of further research into optimal sample length and standardization of methods when studying speech in AD.
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Affiliation(s)
- Ulla Petti
- Language Technology Lab, University of Cambridge, Cambridge, UK
| | - Simon Baker
- Language Technology Lab, University of Cambridge, Cambridge, UK
| | - Anna Korhonen
- Language Technology Lab, University of Cambridge, Cambridge, UK
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30
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Igarashi T, Umeda-Kameyama Y, Kojima T, Akishita M, Nihei M. Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models. Front Med (Lausanne) 2023; 10:1145314. [PMID: 37153095 PMCID: PMC10162011 DOI: 10.3389/fmed.2023.1145314] [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: 01/16/2023] [Accepted: 03/02/2023] [Indexed: 05/09/2023] Open
Abstract
In this article, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the developed interview items and the accuracy of the natural language processing model, we recruited participants with the approval of the University of Tokyo Hospital and obtained the cooperation of 29 participants (7 men and 22 women) aged 72-91 years. Based on the MMSE results, a multilevel classification model was created to classify the three groups, and a binary classification model to sort the two groups. For each of these models, we tested whether the accuracy would improve when text augmentation was performed. The accuracy in the multi-level classification results for the test data was 0.405 without augmentation and 0.991 with augmentation. The accuracy of the test data in the results of the binary classification without augmentation was 0.488 for the moderate dementia and mild dementia groups, 0.767 for the moderate dementia and MCI groups, and 0.700 for the mild dementia and MCI groups. In contrast, the accuracy of the test data in the augmented binary classification results was 0.972 for moderate dementia and mild dementia groups, 0.996 for moderate dementia and MCI groups, and 0.985 for mild dementia and MCI groups.
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Affiliation(s)
- Toshiharu Igarashi
- Department of Human and Engineered Environmental Studies, The University of Tokyo, Kashiwa, Japan
- *Correspondence: Toshiharu Igarashi,
| | - Yumi Umeda-Kameyama
- Department of Geriatric Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Taro Kojima
- Department of Geriatric Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Masahiro Akishita
- Department of Geriatric Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Misato Nihei
- Department of Human and Engineered Environmental Studies, The University of Tokyo, Kashiwa, Japan
- Institute of Gerontology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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32
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Agbavor F, Liang H. Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer's Disease Using Voice. Brain Sci 2022; 13:28. [PMID: 36672010 PMCID: PMC9856143 DOI: 10.3390/brainsci13010028] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
There is currently no simple, widely available screening method for Alzheimer's disease (AD), partly because the diagnosis of AD is complex and typically involves expensive and sometimes invasive tests not commonly available outside highly specialized clinical settings. Here, we developed an artificial intelligence (AI)-powered end-to-end system to detect AD and predict its severity directly from voice recordings. At the core of our system is the pre-trained data2vec model, the first high-performance self-supervised algorithm that works for speech, vision, and text. Our model was internally evaluated on the ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech only) dataset containing voice recordings of subjects describing the Cookie Theft picture, and externally validated on a test dataset from DementiaBank. The AI model can detect AD with average area under the curve (AUC) of 0.846 and 0.835 on held-out and external test set, respectively. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.9616). Moreover, the model can reliably predict the subject's cognitive testing score solely based on raw voice recordings. Our study demonstrates the feasibility of using the AI-powered end-to-end model for early AD diagnosis and severity prediction directly based on voice, showing its potential for screening Alzheimer's disease in a community setting.
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Affiliation(s)
| | - Hualou Liang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
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Agbavor F, Liang H. Predicting dementia from spontaneous speech using large language models. PLOS DIGITAL HEALTH 2022; 1:e0000168. [PMID: 36812634 PMCID: PMC9931366 DOI: 10.1371/journal.pdig.0000168] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Language impairment is an important biomarker of neurodegenerative disorders such as Alzheimer's disease (AD). Artificial intelligence (AI), particularly natural language processing (NLP), has recently been increasingly used for early prediction of AD through speech. Yet, relatively few studies exist on using large language models, especially GPT-3, to aid in the early diagnosis of dementia. In this work, we show for the first time that GPT-3 can be utilized to predict dementia from spontaneous speech. Specifically, we leverage the vast semantic knowledge encoded in the GPT-3 model to generate text embedding, a vector representation of the transcribed text from speech, that captures the semantic meaning of the input. We demonstrate that the text embedding can be reliably used to (1) distinguish individuals with AD from healthy controls, and (2) infer the subject's cognitive testing score, both solely based on speech data. We further show that text embedding considerably outperforms the conventional acoustic feature-based approach and even performs competitively with prevailing fine-tuned models. Together, our results suggest that GPT-3 based text embedding is a viable approach for AD assessment directly from speech and has the potential to improve early diagnosis of dementia.
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Affiliation(s)
- Felix Agbavor
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, United States of America
| | - Hualou Liang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, United States of America
- * E-mail:
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Bi H, Zare S, Kania U, Yan R. A systematic review of studies on connected speech processing: Trends, key findings, and implications. Front Psychol 2022; 13:1056827. [DOI: 10.3389/fpsyg.2022.1056827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/07/2022] [Indexed: 11/30/2022] Open
Abstract
Connected speech processing (CSP) is of great significance to individuals’ language and cognitive development. It is particularly crucial not only for clinical detection and treatment of developmental disorders, but also for the Foreign/second language teaching instructions. However, given the importance of this field, there is a clear lack of systematic reviews that summarize the key findings of previous studies. To this end, through searching in the scientific databases PsycInfo, Scopus, PubMed, ERIC, Taylor and Francis, and Web of Science, the present study identified 128 core CSP articles with high reference values according to PRISMA guidance and the following results were obtained through quantitative analysis and qualitative comparative synthesis: (1) The number of studies on CSP published per year showed an upward trend; however, most focused on English language, whereas the studies on other languages were comparatively rare; (2) CSP was found to be affected by multiple factors, among which speech speed, semantics, word frequency, and phonological awareness were most frequently investigated; (3) the deficit in CSP capacity was widely recognized as a significant predictor and indicator of developmental disorders; (4) more studies were carried out on connected speech production than on perception; and (5) almost no longitudinal studies have ever been conducted among either native or non-native speakers. Therefore, future research is needed to explore the developmental trajectory of CSP skills of typically developing language learners and speakers with cognitive disorders over different periods of time. It is also necessary to deepen the understanding of the processing mechanism beyond their performance and the role played by phonological awareness and lexical representations in CSP.
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Kumpik DP, Santos-Rodriguez R, Selwood J, Coulthard E, Twomey N, Craddock I, Ben-Shlomo Y. A longitudinal observational study of home-based conversations for detecting early dementia: protocol for the CUBOId TV task. BMJ Open 2022; 12:e065033. [PMID: 36418120 PMCID: PMC9684963 DOI: 10.1136/bmjopen-2022-065033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Limitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the 'TV task', designed to track changes in ecologically valid conversations with disease progression. METHODS AND ANALYSIS CUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 8-25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone. ETHICS AND DISSEMINATION CUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals.
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Affiliation(s)
- Daniel Paul Kumpik
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
| | | | - James Selwood
- Bristol Medical School, University of Bristol, Bristol, UK
- Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Elizabeth Coulthard
- Bristol Medical School, University of Bristol, Bristol, UK
- Department of Translational Health Sciences, University of Bristol, Bristol, UK
| | - Niall Twomey
- Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
| | - Ian Craddock
- Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
| | - Yoav Ben-Shlomo
- Department of Population Health Sciences, University of Bristol, Bristol, UK
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Hason L, Krishnan S. Spontaneous speech feature analysis for alzheimer's disease screening using a random forest classifier. Front Digit Health 2022; 4:901419. [PMID: 36465088 PMCID: PMC9712439 DOI: 10.3389/fdgth.2022.901419] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/19/2022] [Indexed: 07/20/2023] Open
Abstract
Detecting Alzheimer's disease (AD) and disease progression based on the patient's speech not the patient's speech data can aid non-invasive, cost-effective, real-time early diagnostic and repetitive monitoring in minimum time and effort using machine learning (ML) classification approaches. This paper aims to predict early AD diagnosis and evaluate stages of AD through exploratory analysis of acoustic features, non-stationarity, and non-linearity testing, and applying data augmentation techniques on spontaneous speech signals collected from AD and cognitively normal (CN) subjects. Evaluation of the proposed AD prediction and AD stages classification models using Random Forest classifier yielded accuracy rates of 82.2% and 71.5%. This will enrich the Alzheimer's research community with further understanding of methods to improve models for AD classification and addressing non-stationarity and non-linearity properties on audio features to determine the best-suited acoustic features for AD monitoring.
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Whelan R, Barbey FM, Cominetti MR, Gillan CM, Rosická AM. Developments in scalable strategies for detecting early markers of cognitive decline. Transl Psychiatry 2022; 12:473. [PMID: 36351888 PMCID: PMC9645320 DOI: 10.1038/s41398-022-02237-w] [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: 06/02/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/10/2022] Open
Abstract
Effective strategies for early detection of cognitive decline, if deployed on a large scale, would have individual and societal benefits. However, current detection methods are invasive or time-consuming and therefore not suitable for longitudinal monitoring of asymptomatic individuals. For example, biological markers of neuropathology associated with cognitive decline are typically collected via cerebral spinal fluid, cognitive functioning is evaluated from face-to-face assessments by experts and brain measures are obtained using expensive, non-portable equipment. Here, we describe scalable, repeatable, relatively non-invasive and comparatively inexpensive strategies for detecting the earliest markers of cognitive decline. These approaches are characterized by simple data collection protocols conducted in locations outside the laboratory: measurements are collected passively, by the participants themselves or by non-experts. The analysis of these data is, in contrast, often performed in a centralized location using sophisticated techniques. Recent developments allow neuropathology associated with potential cognitive decline to be accurately detected from peripheral blood samples. Advances in smartphone technology facilitate unobtrusive passive measurements of speech, fine motor movement and gait, that can be used to predict cognitive decline. Specific cognitive processes can be assayed using 'gamified' versions of standard laboratory cognitive tasks, which keep users engaged across multiple test sessions. High quality brain data can be regularly obtained, collected at-home by users themselves, using portable electroencephalography. Although these methods have great potential for addressing an important health challenge, there are barriers to be overcome. Technical obstacles include the need for standardization and interoperability across hardware and software. Societal challenges involve ensuring equity in access to new technologies, the cost of implementation and of any follow-up care, plus ethical issues.
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Affiliation(s)
- Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin, Ireland. .,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
| | - Florentine M. Barbey
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,Cumulus Neuroscience Ltd, Dublin, Ireland
| | - Marcia R. Cominetti
- grid.8217.c0000 0004 1936 9705Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland ,grid.411247.50000 0001 2163 588XDepartment of Gerontology, Universidade Federal de São Carlos, São Carlos, Brazil
| | - Claire M. Gillan
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Anna M. Rosická
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland
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Gregory S, Saunders S, Ritchie CW. Science disconnected: the translational gap between basic science, clinical trials, and patient care in Alzheimer's disease. THE LANCET. HEALTHY LONGEVITY 2022; 3:e797-e803. [PMID: 36356629 DOI: 10.1016/s2666-7568(22)00219-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/22/2022] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
Both research and clinical practice have traditionally centred on the dementia syndrome of Alzheimer's disease rather than its preclinical and prodromal stages. However, there is a strong scientific and ethical impetus to shift focus to earlier disease stages to improve brain health outcomes and help to keep affected individuals symptom-free (dementia-free) for as long as possible. We provide an overview of recent advancements in early detection, drug development, and trial methodology that should be utilised in the development of new therapies for use in brain health clinics. We propose a triad approach to Alzheimer's disease clinical trials, encompassing (1) experimental medicine studies to gather greater knowledge of disease mechanisms, (2) a more comprehensive platform of phase 2 learning trials to inform phase 3 confirmatory trials, and (3) precision medicine involving smaller subgroups of patients with shared characteristics. This triad would ensure that treatment targets are identified accurately, trial methodology focuses on at-risk populations, and sensitive outcome measures capture potential treatment effects. Clinical services around the world must embrace the brain health clinic model so that neurodegenerative diseases can be detected in their earliest phase to quicken drug development pipelines and potentially improve prognosis.
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Affiliation(s)
- Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK.
| | - Stina Saunders
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK; Brain Health Scotland, Edinburgh, UK
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Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics , University of Ghana, Ghana Atomic Energy Commission , Accra , Ghana
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Igarashi T, Nihei M. Cognitive Assessment of Japanese Older Adults with Text Data Augmentation. Healthcare (Basel) 2022; 10:healthcare10102051. [PMID: 36292498 PMCID: PMC9602467 DOI: 10.3390/healthcare10102051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/10/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Current medical science has not yet found a cure for dementia. The most important measures to combat dementia are to detect the tendency toward cognitive decline as early as possible and to intervene at an early stage. For this reason, screening for dementia based on language ability has attracted much attention in recent years. However, in most of the previous studies, the cohort of people with dementia has been smaller than the control cohort. In this paper, we use a pre-trained Japanese language model for text analysis and evaluate the effectiveness of text augmentation on a dataset consisting of Japanese-speaking healthy older adults and those with mild cognitive impairment (MCI). We also examined what tasks contributed to the results. This experimental setting can also be used to detect other diseases that may affect the language areas of the brain outside of the hospital.
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Affiliation(s)
- Toshiharu Igarashi
- Department of Human and Engineered Environmental Studies, The University of Tokyo, Kashiwanoha 5-1-5, Chiba 277-8563, Japan
- Correspondence:
| | - Misato Nihei
- Department of Human and Engineered Environmental Studies, The University of Tokyo, Kashiwanoha 5-1-5, Chiba 277-8563, Japan
- Institute of Gerontology, The University of Tokyo, 3-1, Hongo 7-chome, Bunkyo-ku, Tokyo 113-8654, Japan
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Fristed E, Skirrow C, Meszaros M, Lenain R, Meepegama U, Papp KV, Ropacki M, Weston J. Leveraging speech and artificial intelligence to screen for early Alzheimer's disease and amyloid beta positivity. Brain Commun 2022; 4:fcac231. [PMID: 36381988 PMCID: PMC9639797 DOI: 10.1093/braincomms/fcac231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/30/2022] [Accepted: 09/13/2022] [Indexed: 08/27/2023] Open
Abstract
Early detection of Alzheimer's disease is required to identify patients suitable for disease-modifying medications and to improve access to non-pharmacological preventative interventions. Prior research shows detectable changes in speech in Alzheimer's dementia and its clinical precursors. The current study assesses whether a fully automated speech-based artificial intelligence system can detect cognitive impairment and amyloid beta positivity, which characterize early stages of Alzheimer's disease. Two hundred participants (age 54-85, mean 70.6; 114 female, 86 male) from sister studies in the UK (NCT04828122) and the USA (NCT04928976), completed the same assessments and were combined in the current analyses. Participants were recruited from prior clinical trials where amyloid beta status (97 amyloid positive, 103 amyloid negative, as established via PET or CSF test) and clinical diagnostic status was known (94 cognitively unimpaired, 106 with mild cognitive impairment or mild Alzheimer's disease). The automatic story recall task was administered during supervised in-person or telemedicine assessments, where participants were asked to recall stories immediately and after a brief delay. An artificial intelligence text-pair evaluation model produced vector-based outputs from the original story text and recorded and transcribed participant recalls, quantifying differences between them. Vector-based representations were fed into logistic regression models, trained with tournament leave-pair-out cross-validation analysis to predict amyloid beta status (primary endpoint), mild cognitive impairment and amyloid beta status in diagnostic subgroups (secondary endpoints). Predictions were assessed by the area under the receiver operating characteristic curve for the test result in comparison with reference standards (diagnostic and amyloid status). Simulation analysis evaluated two potential benefits of speech-based screening: (i) mild cognitive impairment screening in primary care compared with the Mini-Mental State Exam, and (ii) pre-screening prior to PET scanning when identifying an amyloid positive sample. Speech-based screening predicted amyloid beta positivity (area under the curve = 0.77) and mild cognitive impairment or mild Alzheimer's disease (area under the curve = 0.83) in the full sample, and predicted amyloid beta in subsamples (mild cognitive impairment or mild Alzheimer's disease: area under the curve = 0.82; cognitively unimpaired: area under the curve = 0.71). Simulation analyses indicated that in primary care, speech-based screening could modestly improve detection of mild cognitive impairment (+8.5%), while reducing false positives (-59.1%). Furthermore, speech-based amyloid pre-screening was estimated to reduce the number of PET scans required by 35.3% and 35.5% in individuals with mild cognitive impairment and cognitively unimpaired individuals, respectively. Speech-based assessment offers accessible and scalable screening for mild cognitive impairment and amyloid beta positivity.
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Affiliation(s)
| | | | | | | | | | - Kathryn V Papp
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Michael Ropacki
- Strategic Global Research & Development, Temecula, California, 94019, USA
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Tröger J, Baykara E, Zhao J, ter Huurne D, Possemis N, Mallick E, Schäfer S, Schwed L, Mina M, Linz N, Ramakers I, Ritchie C. Validation of the Remote Automated ki:e Speech Biomarker for Cognition in Mild Cognitive Impairment: Verification and Validation following DiME V3 Framework. Digit Biomark 2022; 6:107-116. [PMID: 36466952 PMCID: PMC9710455 DOI: 10.1159/000526471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/06/2022] [Indexed: 08/01/2023] Open
Abstract
INTRODUCTION Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer's disease. While most well-established measures for cognition might not fit tomorrow's decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society's V3 framework: verification, analytical validation, and clinical validation. METHODS Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. RESULTS Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. CONCLUSION Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.
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Affiliation(s)
| | | | | | - Daphne ter Huurne
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Nina Possemis
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | | | | | | | | | - Inez Ramakers
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
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Natural language processing to identify substance misuse in the electronic health record. Lancet Digit Health 2022; 4:e401-e402. [DOI: 10.1016/s2589-7500(22)00096-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 11/24/2022]
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Bose A, Dutta M, Dash NS, Nandi R, Dutt A, Ahmed S. Importance of Task Selection for Connected Speech Analysis in Patients with Alzheimer’s Disease from an Ethnically Diverse Sample. J Alzheimers Dis 2022; 87:1475-1481. [PMID: 35491794 PMCID: PMC9277689 DOI: 10.3233/jad-220166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Features of linguistic impairment in Alzheimer’s disease (AD) are primarily derived from English-speaking patients. Little is known regarding such deficits in linguistically diverse speakers with AD. We aimed to detail linguistic profiles (speech rate, dysfluencies, syntactic, lexical, morphological, semantics) from two connected speech tasks–Frog Story and picture description–in Bengali-speaking AD patients. The Frog Story detected group differences on all six linguistic levels, compared to only three with picture description. Critically, Frog Story captured the language-specific differences between the groups. Careful consideration should be given to the choice of connected speech tasks for dementia diagnosis in linguistically diverse populations.
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Affiliation(s)
- Arpita Bose
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
| | - Manaswita Dutta
- Department of Communication Disorders and Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Niladri S. Dash
- Linguistic Research Unit, Indian Statistical Institute, Kolkata, India
| | - Ranita Nandi
- Neuropsychology and Clinical Psychology Unit, Duttanagar Mental Health Centre, Kolkata, India
| | - Aparna Dutt
- Neuropsychology and Clinical Psychology Unit, Duttanagar Mental Health Centre, Kolkata, India
| | - Samrah Ahmed
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Kálmán J, Devanand DP, Gosztolya G, Balogh R, Imre N, Tóth L, Hoffmann I, Kovács I, Vincze V, Pákáski M. Temporal speech parameters detect mild cognitive impairment in different languages: validation and comparison of the Speech-GAP Test® in English and Hungarian. Curr Alzheimer Res 2022; 19:373-386. [PMID: 35440309 DOI: 10.2174/1567205019666220418155130] [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: 12/20/2021] [Revised: 02/08/2022] [Accepted: 02/17/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments. OBJECTIVE The main goal of this international pilot study is to address the question whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English. METHOD After an initial screening of 88 individuals, English-speaking (n = 33) and Hungarian-speaking (n = 33) participants were classified as having MCI or as healthy controls (HC) based on Petersen's criteria. Speech of each participant was recorded via a spontaneous speech task. 15 temporal parameters were determined and calculated by means of ASR. RESULTS Seven temporal parameters in the English-speaking sample and 5 in the Hungarian-speaking sample showed significant differences between the MCI and the HC group. Receiver operating characteristics (ROC) analysis clearly distinguished the English-speaking MCI cases from the HC group based on speech tempo and articulation tempo with 100% sensitivity, and on three more temporal parameters with high sensitivity (85.7%). In the Hungarian-speaking sample, the ROC analysis showed similar sensitivity rates (92.3%). CONCLUSION The results of this study in different native-speaking populations suggest that changes in acoustic parameters detected by the S-GAP Test® might be present across different languages.
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Affiliation(s)
- János Kálmán
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Davangere P Devanand
- Columbia University Medical Center, New York, NY.,New York State Psychiatric Institute, New York, NY
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, Faculty of Science and Informatics, University of Szeged, Szeged
| | - Réka Balogh
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Nóra Imre
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - László Tóth
- Faculty of Science and Informatics, University of Szeged, Szeged
| | - Ildikó Hoffmann
- Faculty of Humanities and Social Sciences, University of Szeged, Szeged.,Hungarian Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest
| | - Ildikó Kovács
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Veronika Vincze
- MTA-SZTE Research Group on Artificial Intelligence, Faculty of Science and Informatics, University of Szeged, Szeged
| | - Magdolna Pákáski
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
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46
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Sanz C, Carrillo F, Slachevsky A, Forno G, Gorno Tempini ML, Villagra R, Ibáñez A, Tagliazucchi E, García AM. Automated text-level semantic markers of Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12276. [PMID: 35059492 PMCID: PMC8759093 DOI: 10.1002/dad2.12276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 11/04/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer's disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity. METHODS Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson's disease (PD) patients. RESULTS Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near-chance classification between PD patients and HCs. DISCUSSION Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
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Affiliation(s)
- Camila Sanz
- Departamento de FísicaUniversidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA‐CONICET)Pabellón ICiudad Universitaria (1428)CABABuenos AiresArgentina
| | - Facundo Carrillo
- Applied Artificial Intelligence Lab (ICC‐CONICET)Pabellón ICiudad Universitaria (1428)CABABuenos AiresArgentina
| | - Andrea Slachevsky
- Memory and Neuropsychiatric Clinic, Neurology Department, Hospital del Salvador (7500000), SSMO & Faculty of Medicine (8380000)University of ChileSantiagoChile
- Center for Brain Health and Metabolism (GERO) (7500922)SantiagoChile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile (7500922)University of ChileSantiagoChile
- Servicio de Neurología, Departamento de MedicinaClínica Alemana‐Universidad del Desarrollo (7550000)SantiagoChile
- East Neuroscience Department, Faculty of Medicine (7650567)University of ChileSantiagoChile
| | - Gonzalo Forno
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile (7500922)University of ChileSantiagoChile
- School of PsychologyUniversidad de los Andes (7550000)SantiagoChile
- Alzheimer's and other cognitive disorders groupInstitute of Neurosciences (08035)University of BarcelonaBarcelonaSpain
| | - Maria Luisa Gorno Tempini
- Memory and Aging CenterDepartment of Neurology (94143)University of CaliforniaSan FranciscoCaliforniaUSA
| | - Roque Villagra
- Center for Brain Health and Metabolism (GERO) (7500922)SantiagoChile
- East Neuroscience Department, Faculty of Medicine (7650567)University of ChileSantiagoChile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat) (7550000)Universidad Adolfo IbáñezSantiagoChile
- Cognitive Neuroscience Center (1644)Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (1425)Buenos AiresArgentina
- Global Brain Health Institute (94143)University of California‐San Francisco, San Francisco, California, USA; and Trinity College Dublin (D02), Dublin, Ireland
| | - Enzo Tagliazucchi
- Departamento de FísicaUniversidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA‐CONICET)Pabellón ICiudad Universitaria (1428)CABABuenos AiresArgentina
- Latin American Brain Health Institute (BrainLat) (7550000)Universidad Adolfo IbáñezSantiagoChile
| | - Adolfo M. García
- Cognitive Neuroscience Center (1644)Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (1425)Buenos AiresArgentina
- Global Brain Health Institute (94143)University of California‐San Francisco, San Francisco, California, USA; and Trinity College Dublin (D02), Dublin, Ireland
- Departamento de Lingüística y LiteraturaFacultad de Humanidades (9160000)Universidad de Santiago de ChileSantiagoChile
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Vasudeva A, Sheikh NA, Sahu S. International Classification of Functioning, Disability, and Health augmented by telemedicine and artificial intelligence for assessment of functional disability. J Family Med Prim Care 2021; 10:3535-3539. [PMID: 34934642 PMCID: PMC8653435 DOI: 10.4103/jfmpc.jfmpc_692_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 11/04/2022] Open
Abstract
The concept of functional disability is aligned with the biopsycho-social model of disability. However, there are reasons why the antiquated measurement of medical impairment continues to be in use. We propose solutions for a fairer process using the International Classification of Functioning, Disability, and Health (ICF) at the level of the medical boards augmented by telemedicine and artificial intelligence (AI). The proposed technologies (Level 1 and Level 2 AI) need to be tried in pilot projects. It will accomplish two goals, the first being the measurement of disability and not merely the impairment. Second, and perhaps more importantly, making the process more transparent in creating a "just" society.
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Affiliation(s)
- Abhimanyu Vasudeva
- Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, Gorakhpur, Uttar Pradesh, India
| | - Nishat A Sheikh
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Gorakhpur, Uttar Pradesh, India
| | - Samantak Sahu
- Department of Physical Medicine and Rehabilitation, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
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48
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Mahendran N, P M DRV. A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease. Comput Biol Med 2021; 141:105056. [PMID: 34839903 DOI: 10.1016/j.compbiomed.2021.105056] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 11/20/2021] [Accepted: 11/20/2021] [Indexed: 12/29/2022]
Abstract
Ageing is associated with various ailments including Alzheimer 's disease (AD), which is a progressive form of dementia. AD symptoms develop over a period of years and, unfortunately, there is no cure. Existing AD treatments can only slow down the progression of symptoms and thus it is critical to diagnose the disease at an early stage. To help improve the early diagnosis of AD, a deep learning-based classification model with an embedded feature selection approach was used to classify AD patients. An AD DNA methylation data set (64 records with 34 cases and 34 controls) from the GEO omnibus database was used for the analysis. Before selecting the relevant features, the data were preprocessed by performing quality control, normalization and downstream analysis. As the number of associated CpG sites was huge, four embedded-based feature selection models were compared and the best method was used for the proposed classification model. An Enhanced Deep Recurrent Neural Network (EDRNN) was implemented and compared to other existing classification models, including a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Deep Recurrent Neural Network (DRNN). The results showed a significant improvement in the classification accuracy of the proposed model as compared to the other methods.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
| | - Durai Raj Vincent P M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
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Robin J, Xu M, Kaufman LD, Simpson W. Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment. Front Digit Health 2021; 3:749758. [PMID: 34778869 PMCID: PMC8579012 DOI: 10.3389/fdgth.2021.749758] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/27/2021] [Indexed: 11/23/2022] Open
Abstract
Detecting early signs of cognitive decline is crucial for early detection and treatment of Alzheimer's Disease. Most of the current screening tools for Alzheimer's Disease represent a significant burden, requiring invasive procedures, or intensive and costly clinical testing. Recent findings have highlighted changes to speech and language patterns that occur in Alzheimer's Disease, and may be detectable prior to diagnosis. Automated tools to assess speech have been developed that can be used on a smartphone or tablet, from one's home, in under 10 min. In this study, we present the results of a study of older adults who completed a digital speech assessment task over a 6-month period. Participants were grouped according to those who scored above (N = 18) or below (N = 18) the recommended threshold for detecting cognitive impairment on the Montreal Cognitive Assessment (MoCA) and those with diagnoses of mild cognitive impairment (MCI) or early Alzheimer's Disease (AD) (N = 14). Older adults who scored above the MoCA threshold had better performance on speech composites reflecting language coherence, information richness, syntactic complexity, and word finding abilities. Those with MCI and AD showed more rapid decline in the coherence of language from baseline to 6-month follow-up, suggesting that this score may be useful both for detecting cognitive decline and monitoring change over time. This study demonstrates that automated speech assessments have potential as sensitive tools to detect early signs of cognitive impairment and monitor progression over time.
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Affiliation(s)
| | | | | | - William Simpson
- Winterlight Labs, Toronto, ON, Canada.,Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON, Canada
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50
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Abstract
Digital health data are multimodal and high-dimensional. A patient's health state can be characterized by a multitude of signals including medical imaging, clinical variables, genome sequencing, conversations between clinicians and patients, and continuous signals from wearables, among others. This high volume, personalized data stream aggregated over patients' lives has spurred interest in developing new artificial intelligence (AI) models for higher-precision diagnosis, prognosis, and tracking. While the promise of these algorithms is undeniable, their dissemination and adoption have been slow, owing partially to unpredictable AI model performance once deployed in the real world. We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting-their high-dimensional nature. This paper considers how the large number of features in vast digital health data can challenge the development of robust AI models-a phenomenon known as "the curse of dimensionality" in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algorithm designers.
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