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Chenain L, Riad R, Fraisse N, Jubin C, Morgado G, Youssov K, Lunven M, Bachoud-Levi AC. Graph methods to infer spatial disturbances: Application to Huntington's Disease's speech. Cortex 2024; 176:144-160. [PMID: 38795650 DOI: 10.1016/j.cortex.2024.04.014] [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: 11/19/2023] [Revised: 03/07/2024] [Accepted: 04/25/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVE Huntington's Disease (HD) is an inherited neurodegenerative disease caused by the mutation of the Htt gene, impacting all aspects of living and functioning. Among cognitive disabilities, spatial capacities are impaired, but their monitoring remains scarce as limited by lengthy experts' assessments. Language offers an alternative medium to evaluate patients' performance in HD. Yet, its capacities to assess HD's spatial abilities are unknown. Here, we aimed to bring proof-of-concept that HD's spatial deficits can be assessed through speech. METHODS We developed the Spatial Description Model to graphically represent spatial relations described during the Cookie Theft Picture (CTP) task. We increased the sensitivity of our model by using only sentences with spatial terms, unlike previous studies in Alzheimer's disease. 78 carriers of the mutant Htt, including 56 manifest and 22 premanifest individuals, as well as 25 healthy controls were included from the BIOHD & (NCT01412125) & Repair-HD (NCT03119246) cohorts. The convergence and divergence of the model were validated using the SelfCog battery. RESULTS Our Spatial Description Model was the only one among the four assessed approaches, revealing that individuals with manifest HD expressed fewer spatial relations and engaged in less spatial exploration compared to healthy controls. Their graphs correlated with both visuospatial and language SelfCog performances, but not with motor, executive nor memory functions. CONCLUSIONS We provide the proof-of-concept using our Spatial Description Model that language can grasp HD patient's spatial disturbances. By adding spatial capabilities to the panel of functions tested by the language, it paves the way for eventual remote clinical application.
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Affiliation(s)
- Lucie Chenain
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France; ALMAnaCH, INRIA, 75012 Paris, France; Learning Planet Institute, Université de Paris, 75004 Paris, France
| | - Rachid Riad
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France
| | - Nicolas Fraisse
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France; AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
| | - Cécilia Jubin
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France; AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
| | - Graça Morgado
- Inserm, Centre d'Investigation Clinique 1430, AP-HP, Hôpital Henri Mondor, Créteil, France
| | - Katia Youssov
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France; AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France; Inserm, Centre d'Investigation Clinique 1430, AP-HP, Hôpital Henri Mondor, Créteil, France
| | - Marine Lunven
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France.
| | - Anne-Catherine Bachoud-Levi
- Département d'Etudes Cognitives, École normale supérieure, PSL University, NeuroPsychologie Interventionnelle, 75005 Paris, France; Univ Paris Est Créteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Créteil, France; NeurATRIS Créteil, France; AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France; Inserm, Centre d'Investigation Clinique 1430, AP-HP, Hôpital Henri Mondor, Créteil, France
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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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3
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Camilleri JA, Volkening J, Heim S, Mochalski LN, Neufeld H, Schlothauer N, Kuhles G, Eickhoff SB, Weis S. SpEx: a German-language dataset of speech and executive function performance. Sci Rep 2024; 14:9431. [PMID: 38658576 PMCID: PMC11043440 DOI: 10.1038/s41598-024-58617-3] [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: 07/17/2023] [Accepted: 04/01/2024] [Indexed: 04/26/2024] Open
Abstract
This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.
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Affiliation(s)
- Julia A Camilleri
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany.
| | - Julia Volkening
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
- PeakProfiling GmbH, Eschenallee 36, 14050, Berlin, Germany
| | - Stefan Heim
- Institute of Neuroscience and Medicine (INM-1 Structural and Functional Organisation of the Brain), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- Department of Neurology, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Lisa N Mochalski
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Hannah Neufeld
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Natalie Schlothauer
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Gianna Kuhles
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
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Harris C, Tang Y, Birnbaum E, Cherian C, Mendhe D, Chen MH. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies. Arch Clin Neuropsychol 2024; 39:290-304. [PMID: 38520381 DOI: 10.1093/arclin/acae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 03/25/2024] Open
Abstract
Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.
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Affiliation(s)
- Che Harris
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Yingfei Tang
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Eliana Birnbaum
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Christine Cherian
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Dinesh Mendhe
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Michelle H Chen
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
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5
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Griffin JM, Kim K, Finnie DM, Lapid MI, Gaugler JE, Batthyány A, Bangerter LR, Biggar VS, Frangiosa T. Developing and describing a typology of lucid episodes among people with Alzheimer's disease and related dementias. Alzheimers Dement 2024; 20:2434-2443. [PMID: 38305566 PMCID: PMC11032560 DOI: 10.1002/alz.13667] [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: 03/09/2023] [Revised: 11/28/2023] [Accepted: 12/03/2023] [Indexed: 02/03/2024]
Abstract
INTRODUCTION This study examined lucid episodes among people living with late-stage Alzheimer's disease and related dementias (PLWD) and then developed a typology of these episodes to help characterize them. METHODS Family caregivers of PLWD provided information about witnessed episodes, including proximity to death, cognitive status, duration, communication quality, and circumstances prior to lucid episodes on up to two episodes (caregiver N = 151; episode N = 279). Latent class analysis was used to classify and characterize empirically distinct clusters of lucid episodes. RESULTS Four lucid episode types were identified. The most common type occurred during visits with family and among PLWD who lived > 6 months after the episode. The least common type coincided with family visits and occurred within 7 days of the PLWD's death. DISCUSSION Findings suggest that multiple types of lucid episodes exist; not all signal impending death; and some, but not all, are precipitated by external stimuli.
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Affiliation(s)
- Joan M. Griffin
- Division of Health Care Delivery Research and Kern Center for the Science of Healthcare DeliveryMayo ClinicRochesterMinnesotaUSA
- Kern Center for the Science of Healthcare DeliveryMayo ClinicRochesterMinnesotaUSA
| | - Kyungmin Kim
- Department of Child Development and Family StudiesResearch Institute of Human EcologySeoul National UniversitySeoulRepublic of Korea
| | - Dawn M. Finnie
- Kern Center for the Science of Healthcare DeliveryMayo ClinicRochesterMinnesotaUSA
| | - Maria I. Lapid
- Department of Psychiatry and PsychologyMayo ClinicRochesterMinnesotaUSA
| | - Joseph E. Gaugler
- School of Public HealthUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Alexander Batthyány
- Viktor Frankl Research Institute for Theoretical Psychology and Personalist StudiesPázmány Péter Catholic UniversityBudapestHungary
| | - Lauren R. Bangerter
- Health Economics and Aging Research InstituteMedStar Health Research InstituteHyattsvilleMarylandUSA
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Powell D. Walk, talk, think, see and feel: harnessing the power of digital biomarkers in healthcare. NPJ Digit Med 2024; 7:45. [PMID: 38396034 PMCID: PMC10891042 DOI: 10.1038/s41746-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/18/2024] [Indexed: 02/25/2024] Open
Affiliation(s)
- Dylan Powell
- Faculty of Health Sciences & Sport, University of Stirling, Stirling, UK.
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7
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Geronimo A. Turning up the volume on neuromuscular cough medicine. Muscle Nerve 2024; 69:129-130. [PMID: 38037436 DOI: 10.1002/mus.28016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
See article on pages 213–217 in this issue.
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Affiliation(s)
- Andrew Geronimo
- Neurology, Penn State College of Medicine, Hershey, Pennsylvania, USA
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Erickson CM, Wexler A, Largent EA. Alzheimer's in the modern age: Ethical challenges in the use of digital monitoring to identify cognitive changes. Inform Health Soc Care 2024; 49:1-13. [PMID: 38116960 PMCID: PMC11001527 DOI: 10.1080/17538157.2023.2294203] [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] [Indexed: 12/21/2023]
Abstract
Pushes toward earlier detection of Alzheimer's disease (AD)-related cognitive changes are creating interest in leveraging technologies, like cellphones, that are already widespread and well-equipped for data collection to facilitate digital monitoring for AD. Studies are ongoing to identify and validate potential "digital biomarkers" that might indicate someone has or is at risk of developing AD dementia. Digital biomarkers for AD have potential as a tool in aiding more timely diagnosis, though more robust research is needed to support their validity and utility. While there are grounds for optimism, leveraging digital monitoring and informatics for cognitive changes also poses ethical challenges, related to topics such as algorithmic bias, consent, and data privacy and security. As we confront the modern era of Alzheimer's disease, individuals, companies, regulators and policymakers alike must prepare for a future in which our day-to-day interactions with technology in our daily life may identify AD-related cognitive changes.
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Affiliation(s)
- Claire M Erickson
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily A Largent
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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9
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Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, Dehsarvi A, Lengyel I, Lourida I, Madan CR, Marzi SJ, Proitsi P, Rajkumar AP, Rittman T, Silajdžić E, Tamburin S, Ranson JM, Llewellyn DJ. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimers Dement 2023; 19:5860-5871. [PMID: 37654029 PMCID: PMC10840606 DOI: 10.1002/alz.13390] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/02/2023]
Abstract
With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.
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Affiliation(s)
| | - Eric L Harshfield
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Headington, UK
| | - AmanPreet Badhwar
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Natasha Clarke
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Amir Dehsarvi
- School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Imre Lengyel
- Wellcome-Wolfson Institute of Experimental Medicine, Queen's University, Belfast, UK
| | - Ilianna Lourida
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute at Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anto P Rajkumar
- Institute of Mental Health, Mental Health and Clinical Neurosciences academic unit, University of Nottingham, Nottingham, UK, Mental health services of older people, Nottinghamshire healthcare NHS foundation trust, Nottingham, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Edina Silajdžić
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Janice M Ranson
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - David J Llewellyn
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Neel A, Krasilshchikova S, Richardson JD, Arenas R, Bennett L, Banks S, Ritter A, Bernick C. Articulation Rate, Pauses, and Disfluencies in Professional Fighters: Potential Speech Biomarkers for Repetitive Head Injury. J Head Trauma Rehabil 2023; 38:458-466. [PMID: 36701308 PMCID: PMC10368786 DOI: 10.1097/htr.0000000000000841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVE As part of a larger study dedicated to identifying speech and language biomarkers of neurological decline associated with repetitive head injury (RHI) in professional boxers and mixed martial artists (MMAs), we examined articulation rate, pausing, and disfluency in passages read aloud by participants in the Professional Athletes Brain Health Study. SETTING A large outpatient medical center specializing in neurological care. PARTICIPANTS, DESIGN, AND MAIN MEASURES Passages read aloud by 60 boxers, 40 MMAs, and 55 controls were acoustically analyzed to determine articulation rate (the number of syllables produced per second), number and duration of pauses, and number and duration of disfluencies in this observational study. RESULTS Both boxers and MMAs differed from controls in articulation rate, producing syllables at a slower rate than controls by nearly half a syllable per second on average. Boxers produced significantly more pauses and disfluencies in passages read aloud than MMAs and controls. CONCLUSIONS Slower articulation rate in both boxers and MMA fighters compared with individuals with no history of RHI and the increased occurrence of pauses and disfluencies in the speech of boxers suggest changes in speech motor behavior that may relate to RHI. These speech characteristics can be measured in everyday speaking conditions and by automatic recognition systems, so they have the potential to serve as effective, noninvasive clinical indicators for RHI-associated neurological decline.
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Affiliation(s)
- Amy Neel
- Department of Speech and Hearing Sciences, University of New Mexico, Albuquerque (Drs Neel, Richardson, and Arenas and Ms Krasilshchikova); Pickup Family Neurosciences Institute, Hoag Memorial Hospital Presbyterian, Newport Beach, California (Dr Bennett); Department of Neurosciences, University of California, San Diego, La Jolla (Dr Banks); and Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada (Drs Ritter and Bernick)
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Pyper E, McKeown S, Hartmann-Boyce J, Powell J. Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review. J Med Internet Res 2023; 25:e46992. [PMID: 37819698 PMCID: PMC10600647 DOI: 10.2196/46992] [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: 03/10/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Digital health technologies (DHTs) play an ever-expanding role in health care management and delivery. Beyond their use as interventions, DHTs also serve as a vehicle for real-world data collection to characterize patients, their care journeys, and their responses to other clinical interventions. There is a need to comprehensively map the evidence-across all conditions and technology types-on DHT measurement of patient outcomes in the real world. OBJECTIVE We aimed to investigate the use of DHTs to measure real-world clinical outcomes using patient-generated data. METHODS We conducted this systematic scoping review in accordance with the Joanna Briggs Institute methodology. Detailed eligibility criteria documented in a preregistered protocol informed a search strategy for the following databases: MEDLINE (Ovid), CINAHL, Cochrane (CENTRAL), Embase, PsycINFO, ClinicalTrials.gov, and the EU Clinical Trials Register. We considered studies published between 2000 and 2022 wherein digital health data were collected, passively or actively, from patients with any specified health condition outside of clinical visits. Categories for key concepts, such as DHT type and analytical applications, were established where needed. Following screening and full-text review, data were extracted and analyzed using predefined fields, and findings were reported in accordance with established guidelines. RESULTS The search strategy identified 11,015 publications, with 7308 records after duplicates and reviews were removed. After screening and full-text review, 510 studies were included for extraction. These studies encompassed 169 different conditions in over 20 therapeutic areas and 44 countries. The DHTs used for mental health and addictions research (111/510, 21.8%) were the most prevalent. The most common type of DHT, mobile apps, was observed in approximately half of the studies (250/510, 49%). Most studies used only 1 DHT (346/510, 67.8%); however, the majority of technologies used were able to collect more than 1 type of data, with the most common being physiological data (189/510, 37.1%), clinical symptoms data (188/510, 36.9%), and behavioral data (171/510, 33.5%). Overall, there has been real growth in the depth and breadth of evidence, number of DHT types, and use of artificial intelligence and advanced analytics over time. CONCLUSIONS This scoping review offers a comprehensive view of the variety of types of technology, data, collection methods, analytical approaches, and therapeutic applications within this growing body of evidence. To unlock the full potential of DHT for measuring health outcomes and capturing digital biomarkers, there is a need for more rigorous research that goes beyond technology validation to demonstrate whether robust real-world data can be reliably captured from patients in their daily life and whether its capture improves patient outcomes. This study provides a valuable repository of DHT studies to inform subsequent research by health care providers, policy makers, and the life sciences industry. TRIAL REGISTRATION Open Science Framework 5TMKY; https://osf.io/5tmky/.
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Affiliation(s)
- Evelyn Pyper
- Department for Continuing Education, University of Oxford, Oxford, United Kingdom
| | - Sarah McKeown
- Department for Continuing Education, University of Oxford, Oxford, United Kingdom
| | - Jamie Hartmann-Boyce
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Department of Health Promotion and Policy, University of Massachusetts Amherst, Amherst, MA, United States
| | - John Powell
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Marchese MR, Sensoli F, Campagnini S, Cianchetti M, Nacci A, Ursino F, D’Alatri L, Galli J, Carrozza MC, Paludetti G, Mannini A. Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings. ACTA OTORHINOLARYNGOLOGICA ITALICA : ORGANO UFFICIALE DELLA SOCIETA ITALIANA DI OTORINOLARINGOLOGIA E CHIRURGIA CERVICO-FACCIALE 2023; 43:317-323. [PMID: 37519137 PMCID: PMC10551729 DOI: 10.14639/0392-100x-n2309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/22/2023] [Indexed: 08/01/2023]
Abstract
Objective The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis. Materials and methods In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts. The model's performance was presented as accuracy and average F1-score. The results were also analysed in male (M) and female (F) subgroups. Results The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M and F, respectively. Better performances were observed in the detection of cysts and nodules (58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The results on each lesion and the different patterns of the model on M and F are in line with clinical observations, obtaining better results on F and detection of sensitive polyps in M. Conclusions This study showed moderately accurate detection of four types of BLVF using acoustic signals. The analysis of the diagnostic results on gender subgroups highlights different behaviours of the diagnostic model.
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Affiliation(s)
- Maria Raffaella Marchese
- Unità Operativa Complessa di Otorinolaringoiatria, Dipartimento di Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Federico Sensoli
- Institute of Biorobotics, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Silvia Campagnini
- Institute of Biorobotics, Scuola Superiore Sant’Anna, Pontedera, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy
| | - Matteo Cianchetti
- Institute of Biorobotics, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Andrea Nacci
- U.O. Otorinolaringoiatria Audiologia e Foniatria, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Francesco Ursino
- Istituto Nazionale di Ricerche in Foniatria “G. Bartalena”, Pisa, Italy
| | - Lucia D’Alatri
- Unità Operativa Complessa di Otorinolaringoiatria, Dipartimento di Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Sezione di Otorinolaringoiatria, Dipartimento Universitario Testa-Collo e Organi di Senso, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Jacopo Galli
- Unità Operativa Complessa di Otorinolaringoiatria, Dipartimento di Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Sezione di Otorinolaringoiatria, Dipartimento Universitario Testa-Collo e Organi di Senso, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Gaetano Paludetti
- Unità Operativa Complessa di Otorinolaringoiatria, Dipartimento di Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Sezione di Otorinolaringoiatria, Dipartimento Universitario Testa-Collo e Organi di Senso, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea Mannini
- Institute of Biorobotics, Scuola Superiore Sant’Anna, Pontedera, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy
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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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Rowe HP, Shellikeri S, Yunusova Y, Chenausky KV, Green JR. Quantifying articulatory impairments in neurodegenerative motor diseases: A scoping review and meta-analysis of interpretable acoustic features. INTERNATIONAL JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2023; 25:486-499. [PMID: 36001500 PMCID: PMC9950294 DOI: 10.1080/17549507.2022.2089234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
PURPOSE Neurodegenerative motor diseases (NMDs) have devastating effects on the lives of patients and their loved ones, in part due to the impact of neurologic abnormalities on speech, which significantly limits functional communication. Clinical speech researchers have thus spent decades investigating speech features in populations suffering from NMDs. Features of impaired articulatory function are of particular interest given their detrimental impact on intelligibility, their ability to encode a variety of distinct movement disorders, and their potential as diagnostic indicators of neurodegenerative diseases. The objectives of this scoping review were to identify (1) which components of articulation (i.e. coordination, consistency, speed, precision, and repetition rate) are the most represented in the acoustic literature on NMDs; (2) which acoustic articulatory features demonstrate the most potential for detecting speech motor dysfunction in NMDs; and (3) which articulatory components are the most impaired within each NMD. METHOD This review examined literature published between 1976 and 2020. Studies were identified from six electronic databases using predefined key search terms. The first research objective was addressed using a frequency count of studies investigating each articulatory component, while the second and third objectives were addressed using meta-analyses. RESULT Findings from 126 studies revealed a considerable emphasis on articulatory precision. Of the 24 features included in the meta-analyses, vowel dispersion/distance and stop gap duration exhibited the largest effects when comparing the NMD population to controls. The meta-analyses also revealed divergent patterns of articulatory performance across disease types, providing evidence of unique profiles of articulatory impairment. CONCLUSION This review illustrates the current state of the literature on acoustic articulatory features in NMDs. By highlighting the areas of need within each articulatory component and disease group, this work provides a foundation on which clinical researchers, speech scientists, neurologists, and computer science engineers can develop research questions that will both broaden and deepen the understanding of articulatory impairments in NMDs.
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Affiliation(s)
- Hannah P Rowe
- MGH Institute of Health Professions, Boston, MA, USA
| | - Sanjana Shellikeri
- Department of Speech-Language Pathology & Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Yana Yunusova
- Department of Speech-Language Pathology & Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
| | - Karen V Chenausky
- MGH Institute of Health Professions, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA, and
| | - Jordan R Green
- MGH Institute of Health Professions, Boston, MA, USA
- Speech and Hearing Biosciences and Technology Program, Harvard University, Cambridge, MA, USA
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15
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Richter V, Neumann M, Green JR, Richburg B, Roesler O, Kothare H, Ramanarayanan V. Remote Assessment for ALS using Multimodal Dialog Agents: Data Quality, Feasibility and Task Compliance. INTERSPEECH 2023; 2023:5441-5445. [PMID: 37791043 PMCID: PMC10547018 DOI: 10.21437/interspeech.2023-2115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
We investigate the feasibility, task compliance and audiovisual data quality of a multimodal dialog-based solution for remote assessment of Amyotrophic Lateral Sclerosis (ALS). 53 people with ALS and 52 healthy controls interacted with Tina, a cloud-based conversational agent, in performing speech tasks designed to probe various aspects of motor speech function while their audio and video was recorded. We rated a total of 250 recordings for audio/video quality and participant task compliance, along with the relative frequency of different issues observed. We observed excellent compliance (98%) and audio (95.2%) and visual quality rates (84.8%), resulting in an overall yield of 80.8% recordings that were both compliant and of high quality. Furthermore, recording quality and compliance were not affected by level of speech severity and did not differ significantly across end devices. These findings support the utility of dialog systems for remote monitoring of speech in ALS.
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Tröger J, Baltes J, Baykara E, Kasper E, Kring M, Linz N, Robin J, Schäfer S, Schneider A, Hermann A. PROSA-a multicenter prospective observational study to develop low-burden digital speech biomarkers in ALS and FTD. Amyotroph Lateral Scler Frontotemporal Degener 2023:1-10. [PMID: 37516990 DOI: 10.1080/21678421.2023.2239312] [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: 03/24/2023] [Accepted: 07/15/2023] [Indexed: 08/01/2023]
Abstract
Objective: There is a need for novel biomarkers that can indicate disease state, project disease progression, or assess response to treatment for amyotrophic lateral sclerosis (ALS) and associated neurodegenerative diseases such as frontotemporal dementia (FTD). Digital biomarkers are especially promising as they can be collected non-invasively and at low burden for patients. Speech biomarkers have the potential to objectively measure cognitive, motor as well as respiratory symptoms at low-cost and in a remote fashion using widely available technology such as telephone calls. Methods: The PROSA study aims to develop and evaluate low-burden frequent prognostic digital speech biomarkers. The main goal is to create a single, easy-to-perform battery that serves as a valid and reliable proxy for cognitive, respiratory, and motor domains in ALS and FTD. The study will be a multicenter 12-months observational study aiming to include 75 ALS and 75 FTD patients as well as 50 healthy controls and build on three established longitudinal cohorts: DANCER, DESCRIBE-ALS and DESCRIBE-FTD. In addition to the extensive clinical phenotyping in DESCRIBE, PROSA collects a comprehensive speech protocol in fully remote and automated fashion over the telephone at four time points. This longitudinal speech data, together with gold standard measures, will allow advanced speech analysis using artificial intelligence for the development of speech-based phenotypes of ALS and FTD patients measuring cognitive, motor and respiratory symptoms. Conclusion: Speech-based phenotypes can be used to develop diagnostic and prognostic models predicting clinical change. Results are expected to have implications for future clinical trial stratification as well as supporting innovative trial designs in ALS and FTD.
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Affiliation(s)
| | - Judith Baltes
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Elisabeth Kasper
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany
- Department of Neurology, University Medical Center Rostock, Rostock, Germany
| | - Martha Kring
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany
| | | | | | | | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Andreas Hermann
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany
- Center for Transdisciplinary Neurosciences Rostock (CTNR), University Medical Center Rostock, Rostock, Germany, and
- Translational Neurodegeneration Section "Albrecht-Kossel", Department of Neurology, University Medical Center Rostock, Rostock, Germany
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17
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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: 3] [Impact Index Per Article: 3.0] [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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Brady B, Zhou S, Ashworth D, Zheng L, Eramudugolla R, Huque MH, Anstey KJ. A Technology-Enriched Approach to Studying Microlongitudinal Aging Among Adults Aged 18 to 85 Years: Protocol for the Labs Without Walls Study. JMIR Res Protoc 2023; 12:e47053. [PMID: 37410527 PMCID: PMC10360017 DOI: 10.2196/47053] [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: 03/06/2023] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Traditional longitudinal aging research involves studying the same individuals over a long period, with measurement intervals typically several years apart. App-based studies have the potential to provide new insights into life-course aging by improving the accessibility, temporal specificity, and real-world integration of data collection. We developed a new research app for iOS named Labs Without Walls to facilitate the study of life-course aging. Combined with data collected using paired smartwatches, the app collects complex data including data from one-time surveys, daily diary surveys, repeated game-like cognitive and sensory tasks, and passive health and environmental data. OBJECTIVE The aim of this protocol is to describe the research design and methods of the Labs Without Walls study conducted between 2021 and 2023 in Australia. METHODS Overall, 240 Australian adults will be recruited, stratified by age group (18-25, 26-35, 36-45, 46-55, 56-65, 66-75, and 76-85 years) and sex at birth (male and female). Recruitment procedures include emails to university and community networks, as well as paid and unpaid social media advertisements. Participants will be invited to complete the study onboarding either in person or remotely. Participants who select face-to-face onboarding (n=approximately 40) will be invited to complete traditional in-person cognitive and sensory assessments to be cross-validated against their app-based counterparts. Participants will be sent an Apple Watch and headphones for use during the study period. Participants will provide informed consent within the app and then begin an 8-week study protocol, which includes scheduled surveys, cognitive and sensory tasks, and passive data collection using the app and a paired watch. At the conclusion of the study period, participants will be invited to rate the acceptability and usability of the study app and watch. We hypothesize that participants will be able to successfully provide e-consent, input survey data through the Labs Without Walls app, and have passive data collected over 8 weeks; participants will rate the app and watch as user-friendly and acceptable; the app will allow for the study of daily variability in self-perceptions of age and gender; and data will allow for the cross-validation of app- and laboratory-based cognitive and sensory tasks. RESULTS Recruitment began in May 2021, and data collection was completed in February 2023. The publication of preliminary results is anticipated in 2023. CONCLUSIONS This study will provide evidence regarding the acceptability and usability of the research app and paired watch for studying life-course aging processes on multiple timescales. The feedback obtained will be used to improve future iterations of the app, explore preliminary evidence for intraindividual variability in self-perceptions of aging and gender expression across the life span, and explore the associations between performance on app-based cognitive and sensory tests and that on similar traditional cognitive and sensory tests. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/47053.
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Affiliation(s)
- Brooke Brady
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - Shally Zhou
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - Daniel Ashworth
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
| | - Lidan Zheng
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - Ranmalee Eramudugolla
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - Md Hamidul Huque
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - Kaarin Jane Anstey
- School of Psychology, University of New South Wales, Randwick, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
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Teepe GW, Lukic YX, Kleim B, Jacobson NC, Schneider F, Santhanam P, Fleisch E, Kowatsch T. Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study. BMC Psychol 2023; 11:186. [PMID: 37349832 PMCID: PMC10288725 DOI: 10.1186/s40359-023-01215-1] [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: 08/29/2022] [Accepted: 05/18/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). AIM With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. METHOD Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. DISCUSSION Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).
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Affiliation(s)
- Gisbert W. Teepe
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006 Zürich, Switzerland
| | - Yanick X. Lukic
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006 Zürich, Switzerland
| | - Birgit Kleim
- Department of Psychology, Experimental Psychopathology and Psychotherapy, Binzmühlestrasse 14, Box 8, 8050 Zürich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Lenggstrasse 31, 8032 Zürich, Switzerland
| | - Nicholas C. Jacobson
- Departments of Biomedical Data Science and Psychiatry, Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Lebanon, NH 03766 USA
| | - Fabian Schneider
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006 Zürich, Switzerland
| | - Prabhakaran Santhanam
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006 Zürich, Switzerland
| | - Elgar Fleisch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006 Zürich, Switzerland
- Centre for Digital Health Intervention, Institute of Technology Management, University of St.Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland
| | - Tobias Kowatsch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/56, 8006 Zürich, Switzerland
- Centre for Digital Health Intervention, Institute of Technology Management, University of St.Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland
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Martínez-Nicolás I, Martínez-Sánchez F, Ivanova O, Meilán JJG. Reading and lexical-semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease. Sci Rep 2023; 13:9728. [PMID: 37322073 PMCID: PMC10272227 DOI: 10.1038/s41598-023-36804-y] [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: 03/27/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Age-related cognitive impairment have increased dramatically in recent years, which has risen the interes in developing screening tools for mild cognitive impairment and Alzheimer's disease. Speech analysis allows to exploit the behavioral consequences of cognitive deficits on the patient's vocal performance so that it is possible to identify pathologies affecting speech production such as dementia. Previous studies have further shown that the speech task used determines how the speech parameters are altered. We aim to combine the impairments in several speech production tasks in order to improve the accuracy of screening through speech analysis. The sample consists of 72 participants divided into three equal groups of healthy older adults, people with mild cognitive impairment, or Alzheimer's disease, matched by age and education. A complete neuropsychological assessment and two voice recordings were performed. The tasks required the participants to read a text, and complete a sentence with semantic information. A stepwise linear discriminant analysis was performed to select speech parameters with discriminative power. The discriminative functions obtained an accuracy of 83.3% in simultaneous classifications of several levels of cognitive impairment. It would therefore be a promising screening tool for dementia.
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Affiliation(s)
| | | | - Olga Ivanova
- Faculty of Philology, University of Salamanca, 37008, Salamanca, Spain
| | - Juan J G Meilán
- Faculty of Psychology, University of Salamanca, 37008, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, 37007, Salamanca, Spain
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21
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Shellikeri S, Cho S, Ash S, Gonzalez-Recober C, McMillan CT, Elman L, Quinn C, Amado DA, Baer M, Irwin DJ, Massimo L, Olm C, Liberman M, Grossman M, Nevler N. Digital markers of motor speech impairments in natural speech of patients with ALS-FTD spectrum disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.29.23289308. [PMID: 37205390 PMCID: PMC10187360 DOI: 10.1101/2023.04.29.23289308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Background and objectives Patients with ALS-FTD spectrum disorders (ALS-FTSD) have mixed motor and cognitive impairments and require valid and quantitative assessment tools to support diagnosis and tracking of bulbar motor disease. This study aimed to validate a novel automated digital speech tool that analyzes vowel acoustics from natural, connected speech as a marker for impaired articulation due to bulbar motor disease in ALS-FTSD. Methods We used an automatic algorithm called Forced Alignment Vowel Extraction (FAVE) to detect spoken vowels and extract vowel acoustics from 1 minute audio-recorded picture descriptions. Using automated acoustic analysis scripts, we derived two articulatory-acoustic measures: vowel space area (VSA, in Bark 2 ) which represents tongue range-of-motion (size), and average second formant slope of vowel trajectories (F2 slope) which represents tongue movement speed. We compared vowel measures between ALS with and without clinically-evident bulbar motor disease (ALS+bulbar vs. ALS-bulbar), behavioral variant frontotemporal dementia (bvFTD) without a motor syndrome, and healthy controls (HC). We correlated impaired vowel measures with bulbar disease severity, estimated by clinical bulbar scores and perceived listener effort, and with MRI cortical thickness of the orobuccal part of the primary motor cortex innervating the tongue (oralPMC). We also tested correlations with respiratory capacity and cognitive impairment. Results Participants were 45 ALS+bulbar (30 males, mean age=61±11), 22 ALS-nonbulbar (11 males, age=62±10), 22 bvFTD (13 males, age=63±7), and 34 HC (14 males, age=69±8). ALS+bulbar had smaller VSA and shallower average F2 slopes than ALS-bulbar (VSA: | d |=0.86, p =0.0088; F2 slope: | d |=0.98, p =0.0054), bvFTD (VSA: | d |=0.67, p =0.043; F2 slope: | d |=1.4, p <0.001), and HC (VSA: | d |=0.73, p =0.024; F2 slope: | d |=1.0, p <0.001). Vowel measures declined with worsening bulbar clinical scores (VSA: R=0.33, p =0.033; F2 slope: R=0.25, p =0.048), and smaller VSA was associated with greater listener effort (R=-0.43, p =0.041). Shallower F2 slopes were related to cortical thinning in oralPMC (R=0.50, p =0.03). Neither vowel measure was associated with respiratory nor cognitive test scores. Conclusions Vowel measures extracted with automatic processing from natural speech are sensitive to bulbar motor disease in ALS-FTD and are robust to cognitive impairment.
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DuBord AY, Paolillo EW, Staffaroni AM. Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases. J Diabetes Sci Technol 2023:19322968231171399. [PMID: 37102472 DOI: 10.1177/19322968231171399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.
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Affiliation(s)
- Ashley Y DuBord
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Technology Society, Burlingame, CA, USA
| | - Emily W Paolillo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
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23
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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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Comparison of In-Person and Online Recordings in the Clinical Teleassessment of Speech Production: A Pilot Study. Brain Sci 2023; 13:brainsci13020342. [PMID: 36831885 PMCID: PMC9953872 DOI: 10.3390/brainsci13020342] [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: 01/24/2023] [Revised: 02/12/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
In certain circumstances, speech and language therapy is proposed in telepractice as a practical alternative to in-person services. However, little is known about the minimum quality requirements of recordings in the teleassessment of motor speech disorders (MSD) utilizing validated tools. The aim here is to examine the comparability of offline analyses based on speech samples acquired from three sources: (1) in-person recordings with high quality material, serving as the baseline/gold standard; (2) in-person recordings with standard equipment; (3) online recordings from videoconferencing. Speech samples were recorded simultaneously from these three sources in fifteen neurotypical speakers performing a screening battery of MSD and analyzed by three speech and language therapists. Intersource and interrater agreements were estimated with intraclass correlation coefficients on seventeen perceptual and acoustic parameters. While the interrater agreement was excellent for most speech parameters, especially on high quality in-person recordings, it decreased in online recordings. The intersource agreement was excellent for speech rate and mean fundamental frequency measures when comparing high quality in-person recordings to the other conditions. The intersource agreement was poor for voice parameters, but also for perceptual measures of intelligibility and articulation. Clinicians who plan to teleassess MSD should adapt their recording setting to the parameters they want to reliably interpret.
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25
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Applications of Speech Analysis in Psychiatry. Harv Rev Psychiatry 2023; 31:1-13. [PMID: 36608078 DOI: 10.1097/hrp.0000000000000356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
ABSTRACT The need for objective measurement in psychiatry has stimulated interest in alternative indicators of the presence and severity of illness. Speech may offer a source of information that bridges the subjective and objective in the assessment of mental disorders. We systematically reviewed the literature for articles exploring speech analysis for psychiatric applications. The utility of speech analysis depends on how accurately speech features represent clinical symptoms within and across disorders. We identified four domains of the application of speech analysis in the literature: diagnostic classification, assessment of illness severity, prediction of onset of illness, and prognosis and treatment outcomes. We discuss the findings in each of these domains, with a focus on how types of speech features characterize different aspects of psychopathology. Models that bring together multiple speech features can distinguish speakers with psychiatric disorders from healthy controls with high accuracy. Differentiating between types of mental disorders and symptom dimensions are more complex problems that expose the transdiagnostic nature of speech features. Convergent progress in speech research and computer sciences opens avenues for implementing speech analysis to enhance objectivity of assessment in clinical practice. Application of speech analysis will need to address issues of ethics and equity, including the potential to perpetuate discriminatory bias through models that learn from clinical assessment data. Methods that mitigate bias are available and should play a key role in the implementation of speech analysis.
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26
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Gumus M, DeSouza DD, Xu M, Fidalgo C, Simpson W, Robin J. Evaluating the utility of daily speech assessments for monitoring depression symptoms. Digit Health 2023; 9:20552076231180523. [PMID: 37426590 PMCID: PMC10328009 DOI: 10.1177/20552076231180523] [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: 05/19/2023] [Indexed: 07/11/2023] Open
Abstract
Objective Depression is a common mental health disorder and a major public health concern, significantly interfering with the lives of those affected. The complex clinical presentation of depression complicates symptom assessments. Day-to-day fluctuations of depression symptoms within an individual bring an additional barrier, since infrequent testing may not reveal symptom fluctuation. Digital measures such as speech can facilitate daily objective symptom evaluation. Here, we evaluated the effectiveness of daily speech assessment in characterizing speech fluctuations in the context of depression symptoms, which can be completed remotely, at a low cost and with relatively low administrative resources. Methods Community volunteers (N = 16) completed a daily speech assessment, using the Winterlight Speech App, and Patient Health Questionnaire-9 (PHQ-9) for 30 consecutive business days. We calculated 230 acoustic and 290 linguistic features from individual's speech and investigated their relationship to depression symptoms at the intra-individual level through repeated measures analyses. Results We observed that depression symptoms were linked to linguistic features, such as less frequent use of dominant and positive words. Greater depression symptomatology was also significantly correlated with acoustic features: reduced variability in speech intensity and increased jitter. Conclusions Our findings support the feasibility of using acoustic and linguistic features as a measure of depression symptoms and propose daily speech assessment as a tool for better characterization of symptom fluctuations.
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Affiliation(s)
- Melisa Gumus
- Winterlight Labs, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | | | - Mengdan Xu
- Winterlight Labs, Toronto, Ontario, Canada
| | | | - William Simpson
- Winterlight Labs, Toronto, Ontario, Canada
- McMaster University, Hamilton, Ontario, Canada
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Simmatis LER, Robin J, Pommée T, McKinlay S, Sran R, Taati N, Truong J, Koyani B, Yunusova Y. Validation of automated pipeline for the assessment of a motor speech disorder in amyotrophic lateral sclerosis (ALS). Digit Health 2023; 9:20552076231219102. [PMID: 38144173 PMCID: PMC10748679 DOI: 10.1177/20552076231219102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Background and objective Amyotrophic lateral sclerosis (ALS) frequently causes speech impairments, which can be valuable early indicators of decline. Automated acoustic assessment of speech in ALS is attractive, and there is a pressing need to validate such tools in line with best practices, including analytical and clinical validation. We hypothesized that data analysis using a novel speech assessment pipeline would correspond strongly to analyses performed using lab-standard practices and that acoustic features from the novel pipeline would correspond to clinical outcomes of interest in ALS. Methods We analyzed data from three standard speech assessment tasks (i.e., vowel phonation, passage reading, and diadochokinesis) in 122 ALS patients. Data were analyzed automatically using a pipeline developed by Winterlight Labs, which yielded 53 acoustic features. First, for analytical validation, data were analyzed using a lab-standard analysis pipeline for comparison. This was followed by univariate analysis (Spearman correlations between individual features in Winterlight and in-lab datasets) and multivariate analysis (sparse canonical correlation analysis (SCCA)). Subsequently, clinical validation was performed. This included univariate analysis (Spearman correlation between automated acoustic features and clinical measures) and multivariate analysis (interpretable autoencoder-based dimensionality reduction). Results Analytical validity was demonstrated by substantial univariate correlations (Spearman's ρ > 0.70) between corresponding pairs of features from automated and lab-based datasets, as well as interpretable SCCA feature groups. Clinical validity was supported by strong univariate correlations between automated features and clinical measures (Spearman's ρ > 0.70), as well as associations between multivariate outputs and clinical measures. Conclusion This novel, automated speech assessment feature set demonstrates substantial promise as a valid tool for analyzing impaired speech in ALS patients and for the further development of these technologies.
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Affiliation(s)
- Leif ER Simmatis
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | | | - Timothy Pommée
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Scotia McKinlay
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Rupinder Sran
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Niyousha Taati
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Justin Truong
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Yana Yunusova
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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28
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Martínez-Nicolás I, Llorente TE, Martínez-Sánchez F, Meilán JJG. Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment. Front Hum Neurosci 2022; 16:1057578. [PMID: 36590068 PMCID: PMC9798230 DOI: 10.3389/fnhum.2022.1057578] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction In this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia. Methods In this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed. Results We found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%. Discussion The predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.
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Affiliation(s)
- Israel Martínez-Nicolás
- Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain,*Correspondence: Israel Martínez-Nicolás,
| | - Thide E. Llorente
- Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain,Instituto de Neurociencias de Castilla y León, Salamanca, Spain
| | | | - Juan J. G. Meilán
- Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain,Instituto de Neurociencias de Castilla y León, Salamanca, Spain
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Fischer A, Elbeji A, Aguayo G, Fagherazzi G. Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research. Interact J Med Res 2022; 11:e40655. [PMID: 36378504 PMCID: PMC9668331 DOI: 10.2196/40655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/30/2022] [Accepted: 10/18/2022] [Indexed: 11/16/2022] Open
Abstract
The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.
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Affiliation(s)
- Aurelie Fischer
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Abir Elbeji
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Gloria Aguayo
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
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30
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Fahed VS, Doheny EP, Busse M, Hoblyn J, Lowery MM. Comparison of Acoustic Voice Features Derived From Mobile Devices and Studio Microphone Recordings. J Voice 2022:S0892-1997(22)00312-5. [PMID: 36379826 DOI: 10.1016/j.jvoice.2022.10.006] [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: 08/15/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 11/14/2022]
Abstract
OBJECTIVES/HYPOTHESIS Improvements in mobile device technology offer new opportunities for remote monitoring of voice for home and clinical assessment. However, there is a need to establish equivalence between features derived from signals recorded from mobile devices and gold standard microphone-preamplifiers. In this study acoustic voice features from android smartphone, tablet, and microphone-preamplifier recordings were compared. METHODS Data were recorded from 37 volunteers (20 female) with no history of speech disorder and six volunteers with Huntington's disease (HD) during sustained vowel (SV) phonation, reading passage (RP), and five syllable repetition (SR) tasks. The following features were estimated: fundamental frequency median and standard deviation (F0 and SD F0), harmonics-to-noise ratio (HNR), local jitter, relative average perturbation of jitter (RAP), five-point period perturbation quotient (PPQ5), difference of differences of amplitude and periods (DDA and DDP), shimmer, and amplitude perturbation quotients (APQ3, APQ5, and APQ11). RESULTS Bland-Altman analysis revealed good agreement between microphone and mobile devices for fundamental frequency, jitter, RAP, PPQ5, and DDP during all tasks and a bias for HNR, shimmer and its variants (APQ3, APQ5, APQ11, and DDA). Significant differences were observed between devices for HNR, shimmer, and its variants for all tasks. High correlation was observed between devices for all features, except SD F0 for RP. Similar results were observed in the HD group for SV and SR task. Biological sex had a significant effect on F0 and HNR during all tests, and for jitter, RAP, PPQ5, DDP, and shimmer for RP and SR. No significant effect of age was observed. CONCLUSIONS Mobile devices provided good agreement with state of the art, high-quality microphones during structured speech tasks for features derived from frequency components of the audio recordings. Caution should be taken when estimating HNR, shimmer and its variants from recordings made with mobile devices.
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Affiliation(s)
- Vitória S Fahed
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland; Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
| | - Emer P Doheny
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland; Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Jennifer Hoblyn
- School of Medicine, Trinity College Dublin, Dublin, Ireland; Bloomfield Health Services, Dublin, Ireland
| | - Madeleine M Lowery
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland; Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
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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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Tang A, Woldemariam S, Roger J, Sirota M. Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms. Yearb Med Inform 2022; 31:106-115. [PMID: 36463867 PMCID: PMC9719766 DOI: 10.1055/s-0042-1742513] [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] [Indexed: 12/07/2022] Open
Abstract
OBJECTIVES Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion. METHODS We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years. RESULTS We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine. CONCLUSION Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all.
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Affiliation(s)
- Alice Tang
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Graduate Program in Bioengineering, UCSF, San Francisco, CA, USA
- School of Medicine, UCSF, San Francisco, CA, USA
| | - Sarah Woldemariam
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- School of Medicine, UCSF, San Francisco, CA, USA
| | - Jacquelyn Roger
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Graduate Program in Biological and Medical Informatics, UCSF, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Department of Pediatrics, UCSF, San Francisco, CA, USA
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Ding Z, Lee TL, Chan AS. Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review. J Clin Med 2022; 11:jcm11144191. [PMID: 35887956 PMCID: PMC9320101 DOI: 10.3390/jcm11144191] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/10/2022] [Accepted: 07/18/2022] [Indexed: 01/28/2023] Open
Abstract
The dementia population is increasing as the world’s population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle–Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.
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Affiliation(s)
- Zihan Ding
- Neuropsychology Laboratory, Department of Psychology, The Chinese University of Hong Kong, Hong Kong, China; (Z.D.); (T.-l.L.)
| | - Tsz-lok Lee
- Neuropsychology Laboratory, Department of Psychology, The Chinese University of Hong Kong, Hong Kong, China; (Z.D.); (T.-l.L.)
| | - Agnes S. Chan
- Neuropsychology Laboratory, Department of Psychology, The Chinese University of Hong Kong, Hong Kong, China; (Z.D.); (T.-l.L.)
- Research Centre for Neuropsychological Well-Being, The Chinese University of Hong Kong, Hong Kong, China
- Correspondence: ; Tel.: +852-3943-6654
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Hecker P, Steckhan N, Eyben F, Schuller BW, Arnrich B. Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends. Front Digit Health 2022; 4:842301. [PMID: 35899034 PMCID: PMC9309252 DOI: 10.3389/fdgth.2022.842301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/25/2022] [Indexed: 11/25/2022] Open
Abstract
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
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Affiliation(s)
- Pascal Hecker
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- audEERING GmbH, Gilching, Germany
- *Correspondence: Pascal Hecker ; orcid.org/0000-0001-6604-1671
| | - Nico Steckhan
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | | | - Björn W. Schuller
- audEERING GmbH, Gilching, Germany
- EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| | - Bert Arnrich
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
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Sezgin E, D'Arcy S. Editorial: Voice Technology and Conversational Agents in Health Care Delivery. Front Public Health 2022; 10:887492. [PMID: 35712270 PMCID: PMC9196328 DOI: 10.3389/fpubh.2022.887492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Affiliation(s)
- Emre Sezgin
- NORC at the University of Chicago, Chicago, IL, United States.,Nationwide Children's Hospital, Columbus, OH, United States
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Sezgin E, Oiler B, Abbott B, Noritz G, Huang Y. "Hey Siri, Help Me Take Care of My Child": A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management. Front Public Health 2022; 10:849322. [PMID: 35309210 PMCID: PMC8927637 DOI: 10.3389/fpubh.2022.849322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background About 23% of households in the United States have at least one child who has special healthcare needs. As most care activities occur at home, there is often a disconnect and lack of communication between families, home care nurses, and healthcare providers. Digital health technologies may help bridge this gap. Objective We conducted a pre-post study with a voice-enabled medical note taking (diary) app (SpeakHealth) in a real world setting with caregivers (parents, family members) of children with special healthcare needs (CSHCN) to understand feasibility of voice interaction and automatic speech recognition (ASR) for medical note taking at home. Methods In total, 41 parents of CSHCN were recruited. Participants completed a pre-study survey collecting demographic details, technology and care management preferences. Out of 41, 24 participants completed the study, using the app for 2 weeks and completing an exit survey. The app facilitated caregiver note-taking using voice interaction and ASR. An exit survey was conducted to collect feedback on technology adoption and changes in technology preferences in care management. We assessed the feasibility of the app by descriptively analyzing survey responses and user data following the key focus areas of acceptability, demand, implementation and integration, adaptation and expansion. In addition, perceived effectiveness of the app was assessed by comparing perceived changes in mobile app preferences among participants. In addition, the voice data, notes, and transcriptions were descriptively analyzed for understanding the feasibility of the app. Results The majority of the recruited parents were 35–44 years old (22, 53.7%), part of a two-parent household (30, 73.2%), white (37, 90.2%), had more than one child (31, 75.6%), lived in Ohio (37, 90.2%), used mobile health apps, mobile note taking apps or calendar apps (28, 68.3%) and patient portal apps (22, 53.7%) to track symptoms and health events at home. Caregivers had experience with voice technology as well (32, 78%). Among those completed the post-study survey (in Likert Scale 1–5), ~80% of the caregivers agreed or strongly agreed that using the app would enhance their performance in completing tasks (perceived usefulness; mean = 3.4, SD = 0.8), the app is free of effort (perceived ease of use; mean = 3.2, SD = 0.9), and they would use the app in the future (behavioral intention; mean = 3.1, SD = 0.9). In total, 88 voice interactive patient notes were generated with the majority of the voice recordings being less than 20 s in length (66%). Most noted symptoms and conditions, medications, treatment and therapies, and patient behaviors. More than half of the caregivers reported that voice interaction with the app and using transcribed notes positively changed their preference of technology to use and methods for tracking symptoms and health events at home. Conclusions Our findings suggested that voice interaction and ASR use in mobile apps are feasible and effective in keeping track of symptoms and health events at home. Future work is suggested toward using integrated and intelligent systems with voice interactions with broader populations.
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Affiliation(s)
- Emre Sezgin
- Information Technology Research and Innovation, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Brannon Oiler
- Information Technology Research and Innovation, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Brandon Abbott
- Information Technology Research and Innovation, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Garey Noritz
- Department of Pediatrics, Nationwide Children's Hospital, Columbus, OH, United States
| | - Yungui Huang
- Information Technology Research and Innovation, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
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Kent RD, Kim Y, Chen LM. Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2022; 65:574-623. [PMID: 34958599 DOI: 10.1044/2021_jslhr-21-00396] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
PURPOSE The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis. METHOD Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate. RESULTS Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions. DISCUSSION A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.
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Affiliation(s)
- Ray D Kent
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison
| | - Yunjung Kim
- School of Communication Sciences & Disorders, Florida State University, Tallahassee
| | - Li-Mei Chen
- Department of Foreign Languages and Literature, National Cheng Kung University, Tainan, Taiwan
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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39
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Parziale A, Mascalzoni D. Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem. Front Psychiatry 2022; 13:873392. [PMID: 35757212 PMCID: PMC9225201 DOI: 10.3389/fpsyt.2022.873392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Psychiatric research traditionally relies on subjective observation, which is time-consuming and labor-intensive. The widespread use of digital devices, such as smartphones and wearables, enables the collection and use of vast amounts of user-generated data as "digital biomarkers." These tools may also support increased participation of psychiatric patients in research and, as a result, the production of research results that are meaningful to them. However, sharing mental health data and research results may expose patients to discrimination and stigma risks, thus discouraging participation. To earn and maintain participants' trust, the first essential requirement is to implement an appropriate data governance system with a clear and transparent allocation of data protection duties and responsibilities among the actors involved in the process. These include sponsors, investigators, operators of digital tools, as well as healthcare service providers and biobanks/databanks. While previous works have proposed practical solutions to this end, there is a lack of consideration of positive data protection law issues in the extant literature. To start filling this gap, this paper discusses the GDPR legal qualifications of controller, processor, and joint controllers in the complex ecosystem unfolded by the integration of digital biomarkers in psychiatric research, considering their implications and proposing some general practical recommendations.
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40
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A Comparison of Speech Features between Mild Cognitive Impairment and Healthy Aging Groups. Dement Neurocogn Disord 2021; 20:52-61. [PMID: 34795768 PMCID: PMC8585532 DOI: 10.12779/dnd.2021.20.4.52] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/02/2022] Open
Abstract
Background and Purpose Language dysfunction is a symptom common to patients with Alzheimer's disease (AD). Speech feature analysis may be a patient-friendly screening test for early-stage AD. We aimed to investigate the speech features of amnestic mild cognitive impairment (aMCI) compared to normal controls (NCs). Methods Spoken responses to test questions were recorded with a microphone placed 15 cm in front of each participant. Speech samples delivered in response to four spoken test prompts (free speech test, Mini-Mental State Examination [MMSE], picture description test, and sentence repetition test) were obtained from 98 patients with aMCI and 139 NCs. Each recording was transcribed, with speech features noted. The frequency of the ten speech features assessed was evaluated to compare speech abilities between the test groups. Results Among the ten speech features, the frequency of pauses (p=0.001) and mumbles (p=0.001) were significantly higher in patients with aMCI than in NCs. Moreover, MMSE score was found to negatively correlate with the frequency of pauses (r=−0.441, p<0.001) and mumbles (r=−0.341, p<0.001). Conclusions Frequent pauses and mumbles reflect cognitive decline in aMCI patients in episodic and semantic memory tests. Speech feature analysis may prove to be a speech-based biomarker for screening early-stage cognitive impairment.
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41
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König A, Mallick E, Tröger J, Linz N, Zeghari R, Manera V, Robert P. Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis. Eur Psychiatry 2021; 64:e64. [PMID: 34641989 PMCID: PMC8581700 DOI: 10.1192/j.eurpsy.2021.2236] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders. METHODS Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. RESULTS Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores. CONCLUSIONS Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.
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Affiliation(s)
- Alexandra König
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - Elisa Mallick
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - Johannes Tröger
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - Nicklas Linz
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - Radia Zeghari
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - Valeria Manera
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - Philippe Robert
- Stars Team, Sophia Antipolis, Institut National de Recherche en Informatique et en Automatique (INRIA), Valbonne, France.,Clinical Research, ki:elements, Saarbrücken, Germany.,CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
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Brederoo SG, Nadema FG, Goedhart FG, Voppel AE, De Boer JN, Wouts J, Koops S, Sommer IEC. Implementation of automatic speech analysis for early detection of psychiatric symptoms: What do patients want? J Psychiatr Res 2021; 142:299-301. [PMID: 34416548 DOI: 10.1016/j.jpsychires.2021.08.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/09/2021] [Accepted: 08/15/2021] [Indexed: 10/20/2022]
Abstract
Psychiatry is in dire need of a method to aid early detection of symptoms. Recent developments in automatic speech analysis prove promising in this regard, and open avenues for implementation of speech-based applications to detect psychiatric symptoms. The current survey was conducted to assess positions with regard to speech recordings among a group (n = 675) of individuals who experience psychiatric symptoms. Overall, respondents are open to the idea of speech recordings in light of their mental welfare. Importantly, concerns with regard to privacy were raised. Given that speech recordings are privacy sensitive, this requires special attention upon implementation of automatic speech analysis techniques. Furthermore, respondents indicated a preference for speech recordings in the presence of a clinician, as opposed to a recording made at home without the clinician present. In developing a speech marker for psychiatry, close collaboration with the intended users is essential to arrive at a truly valid and implementable method.
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Affiliation(s)
- S G Brederoo
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, the Netherlands; Center for Psychiatry, University Medical Center Groningen, Groningen, the Netherlands.
| | - F G Nadema
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, the Netherlands
| | - F G Goedhart
- MIND Landelijk Platform Psychische Gezondheid, Amersfoort, the Netherlands
| | - A E Voppel
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, the Netherlands
| | - J N De Boer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - J Wouts
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, the Netherlands
| | - S Koops
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, the Netherlands
| | - I E C Sommer
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, the Netherlands
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Hepatic Encephalopathy is Associated With Slow Speech on Objective Assessment. Am J Gastroenterol 2021; 116:1950-1953. [PMID: 34465696 DOI: 10.14309/ajg.0000000000001351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/14/2021] [Indexed: 12/11/2022]
Abstract
INTRODUCTION There are no available low-burden, point-of-care tests to diagnose, grade, and predict hepatic encephalopathy (HE). METHODS We evaluated speech as a biomarker of HE in 76 English-speaking adults with cirrhosis. RESULTS Three speech features significantly correlated with the following neuropsychiatric scores: speech rate, word duration, and use of particles. Patients with low neuropsychiatric scores had slower speech (22 words/min, P = 0.01), longer word duration (0.09 seconds/word, P = 0.01), and used fewer particles (0.85% fewer, P = 0.01). Patients with a history of overt HE had slower speech (23 words/min, P = 0.005) and longer word duration (0.09 seconds/word, P = 0.005). DISCUSSION HE is associated with slower speech.
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44
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More than a biomarker: could language be a biosocial marker of psychosis? NPJ SCHIZOPHRENIA 2021; 7:42. [PMID: 34465778 PMCID: PMC8408150 DOI: 10.1038/s41537-021-00172-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/06/2021] [Indexed: 02/07/2023]
Abstract
Automated extraction of quantitative linguistic features has the potential to predict objectively the onset and progression of psychosis. These linguistic variables are often considered to be biomarkers, with a large emphasis placed on the pathological aberrations in the biological processes that underwrite the faculty of language in psychosis. This perspective offers a reminder that human language is primarily a social device that is biologically implemented. As such, linguistic aberrations in patients with psychosis reflect both social and biological processes affecting an individual. Failure to consider the sociolinguistic aspects of NLP measures will limit their usefulness as digital tools in clinical settings. In the context of psychosis, considering language as a biosocial marker could lead to less biased and more accessible tools for patient-specific predictions in the clinic.
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Vijverberg EGB, Axelsen TM, Bihlet AR, Henriksen K, Weber F, Fuchs K, Harrison JE, Kühn-Wache K, Alexandersen P, Prins ND, Scheltens P. Rationale and study design of a randomized, placebo-controlled, double-blind phase 2b trial to evaluate efficacy, safety, and tolerability of an oral glutaminyl cyclase inhibitor varoglutamstat (PQ912) in study participants with MCI and mild AD-VIVIAD. Alzheimers Res Ther 2021; 13:142. [PMID: 34425883 PMCID: PMC8381483 DOI: 10.1186/s13195-021-00882-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 08/04/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND Varoglutamstat (formerly PQ912) is a small molecule that inhibits the activity of the glutaminyl cyclase to reduce the level of pyroglutamate-A-beta (pGluAB42). Recent studies confirm that pGluAB42 is a particular amyloid form that is highly synaptotoxic and plays a significant role in the development of AD. METHODS This paper describes the design and methodology behind the phase 2b VIVIAD-trial in AD. The aim of this study is to evaluate varoglutamstat in a state-of-the-art designed, placebo-controlled, double-blind, randomized clinical trial for safety and tolerability, efficacy on cognition, and effects on brain activity and AD biomarkers. In addition to its main purpose, the trial will explore potential associations between novel and established biomarkers and their individual and composite relation to disease characteristics. RESULTS To be expected early 2023 CONCLUSION: This state of the art phase 2b study will yield important results for the field with respect to trial methodology and for the treatment of AD with a small molecule directed against pyroglutamate-A-beta. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04498650.
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Affiliation(s)
- E. G. B. Vijverberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - T. M. Axelsen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Sanos Clinic A/S, Vejle, Denmark
| | | | | | - F. Weber
- Vivoryon Therapeutics NV, Halle, Germany
| | - K. Fuchs
- Vivoryon Therapeutics NV, Halle, Germany
| | - J. E. Harrison
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Metis Cognition Ltd, Park House, Kilmington Common, Wiltshire, UK
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | | | | | - N. D. Prins
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimercentrum Amsterdam, Amsterdam UMC, Locatie VUmc, De Boelelaan 1117/1118, 1091 HZ Amsterdam, The Netherlands
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Zhang L, Ngo A, Thomas JA, Burkhardt HA, Parsey CM, Au R, Ghomi RH. Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort. EXPLORATION OF MEDICINE 2021; 2:232-252. [PMID: 34746927 PMCID: PMC8570561 DOI: 10.37349/emed.2021.00044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/12/2021] [Indexed: 12/15/2022] Open
Abstract
AIM Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905-22) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features. METHODS Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants' Mini-Mental State Examination (MMSE) scores. RESULTS Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869]. CONCLUSIONS Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.
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Affiliation(s)
- Larry Zhang
- Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, Indiana 47408, United States
- Department of Informatics, Indiana University Bloomington, Bloomington, Indiana 47408, United States
| | - Anthony Ngo
- Department of Statistics, University of Washington, Seattle, Washington 98195-0005, United States
| | - Jason A. Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington Seattle Campus, Seattle, Washington 98195-0005, United States
| | - Hannah A. Burkhardt
- Department of Biomedical Informatics and Medical Education, University of Washington Seattle Campus, Seattle, Washington 98195-0005, United States
| | - Carolyn M. Parsey
- Department of Neurology, University of Washington, Seattle, Washington 98195-0005, United States
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Neurology, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts 02118, United States
| | - Reza Hosseini Ghomi
- Department of Neurology, University of Washington, Seattle, Washington 98195-0005, United States
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Manta C, Mahadevan N, Bakker J, Ozen Irmak S, Izmailova E, Park S, Poon JL, Shevade S, Valentine S, Vandendriessche B, Webster C, Goldsack JC. EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration. Digit Biomark 2021; 5:127-147. [PMID: 34179682 DOI: 10.1159/000515835] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/10/2021] [Indexed: 12/21/2022] Open
Abstract
The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function. EVIDENCE is applicable to 5 types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurements products.
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Affiliation(s)
- Christine Manta
- Digital Medicine Society, Boston, Massachusetts, USA.,Elektra Labs, Boston, Massachusetts, USA
| | - Nikhil Mahadevan
- Digital Medicine Society, Boston, Massachusetts, USA.,Pfizer Inc., Cambridge, Massachusetts, USA
| | - Jessie Bakker
- Digital Medicine Society, Boston, Massachusetts, USA.,Philips, Monroeville, Pennsylvania, USA
| | | | - Elena Izmailova
- Digital Medicine Society, Boston, Massachusetts, USA.,Koneksa Health Inc., New York, New York, USA
| | - Siyeon Park
- Geisinger Health System, Danville, Pennsylvania, USA
| | | | | | | | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium.,Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Fagherazzi G, Fischer A, Ismael M, Despotovic V. Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice. Digit Biomark 2021; 5:78-88. [PMID: 34056518 PMCID: PMC8138221 DOI: 10.1159/000515346] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 02/18/2021] [Indexed: 12/17/2022] Open
Abstract
Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.
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Affiliation(s)
- Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Aurélie Fischer
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Muhannad Ismael
- IT for Innovation in Services Department (ITIS), Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | - Vladimir Despotovic
- Department of Computer Science, Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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