المؤلفون: Johannes Tröger, Ebru Baykara, Jian Zhao, Daphne ter Huurne, Nina Possemis, Elisa Mallick, Simona Schäfer, Louisa Schwed, Mario Mina, Nicklas Linz, Inez Ramakers, Craig Ritchie

المصدر: Digital Biomarkers, Vol 6, Iss 3, Pp 107-116 (2022)

مصطلحات موضوعية: mild cognitive impairment, digital biomarker, speech biomarker, dementia, speech analysis, clinical trials, Biology (General), QH301-705.5

الوصف: 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.

وصف الملف: electronic resource

العلاقة: https://www.karger.com/Article/FullText/526471Test; https://doaj.org/toc/2504-110XTest

الوصول الحر: https://doaj.org/article/d715274c09c14aa5807f9200f2c82a8fTest

المؤلفون: Yudong Xiao, Tao Wang, Wei Deng, Le Yang, Bin Zeng, Xiaomei Lao, Sien Zhang, Xiangqi Liu, Daiqiao Ouyang, Guiqing Liao, Yujie Liang

المصدر: Cancer Medicine, Vol 10, Iss 11, Pp 3822-3835 (2021)

مصطلحات موضوعية: acoustic analysis, diagnostic tests, quality of life, speech biomarker, support vector machine, tongue cancer, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

الوصف: Abstract The promise of speech disorders as biomarkers in clinical examination has been identified in a broad spectrum of neurodegenerative diseases. However, to the best of our knowledge, a validated acoustic marker with established discriminative and evaluative properties has not yet been developed for oral tongue cancers. Here we cross‐sectionally collected a screening dataset that included acoustic parameters extracted from 3 sustained vowels /ɑ/, /i/, /u/ and binary perceptual outcomes from 12 consonant‐vowel syllables. We used a support vector machine with linear kernel function within this dataset to identify the formant centralization ratio (FCR) as a dominant predictor of different perceptual outcomes across gender and syllable. The Acoustic analysis, Perceptual evaluation and Quality of Life assessment (APeQoL) was used to validate the FCR in 33 patients with primary resectable oral tongue cancers. Measurements were taken before (pre‐op) and four to six weeks after (post‐op) surgery. The speech handicap index (SHI), a speech‐specific questionnaire, was also administrated at these time points. Pre‐op correlation analysis within the APeQoL revealed overall consistency and a strong correlation between FCR and SHI scores. FCRs also increased significantly with increasing T classification pre‐operatively, especially for women. Longitudinally, the main effects of T classification, the extent of resection, and their interaction effects with time (pre‐op vs. post‐op) on FCRs were all significant. For pre‐operative FCR, after merging the two datasets, a cut‐off value of 0.970 produced an AUC of 0.861 (95% confidence interval: 0.785–0.938) for T3‐4 patients. In sum, this study determined that FCR is an acoustic marker with the potential to detect disease and related speech function in oral tongue cancers. These are preliminary findings that need to be replicated in longitudinal studies and/or larger cohorts.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2045-7634Test

الوصول الحر: https://doaj.org/article/3d0b6f1b154846cd81fa67fcb30ba7f0Test

المؤلفون: König, Alexandra, Linz, N., Baykara, E., Tröger, J., Ritchie, C., Saunders, S., Teipel, S., Köhler, S., Sánchez-Benavides, G., Grau-Rivera, O., Gispert, J. D., Palmqvist, S., Tideman, P., Hansson, O.

المصدر: Journal of Prevention of Alzheimer's Disease MultiPark: Multidisciplinary research focused on Parkinson´s disease. 10:314-321

مصطلحات موضوعية: Alzheimer’s disease, cognitive assessment, Dementia, machine learning, phone-based, screening, speech biomarker, Medicin och hälsovetenskap, Medicinska och farmaceutiska grundvetenskaper, Neurovetenskaper, Medical and Health Sciences, Basic Medicine, Neurosciences

الوصف: Background: Speech impairments are an early feature of Alzheimer’s disease (AD) and consequently, analysing speech performance is a promising new digital biomarker for AD screening. Future clinical AD trials on disease modifying drugs will require a shift to very early identification of individuals at risk of dementia. Hence, digital markers of language and speech may offer a method for screening of at-risk populations that are at the earliest stages of AD, eventually in combination with advanced machine learning. To this end, we developed a screening battery consisting of speech-based neurocognitive tests. The automated test performs a remote primary screening using a simple telephone. Objectives: PROSPECT-AD aims to validate speech biomarkers for identification of individuals with early signs of AD and monitor their longitudinal course through access to well-phenotyped cohorts. Design: PROSPECT-AD leverages ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), and BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by adding a collection of speech data over the telephone to existing longitudinal follow-ups. Participants at risk of dementia are recruited from existing parent cohorts across Europe to form an AD ‘probability-spectrum’, i.e., individuals with a low risk to high risk of developing AD dementia. The characterization of cognition, biomarker and risk factor (genetic and environmental) status of each research participants over time combined with audio recordings of speech samples will provide a well-phenotyped population for comparing novel speech markers with current gold standard biomarkers and cognitive scores. Participants: N= 1000 participants aged 50 or older will be included in total, with a clinical dementia rating scale (CDR) score of 0 or 0.5. The study protocol is planned to run according to sites between 12 and 18 months. Measurements: The speech protocol includes the following neurocognitive tests which will be administered remotely: Word List [Memory Function], Verbal Fluency [Executive Functions] and spontaneous free speech [Psychological and/ or behavioral symptoms]. Speech features on the linguistic and paralinguistic level will be extracted from the recordings and compared to data from CSF and blood biomarkers, neuroimaging, neuropsychological evaluations, genetic profiles, and family history. Primary candidate marker from speech will be a combination of most significant features in comparison to biomarkers as reference measure. Machine learning and computational techniques will be employed to identify the most significant speech biomarkers that could represent an early indicator of AD pathology. Furthermore, based on the analysis of speech performances, models will be trained to predict cognitive decline and disease progression across the AD continuum. Conclusion: The outcome of PROSPECT-AD may support AD drug development research as well as primary or tertiary prevention of dementia by providing a validated tool using a remote approach for identifying individuals at risk of dementia and monitoring individuals over time, either in a screening context or in clinical trials.

الوصول الحر: https://lup.lub.lu.se/record/a1083540-e995-4082-bf36-514655af7064Test
http://dx.doi.org/10.14283/jpad.2023.11Test

المؤلفون: Tröger, Johannes, Baykara, Ebru, Zhao, Jian, ter Huurne, Daphne, Possemis, Nina, Mallick, Elisa, Schäfer, Simona, Schwed, Louisa, Mina, Mario, Linz, Nicklas, Ramakers, Inez, Ritchie, Craig

المصدر: http://lobid.org/resources/99370675877106441Test#!, 6(3):107-116.

مصطلحات موضوعية: Dementia, Clinical trials, Speech biomarker, Speech analysis, Digital biomarker, Mild cognitive impairment, Validation – Research Article

الوصف: 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 (!##!Results!#!Verification!##!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.

العلاقة: https://repository.publisso.de/resource/frl:6440971Test; https://doi.org/10.1159/000526471Test; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710455Test/

الإتاحة: https://doi.org/10.1159/000526471Test
https://repository.publisso.de/resource/frl:6440971Test
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710455Test/

المؤلفون: A. König, N. Linz, E. Baykara, J. Tröger, C. Ritchie, S. Saunders, S. Teipel, S. Köhler, G. Sánchez-Benavides, O. Grau-Rivera, J.D. Gispert, S. Palmqvist, P. Tideman, O. Hansson

المصدر: The journal of prevention of Alzheimer's disease 10(2), 314-321 (2023). doi:10.14283/jpad.2023.11

مصطلحات موضوعية: psychology [Alzheimer Disease], screening, speech biomarker, General Medicine, cognitive assessment, phone-based, machine learning, Memory, Humans, Speech, Dementia, ddc:610, psychology [Cognitive Dysfunction], Alzheimer’s disease, Biomarkers

الوصف: Speech impairments are an early feature of Alzheimer's disease (AD) and consequently, analysing speech performance is a promising new digital biomarker for AD screening. Future clinical AD trials on disease modifying drugs will require a shift to very early identification of individuals at risk of dementia. Hence, digital markers of language and speech may offer a method for screening of at-risk populations that are at the earliest stages of AD, eventually in combination with advanced machine learning. To this end, we developed a screening battery consisting of speech-based neurocognitive tests. The automated test performs a remote primary screening using a simple telephone.PROSPECT-AD aims to validate speech biomarkers for identification of individuals with early signs of AD and monitor their longitudinal course through access to well-phenotyped cohorts.PROSPECT-AD leverages ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), and BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by adding a collection of speech data over the telephone to existing longitudinal follow-ups. Participants at risk of dementia are recruited from existing parent cohorts across Europe to form an AD 'probability-spectrum', i.e., individuals with a low risk to high risk of developing AD dementia. The characterization of cognition, biomarker and risk factor (genetic and environmental) status of each research participants over time combined with audio recordings of speech samples will provide a well-phenotyped population for comparing novel speech markers with current gold standard biomarkers and cognitive scores.N= 1000 participants aged 50 or older will be included in total, with a clinical dementia rating scale (CDR) score of 0 or 0.5. The study protocol is planned to run according to sites between 12 and 18 months.The speech protocol includes the following neurocognitive tests which will be administered remotely: Word List [Memory Function], Verbal Fluency [Executive Functions] and spontaneous free speech [Psychological and/ or behavioral symptoms]. Speech features on the linguistic and paralinguistic level will be extracted from the recordings and compared to data from CSF and blood biomarkers, neuroimaging, neuropsychological evaluations, genetic profiles, and family history. Primary candidate marker from speech will be a combination of most significant features in comparison to biomarkers as reference measure. Machine learning and computational techniques will be employed to identify the most significant speech biomarkers that could represent an early indicator of AD pathology. Furthermore, based on the analysis of speech performances, models will be trained to predict cognitive decline and disease progression across the AD continuum.The outcome of PROSPECT-AD may support AD drug development research as well as primary or tertiary prevention of dementia by providing a validated tool using a remote approach for identifying individuals at risk of dementia and monitoring individuals over time, either in a screening context or in clinical trials.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f585cdbfd57994e74a14e1db2d81939eTest
https://pub.dzne.de/record/257518Test

المؤلفون: Xiaomei Lao, Yudong Xiao, Bin Zeng, Le Yang, Wei Deng, Xiangqi Liu, Dai-qiao Ouyang, Tao Wang, Yujie Liang, Gui-qing Liao, Si-en Zhang

المصدر: Cancer Medicine
Cancer Medicine, Vol 10, Iss 11, Pp 3822-3835 (2021)

مصطلحات موضوعية: 0301 basic medicine, Adult, Male, Cancer Research, medicine.medical_specialty, China, Support Vector Machine, speech biomarker, Physical examination, Bioinfomatics, Audiology, Correlation, 03 medical and health sciences, diagnostic tests, 0302 clinical medicine, Sex Factors, Discriminative model, Quality of life, Speech Production Measurement, Tongue, Medicine, Data Mining, Humans, Radiology, Nuclear Medicine and imaging, Articulation Disorders, RC254-282, Aged, Original Research, Analysis of Variance, medicine.diagnostic_test, business.industry, tongue cancer, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Middle Aged, Confidence interval, Tongue Neoplasms, Support vector machine, 030104 developmental biology, Formant, Cross-Sectional Studies, acoustic analysis, Oncology, quality of life, 030220 oncology & carcinogenesis, Area Under Curve, Biomarker (medicine), Female, business

الوصف: The promise of speech disorders as biomarkers in clinical examination has been identified in a broad spectrum of neurodegenerative diseases. However, to the best of our knowledge, a validated acoustic marker with established discriminative and evaluative properties has not yet been developed for oral tongue cancers. Here we cross‐sectionally collected a screening dataset that included acoustic parameters extracted from 3 sustained vowels /ɑ/, /i/, /u/ and binary perceptual outcomes from 12 consonant‐vowel syllables. We used a support vector machine with linear kernel function within this dataset to identify the formant centralization ratio (FCR) as a dominant predictor of different perceptual outcomes across gender and syllable. The Acoustic analysis, Perceptual evaluation and Quality of Life assessment (APeQoL) was used to validate the FCR in 33 patients with primary resectable oral tongue cancers. Measurements were taken before (pre‐op) and four to six weeks after (post‐op) surgery. The speech handicap index (SHI), a speech‐specific questionnaire, was also administrated at these time points. Pre‐op correlation analysis within the APeQoL revealed overall consistency and a strong correlation between FCR and SHI scores. FCRs also increased significantly with increasing T classification pre‐operatively, especially for women. Longitudinally, the main effects of T classification, the extent of resection, and their interaction effects with time (pre‐op vs. post‐op) on FCRs were all significant. For pre‐operative FCR, after merging the two datasets, a cut‐off value of 0.970 produced an AUC of 0.861 (95% confidence interval: 0.785–0.938) for T3‐4 patients. In sum, this study determined that FCR is an acoustic marker with the potential to detect disease and related speech function in oral tongue cancers. These are preliminary findings that need to be replicated in longitudinal studies and/or larger cohorts.
Formant centralization Ratio was primarily approved a sensitive acoustic parameter to imply the general articulation function in patients with oral tongue cancer by support vector machine. Clinical validation in the context of preoperative analysis and longitudinal follow‐up further approved its discriminative and evaluative properties, which need to be reproduced in longitudinal studies with larger cohorts.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::635244b1c297df34aef5088dc72291d6Test
https://pubmed.ncbi.nlm.nih.gov/33938165Test

المؤلفون: Bipasha Kashyap, Pubudu Pathirana, M Horne, L Power, D Szmulewicz

مصطلحات موضوعية: Uncategorized, speech biomarker, linear predictive coding, spectrogram, vocal tract measures, acoustic analysis, cerebellar ataxia, Science & Technology, Technology, Engineering, Biomedical, Electrical & Electronic, lang

الوصف: © 2019 IEEE. Cerebellar Ataxia (CA) is a neurological condition that leads to uncoordinated muscle movements, even affecting the production of speech. Effective biomarkers are necessary to produce an objective decision-making support tool for early diagnosis of CA in non-clinical environments. This paper investigates the reliability and effectiveness of vocal tract acoustic biomarkers for assessing CA speech. These features were tested on a database consisting of 52 clinically rated tongue-twister phrase 'British Constitution' and its 4 consonant-vowel (CV) excerpts /ti/, /ti/', /tu/, /tion/ acquired from 30 ataxic patients and 22 healthy controls. Such a marker could be applied to objectively assess the severity of CA from a simple speaking test, contributing to the possibility of being translated into a computer based automatic module to screen the disease from the speech. All the vocal tract features explored in this study were statistically significant using Kolmogorov-Smirnov test at 5% level in distinguishing healthy and CA speech. Several machine learning classifiers with 5-fold cross-validations were implemented on the vocal features. It was observed that the intensity ratios corresponding to the 4 C-V excerpts in CA group showed an increased variability and produced the best classification accuracy of 84.6% using KNN classifier. Results motivate the use of vocal tract features for monitoring CA speech.

العلاقة: http://hdl.handle.net/10536/DRO/DU:30134064Test

الإتاحة: http://hdl.handle.net/10536/DRO/DU:30134064Test

المؤلفون: B Kashyap, Pubudu Pathirana, M Horne, L Power, D Szmulewicz

مصطلحات موضوعية: speech biomarker, linear predictive coding, spectrogram, vocal tract measures, acoustic analysis, cerebellar ataxia, 4003 Biomedical engineering

الوصف: © 2019 IEEE. Cerebellar Ataxia (CA) is a neurological condition that leads to uncoordinated muscle movements, even affecting the production of speech. Effective biomarkers are necessary to produce an objective decision-making support tool for early diagnosis of CA in non-clinical environments. This paper investigates the reliability and effectiveness of vocal tract acoustic biomarkers for assessing CA speech. These features were tested on a database consisting of 52 clinically rated tongue-twister phrase 'British Constitution' and its 4 consonant-vowel (CV) excerpts /ti/, /ti/', /tu/, /tion/ acquired from 30 ataxic patients and 22 healthy controls. Such a marker could be applied to objectively assess the severity of CA from a simple speaking test, contributing to the possibility of being translated into a computer based automatic module to screen the disease from the speech. All the vocal tract features explored in this study were statistically significant using Kolmogorov-Smirnov test at 5% level in distinguishing healthy and CA speech. Several machine learning classifiers with 5-fold cross-validations were implemented on the vocal features. It was observed that the intensity ratios corresponding to the 4 C-V excerpts in CA group showed an increased variability and produced the best classification accuracy of 84.6% using KNN classifier. Results motivate the use of vocal tract features for monitoring CA speech.

العلاقة: http://hdl.handle.net/10536/DRO/DU:30134064Test; https://figshare.com/articles/conference_contribution/Automated_tongue-twister_phrase-based_screening_for_Cerebellar_Ataxia_using_Vocal_tract_Biomarkers_/20720686Test

الإتاحة: http://hdl.handle.net/10536/DRO/DU:30134064Test
https://figshare.com/articles/conference_contribution/Automated_tongue-twister_phrase-based_screening_for_Cerebellar_Ataxia_using_Vocal_tract_Biomarkers_/20720686Test