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Wang Y, Wang H, Li Z, Zhang H, Yang L, Li J, Tang Z, Hou S, Wang Q. Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers. Chin Med 2024; 19:101. [PMID: 39049005 PMCID: PMC11267751 DOI: 10.1186/s13020-024-00973-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types. METHODS Participants, aged 18-45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models-1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)-were implemented using Python to classify health status. Saliency maps were generated to provide model explainability. RESULTS The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy. CONCLUSIONS The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.
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Affiliation(s)
- Yanbing Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Haiyan Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zhuoxuan Li
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Haoran Zhang
- School of Management, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Liwen Yang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Jiarui Li
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zixiang Tang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Shujuan Hou
- National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
| | - Qi Wang
- National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
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Treder MS, Lee S, Tsvetanov KA. Introduction to Large Language Models (LLMs) for dementia care and research. FRONTIERS IN DEMENTIA 2024; 3:1385303. [PMID: 39081594 PMCID: PMC11285660 DOI: 10.3389/frdem.2024.1385303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/23/2024] [Indexed: 08/02/2024]
Abstract
Introduction Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement. In light of the recent advent of Large Language Models (LLMs) such as ChatGPT, this paper aims to thoroughly analyse their potential applications and usefulness in dementia care and research. Method To this end, we offer an introduction into LLMs, outlining the key features, capabilities, limitations, potential risks, and practical considerations for deployment as easy-to-use software (e.g., smartphone apps). We then explore various domains related to dementia, identifying opportunities for LLMs to enhance understanding, diagnostics, and treatment, with a broader emphasis on improving patient care. For each domain, the specific contributions of LLMs are examined, such as their ability to engage users in meaningful conversations, deliver personalized support, and offer cognitive enrichment. Potential benefits encompass improved social interaction, enhanced cognitive functioning, increased emotional well-being, and reduced caregiver burden. The deployment of LLMs in caregiving frameworks also raises a number of concerns and considerations. These include privacy and safety concerns, the need for empirical validation, user-centered design, adaptation to the user's unique needs, and the integration of multimodal inputs to create more immersive and personalized experiences. Additionally, ethical guidelines and privacy protocols must be established to ensure responsible and ethical deployment of LLMs. Results We report the results on a questionnaire filled in by people with dementia (PwD) and their supporters wherein we surveyed the usefulness of different application scenarios of LLMs as well as the features that LLM-powered apps should have. Both PwD and supporters were largely positive regarding the prospect of LLMs in care, although concerns were raised regarding bias, data privacy and transparency. Discussion Overall, this review corroborates the promising utilization of LLMs to positively impact dementia care by boosting cognitive abilities, enriching social interaction, and supporting caregivers. The findings underscore the importance of further research and development in this field to fully harness the benefits of LLMs and maximize their potential for improving the lives of individuals living with dementia.
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Affiliation(s)
- Matthias S. Treder
- School of Computer Science & Informatics, Cardiff University, Cardiff, United Kingdom
| | - Sojin Lee
- Olive AI Limited, London, United Kingdom
| | - Kamen A. Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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3
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Cabreira V, Alty J, Antic S, Araújo R, Aybek S, Ball HA, Baslet G, Bhome R, Coebergh J, Dubois B, Edwards M, Filipović SR, Frederiksen KS, Harbo T, Hayhow B, Howard R, Huntley J, Isaacs J, LaFrance WC, Larner AJ, Di Lorenzo F, Main J, Mallam E, Marra C, Massano J, McGrath ER, McWhirter L, Moreira IP, Nobili F, Pennington C, Tábuas-Pereira M, Perez DL, Popkirov S, Rayment D, Rossor M, Russo M, Santana I, Schott J, Scott EP, Taipa R, Tinazzi M, Tomic S, Toniolo S, Tørring CW, Wilkinson T, Frostholm L, Stone J, Carson A. Perspectives on the diagnosis and management of functional cognitive disorder: An international Delphi study. Eur J Neurol 2024:e16318. [PMID: 38700361 DOI: 10.1111/ene.16318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/18/2024] [Accepted: 04/11/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND Current proposed criteria for functional cognitive disorder (FCD) have not been externally validated. We sought to analyse the current perspectives of cognitive specialists in the diagnosis and management of FCD in comparison with neurodegenerative conditions. METHODS International experts in cognitive disorders were invited to assess seven illustrative clinical vignettes containing history and bedside characteristics alone. Participants assigned a probable diagnosis and selected the appropriate investigation and treatment. Qualitative, quantitative and inter-rater agreement analyses were undertaken. RESULTS Eighteen diagnostic terminologies were assigned by 45 cognitive experts from 12 countries with a median of 13 years of experience, across the seven scenarios. Accurate discrimination between FCD and neurodegeneration was observed, independently of background and years of experience: 100% of the neurodegenerative vignettes were correctly classified and 75%-88% of the FCD diagnoses were attributed to non-neurodegenerative causes. There was <50% agreement in the terminology used for FCD, in comparison with 87%-92% agreement for neurodegenerative syndromes. Blood tests and neuropsychological evaluation were the leading diagnostic modalities for FCD. Diagnostic communication, psychotherapy and psychiatry referral were the main suggested management strategies in FCD. CONCLUSIONS Our study demonstrates the feasibility of distinguishing between FCD and neurodegeneration based on relevant patient characteristics and history details. These characteristics need further validation and operationalisation. Heterogeneous labelling and framing pose clinical and research challenges reflecting a lack of agreement in the field. Careful consideration of FCD diagnosis is advised, particularly in the presence of comorbidities. This study informs future research on diagnostic tools and evidence-based interventions.
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Affiliation(s)
- Verónica Cabreira
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia
| | - Sonja Antic
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Rui Araújo
- Department of Neurology, Centro Hospitalar Universitário São João, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine University of Porto, Porto, Portugal
| | - Selma Aybek
- Neurology, Faculty of Sciences and Medicine, Fribourg University, Fribourg, Switzerland
| | - Harriet A Ball
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Gaston Baslet
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Rohan Bhome
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Jan Coebergh
- Department of Neurology, St George's University of London, London, UK
| | - Bruno Dubois
- Department of Neurology, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), AP-HP, Brain Institute, Sorbonne University, Paris, France
| | - Mark Edwards
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry Psychology and Neurosciences, Kings College London, London, UK
| | - Saša R Filipović
- University of Belgrade Institute for Medical Research, Belgrade, Serbia
| | - Kristian Steen Frederiksen
- Clinical Trial Unit, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Harbo
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Bradleigh Hayhow
- Department of Neurology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
- School of Medicine, University of Notre Dame Australia, Fremantle, Western Australia, Australia
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK
| | - Jonathan Huntley
- Division of Psychiatry, University College London, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - Jeremy Isaacs
- Department of Neurology, St George's University of London, London, UK
| | - William Curt LaFrance
- Alpert Medical School, Brown University, Providence, Rhode Island, USA
- Neuropsychiatry and Behavioral Neurology, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Andrew J Larner
- Cognitive Function Clinic, Walton Centre for Neurology and Neurosurgery, Liverpool, UK
| | - Francesco Di Lorenzo
- Department of Clinical and Behavioural Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - James Main
- Bristol Dementia Wellbeing Service, Devon Partnership NHS Trust, Bristol, UK
| | | | - Camillo Marra
- Department of Neuroscience, Catholic University of the Sacred Heart, Memory Clinic - Fondazione Policlinico Agostino Gemelli IRCCS, Rome, Italy
| | - João Massano
- Department of Neurology, Centro Hospitalar Universitário São João, Porto, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine University of Porto, Porto, Portugal
| | - Emer R McGrath
- School of Medicine, University of Galway, Galway, Ireland
| | - Laura McWhirter
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Isabel Portela Moreira
- Neurology Department, Private Hospital of Gaia of the Trofa Saúde Group, Vila Nova de Gaia, Portugal
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Catherine Pennington
- Clinical Lecturer, University of Edinburgh, Edinburgh, UK
- Neurology Department, NHS Forth Valley, Larbert, UK
- Department of Clinical Neurosciences, NHS Lothian, Edinburgh, UK
| | - Miguel Tábuas-Pereira
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Praceta Prof. Mota Pinto, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - David L Perez
- Department of Neurology and Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stoyan Popkirov
- Department of Neurology, University Hospital Essen, Essen, Germany
| | - Dane Rayment
- Rosa Burden Centre for Neuropsychiatry, Southmead Hospital, Bristol, UK
| | - Martin Rossor
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Isabel Santana
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Jonathan Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Emmi P Scott
- Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ricardo Taipa
- Neuropathology Department, Centro Hospitalar Universitário de Santo António, Porto, Portugal
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Svetlana Tomic
- Department of Neurology, University Hospital Center Osijek, Medical School on University of Osijek, Osijek, Croatia
| | - Sofia Toniolo
- Cognitive Disorder Clinic, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Tim Wilkinson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Lisbeth Frostholm
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Functional Disorders and Psychosomatics, Aarhus University Hospital, Aarhus, Denmark
| | - Jon Stone
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Ambrosini E, Giangregorio C, Lomurno E, Moccia S, Milis M, Loizou C, Azzolino D, Cesari M, Cid Gala M, Galán de Isla C, Gomez-Raja J, Borghese NA, Matteucci M, Ferrante S. Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study. JMIR Aging 2024; 7:e50537. [PMID: 38386279 DOI: 10.2196/50537] [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: 07/05/2023] [Revised: 12/18/2023] [Accepted: 02/12/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults. OBJECTIVE This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline. METHODS A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability. RESULTS In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets. CONCLUSIONS This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.
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Affiliation(s)
- Emilia Ambrosini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Chiara Giangregorio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Eugenio Lomurno
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Sara Moccia
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Christos Loizou
- Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus
| | - Domenico Azzolino
- Geriatric Unit, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Matteo Cesari
- Ageing and Health Unit, Department of Maternal, Newborn, Child, Adolescent Health and Ageing, World Health Organization, Geneva, Switzerland
| | - Manuel Cid Gala
- Consejería de Sanidad y Servicios Sociales, Junta de Extremadura, Merida, Spain
| | | | - Jonathan Gomez-Raja
- Consejería de Sanidad y Servicios Sociales, Junta de Extremadura, Merida, Spain
| | | | - Matteo Matteucci
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Simona Ferrante
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
- Laboratory of E-Health Technologies and Artificial Intelligence Research in Neurology, Joint Research Platform, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
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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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6
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Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc 2024; 13:e031247. [PMID: 38226518 PMCID: PMC10926806 DOI: 10.1161/jaha.123.031247] [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] [Indexed: 01/17/2024]
Abstract
Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.
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Affiliation(s)
- Zachary Popp
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Akwaugo Igwe
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Md Salman Rahman
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Minzae Kim
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Raiyan Khan
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Emily Oh
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ankita Kumar
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Huitong Ding
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Preeti Sunderaraman
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Rhoda Au
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
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7
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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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8
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Cabreira V, Frostholm L, McWhirter L, Stone J, Carson A. Clinical signs in functional cognitive disorders: A systematic review and diagnostic meta-analysis. J Psychosom Res 2023; 173:111447. [PMID: 37567095 DOI: 10.1016/j.jpsychores.2023.111447] [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: 05/17/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023]
Abstract
OBJECTIVE Functional cognitive disorder (FCD) accounts for around a third of patients attending specialized memory clinics. It is also overrepresented in patients with other functional and somatic diagnoses. So far, no long-term diagnostic validity studies were conducted, and a positive diagnostic profile is yet to be identified. We aimed to review the literature on diagnostic signs and symptoms that allow for a discrimination between FCD and neurodegeneration. METHODS Systematic review of Ovid-Medline®, Embase and PsycINFO databases. Relevant clinical features were extracted including demographics, symptom history, comorbidities, language and interaction profiles and cognitive assessments. Studies with quantifiable diagnostic accuracy data were included in a diagnostic meta-analysis. RESULTS Thirty studies (N = 8602) were included. FCD patients were younger, more educated, and more likely to have a family history of older onset dementia, abrupt symptom onset, and higher rates of anxiety, depression and sleep disturbance. Promising language profiles include longer duration of spoken answer, elaborated examples of memory failures, ability to answer compound and personal questions, and demonstration of working memory during interaction. The pooled analysis of clinical accuracy of different signs revealed that attending alone and bringing a handwritten list of problems particularly increase the odds of a FCD diagnosis. Current evidence from neuropsychometric studies in FCD is scarce. CONCLUSIONS Our systematic review reinforces that positive signs contribute for an early differentiation between FCD and neurodegeneration in patients presenting with memory complaints. It is the first to attain quantitative value to clinical observations. These results will inform future diagnostic decision tools and intervention testing.
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Affiliation(s)
- Verónica Cabreira
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
| | - Lisbeth Frostholm
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Laura McWhirter
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Jon Stone
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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9
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Parsapoor M. AI-based assessments of speech and language impairments in dementia. Alzheimers Dement 2023; 19:4675-4687. [PMID: 37578167 DOI: 10.1002/alz.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 08/15/2023]
Abstract
Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.
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Affiliation(s)
- Mahboobeh Parsapoor
- Centre de Recherche Informatique de Montréal: CRIM, Montreal, Quebec, Canada
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10
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Walker G, Pevy N, O'Malley R, Mirheidari B, Reuber M, Christensen H, Blackburn DJ. Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls. CLINICAL LINGUISTICS & PHONETICS 2023:1-22. [PMID: 37722818 DOI: 10.1080/02699206.2023.2254458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/28/2023] [Indexed: 09/20/2023]
Abstract
Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.
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Affiliation(s)
- Gareth Walker
- School of English, University of Sheffield, Sheffield, UK
| | - Nathan Pevy
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Ronan O'Malley
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Bahman Mirheidari
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Markus Reuber
- Academic Neurology Unit, Royal Hallamshire Hospital, University of Sheffield, Sheffield, UK
| | - Heidi Christensen
- Department of Computer Science, University of Sheffield, Sheffield, UK
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11
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Mizuguchi D, Yamamoto T, Omiya Y, Endo K, Tano K, Oya M, Takano S. Novel Screening Tool Using Non-linguistic Voice Features Derived from Simple Phrases to Detect Mild Cognitive Impairment and Dementia. JAR LIFE 2023; 12:72-76. [PMID: 37637273 PMCID: PMC10450207 DOI: 10.14283/jarlife.2023.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/13/2023] [Indexed: 08/29/2023]
Abstract
Appropriate intervention and care in detecting cognitive impairment early are essential to effectively prevent the progression of cognitive deterioration. Diagnostic voice analysis is a noninvasive and inexpensive screening method that could be useful for detecting cognitive deterioration at earlier stages such as mild cognitive impairment. We aimed to distinguish between patients with dementia or mild cognitive impairment and healthy controls by using purely acoustic features (i.e., nonlinguistic features) extracted from two simple phrases. Voice was analyzed on 195 recordings from 150 patients (age, 45-95 years). We applied a machine learning algorithm (LightGBM; Microsoft, Redmond, WA, USA) to test whether the healthy control, mild cognitive impairment, and dementia groups could be accurately classified, based on acoustic features. Our algorithm performed well: area under the curve was 0.81 and accuracy, 66.7% for the 3-class classification. Thus, our vocal biomarker is useful for automated assistance in diagnosing early cognitive deterioration.
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Affiliation(s)
| | | | | | - K Endo
- PST Inc., Yokohama, Japan
| | - K Tano
- Takeyama Hospital, Yokohama, Japan
| | - M Oya
- Takeyama Hospital, Yokohama, Japan
| | - S Takano
- Honjo Kodama Hospital, Honjo, Japan
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12
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Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. J Med Internet Res 2023; 25:e46105. [PMID: 37467031 PMCID: PMC10398366 DOI: 10.2196/46105] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
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Affiliation(s)
- Alper Idrisoglu
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
- School of Health Sciences, University of Skövde, Skövde, Sweden
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13
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Faragó P, Ștefănigă SA, Cordoș CG, Mihăilă LI, Hintea S, Peștean AS, Beyer M, Perju-Dumbravă L, Ileșan RR. CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments. Bioengineering (Basel) 2023; 10:bioengineering10050531. [PMID: 37237601 DOI: 10.3390/bioengineering10050531] [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: 03/13/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Parkinson's disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson's disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.
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Affiliation(s)
- Paul Faragó
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Sebastian-Aurelian Ștefănigă
- Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania
| | - Claudia-Georgiana Cordoș
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Laura-Ioana Mihăilă
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Sorin Hintea
- Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Ana-Sorina Peștean
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Iuliu Hatieganu" Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Michel Beyer
- Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland
- Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, CH-4123 Allschwil, Switzerland
| | - Lăcrămioara Perju-Dumbravă
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Iuliu Hatieganu" Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Robert Radu Ileșan
- Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Iuliu Hatieganu" Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland
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14
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O'Connor S. Artificial Intelligence for Older Adult Health: Opportunities for Advancing Gerontological Nursing Practice. J Gerontol Nurs 2022; 48:3-5. [DOI: 10.3928/00989134-20221107-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Siobhán O'Connor
- Division of Nursing, Midwifery and Social Work, University of Manchester, Manchester,
United Kingdom
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15
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Mahon E, Lachman ME. Voice biomarkers as indicators of cognitive changes in middle and later adulthood. Neurobiol Aging 2022; 119:22-35. [PMID: 35964541 PMCID: PMC9487188 DOI: 10.1016/j.neurobiolaging.2022.06.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 06/20/2022] [Accepted: 06/26/2022] [Indexed: 11/20/2022]
Abstract
Voice prosody measures have been linked with Alzheimer's disease (AD), but it is unclear whether they are associated with normal cognitive aging. We assessed relationships between voice measures and 10-year cognitive changes in the MIDUS national sample of middle-aged and older adults ages 42-92, with a mean age of 64.09 (standard deviation = 11.23) at the second wave. Seven cognitive tests were assessed in 2003-2004 (Wave 2) and 2013-2014 (Wave 3). Voice measures were collected at Wave 3 (N = 2585) from audio recordings of the cognitive interviews. Analyses controlled for age, education, depressive symptoms, and health. As predicted, higher jitter was associated with greater declines in episodic memory, verbal fluency, and attention switching. Lower pulse was related to greater decline in episodic memory, and fewer voice breaks were related to greater declines in episodic memory and verbal fluency, although the direction of these effects was contrary to hypotheses. Findings suggest that voice biomarkers may offer a promising approach for early detection of risk factors for cognitive impairment or AD.
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Affiliation(s)
- Elizabeth Mahon
- Brandeis University, Department of Psychology, Waltham, MA, USA.
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16
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Eaton SL, Murdoch F, Rzechorzek NM, Thompson G, Hartley C, Blacklock BT, Proudfoot C, Lillico SG, Tennant P, Ritchie A, Nixon J, Brennan PM, Guido S, Mitchell NL, Palmer DN, Whitelaw CBA, Cooper JD, Wishart TM. Modelling Neurological Diseases in Large Animals: Criteria for Model Selection and Clinical Assessment. Cells 2022; 11:cells11172641. [PMID: 36078049 PMCID: PMC9454934 DOI: 10.3390/cells11172641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Issue: The impact of neurological disorders is recognised globally, with one in six people affected in their lifetime and few treatments to slow or halt disease progression. This is due in part to the increasing ageing population, and is confounded by the high failure rate of translation from rodent-derived therapeutics to clinically effective human neurological interventions. Improved translation is demonstrated using higher order mammals with more complex/comparable neuroanatomy. These animals effectually span this translational disparity and increase confidence in factors including routes of administration/dosing and ability to scale, such that potential therapeutics will have successful outcomes when moving to patients. Coupled with advancements in genetic engineering to produce genetically tailored models, livestock are increasingly being used to bridge this translational gap. Approach: In order to aid in standardising characterisation of such models, we provide comprehensive neurological assessment protocols designed to inform on neuroanatomical dysfunction and/or lesion(s) for large animal species. We also describe the applicability of these exams in different large animals to help provide a better understanding of the practicalities of cross species neurological disease modelling. Recommendation: We would encourage the use of these assessments as a reference framework to help standardise neurological clinical scoring of large animal models.
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Affiliation(s)
- Samantha L. Eaton
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
- Correspondence: (S.L.E.); (T.M.W.); Tel.: +44-(0)-131-651-9125 (S.L.E.); +44-(0)-131-651-9233 (T.M.W.)
| | - Fraser Murdoch
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Nina M. Rzechorzek
- Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Gerard Thompson
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK
- Department of Clinical Neurosciences, NHS Lothian, 50 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Claudia Hartley
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Benjamin Thomas Blacklock
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Chris Proudfoot
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Simon G. Lillico
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Peter Tennant
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Adrian Ritchie
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - James Nixon
- The Large Animal Research & Imaging Facility, Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Paul M. Brennan
- Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Stefano Guido
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
- Bioresearch & Veterinary Services, University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK
| | - Nadia L. Mitchell
- Faculty of Agriculture and Life Sciences, Lincoln University, P.O. Box 85084, Lincoln 7647, New Zealand
| | - David N. Palmer
- Faculty of Agriculture and Life Sciences, Lincoln University, P.O. Box 85084, Lincoln 7647, New Zealand
| | - C. Bruce A. Whitelaw
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
| | - Jonathan D. Cooper
- Departments of Pediatrics, Genetics, and Neurology, Washington University School of Medicine in St. Louis, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Thomas M. Wishart
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Roslin, Midlothian EH25 9RG, UK
- Correspondence: (S.L.E.); (T.M.W.); Tel.: +44-(0)-131-651-9125 (S.L.E.); +44-(0)-131-651-9233 (T.M.W.)
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17
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Verbal fluency in normal aging and cognitive decline: Results of a longitudinal study. COMPUT SPEECH LANG 2021. [DOI: 10.1016/j.csl.2021.101195] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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18
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19
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Martínez-Nicolás I, Llorente TE, Martínez-Sánchez F, Meilán JJG. Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article. Front Psychol 2021; 12:620251. [PMID: 33833713 PMCID: PMC8021952 DOI: 10.3389/fpsyg.2021.620251] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/15/2021] [Indexed: 11/25/2022] Open
Abstract
Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy. Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists. Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment. Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.
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Affiliation(s)
- Israel Martínez-Nicolás
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | - Thide E Llorente
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | | | - Juan José G Meilán
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
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20
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Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Giardina E, Cascella R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. J Pers Med 2021; 11:280. [PMID: 33917161 PMCID: PMC8067806 DOI: 10.3390/jpm11040280] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/05/2021] [Accepted: 04/06/2021] [Indexed: 12/13/2022] Open
Abstract
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases through the integration of all sources of biomedical data.
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Affiliation(s)
- Andrea Termine
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
| | - Carlo Fabrizio
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Claudia Strafella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Valerio Caputo
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Laura Petrosini
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Carlo Caltagirone
- IRCCS Santa Lucia Foundation, Department of Clinical and Behavioral Neurology, 00179 Rome, Italy;
| | - Emiliano Giardina
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- UILDM Lazio ONLUS Foundation, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy
| | - Raffaella Cascella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedical Sciences, Catholic University Our Lady of Good Counsel, 1000 Tirana, Albania
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O'Malley RPD, Mirheidari B, Harkness K, Reuber M, Venneri A, Walker T, Christensen H, Blackburn D. Fully automated cognitive screening tool based on assessment of speech and language. J Neurol Neurosurg Psychiatry 2020; 92:jnnp-2019-322517. [PMID: 33219045 DOI: 10.1136/jnnp-2019-322517] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 07/16/2020] [Accepted: 09/22/2020] [Indexed: 11/03/2022]
Abstract
INTRODUCTION Recent years have seen an almost sevenfold rise in referrals to specialist memory clinics. This has been associated with an increased proportion of patients referred with functional cognitive disorder (FCD), that is, non-progressive cognitive complaints. These patients are likely to benefit from a range of interventions (eg, psychotherapy) distinct from the requirements of patients with neurodegenerative cognitive disorders. We have developed a fully automated system, 'CognoSpeak', which enables risk stratification at the primary-secondary care interface and ongoing monitoring of patients with memory concerns. METHODS We recruited 15 participants to each of four groups: Alzheimer's disease (AD), mild cognitive impairment (MCI), FCD and healthy controls. Participants responded to 12 questions posed by a computer-presented talking head. Automatic analysis of the audio and speech data involved speaker segmentation, automatic speech recognition and machine learning classification. RESULTS CognoSpeak could distinguish between participants in the AD or MCI groups and those in the FCD or healthy control groups with a sensitivity of 86.7%. Patients with MCI were identified with a sensitivity of 80%. DISCUSSION Our fully automated system achieved levels of accuracy comparable to currently available, manually administered assessments. Greater accuracy should be achievable through further system training with a greater number of users, the inclusion of verbal fluency tasks and repeat assessments. The current data supports CognoSpeak's promise as a screening and monitoring tool for patients with MCI. Pending confirmation of these findings, it may allow clinicians to offer patients at low risk of dementia earlier reassurance and relieve pressures on specialist memory services.
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Affiliation(s)
| | - Bahman Mirheidari
- Department of Computer Science, The University of Sheffield, Sheffield, Sheffield, UK
| | - Kirsty Harkness
- Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK
| | - Markus Reuber
- Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK
| | - Annalena Venneri
- Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK
| | - Traci Walker
- Human Communication Sciences, The University of Sheffield, Sheffield, Sheffield, UK
| | - Heidi Christensen
- Department of Computer Science, The University of Sheffield, Sheffield, Sheffield, UK
| | - Dan Blackburn
- Neuroscience, The University of Sheffield, Sheffield, Sheffield, UK
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Affiliation(s)
- Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming University, Taipei, Taiwan; Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan.
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