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Pourramezan Fard A, Mahoor MH, Alsuhaibani M, Dodge HH. Linguistic-based Mild Cognitive Impairment detection using Informative Loss. Comput Biol Med 2024; 176:108606. [PMID: 38763068 DOI: 10.1016/j.compbiomed.2024.108606] [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: 01/15/2024] [Revised: 04/17/2024] [Accepted: 05/11/2024] [Indexed: 05/21/2024]
Abstract
This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.
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Affiliation(s)
- Ali Pourramezan Fard
- Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO 80208, USA.
| | - Mohammad H Mahoor
- Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO 80208, USA; DreamFace Technologies LLC, Centennial, CO 8011, USA.
| | - Muath Alsuhaibani
- Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO 80208, USA; Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
| | - Hiroko H Dodge
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
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Cho S, Olm CA, Ash S, Shellikeri S, Agmon G, Cousins KAQ, Irwin DJ, Grossman M, Liberman M, Nevler N. Automatic classification of AD pathology in FTD phenotypes using natural speech. Alzheimers Dement 2024; 20:3416-3428. [PMID: 38572850 PMCID: PMC11095488 DOI: 10.1002/alz.13748] [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: 11/27/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 04/05/2024]
Abstract
INTRODUCTION Screening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech-based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD). METHODS We trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC = 36, FTLD = 60, healthy controls [HC] = 89). Patients' pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network-based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features. RESULTS Our classifier showed 0.88 ± $ \pm $ 0.03 area under the curve (AUC) for ADNC versus FTLD and 0.93 ± $ \pm $ 0.04 AUC for patients versus HC. Noun frequency and pause rate correlated with gray matter volume loss in the limbic and salience networks, respectively. DISCUSSION Brief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD. HIGHLIGHTS We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech-based neuropathology screening tool.
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Affiliation(s)
- Sunghye Cho
- Linguistic Data ConsortiumDepartment of LinguisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sharon Ash
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sanjana Shellikeri
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Galit Agmon
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Katheryn A. Q. Cousins
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Murray Grossman
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Mark Liberman
- Linguistic Data ConsortiumDepartment of LinguisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Naomi Nevler
- Penn Frontotemporal Degeneration CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Cordella C, Di Filippo L, Kolachalama VB, Kiran S. Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2024:1-38. [PMID: 38652820 DOI: 10.1044/2024_ajslp-23-00208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
PURPOSE Speech fluency has important diagnostic implications for individuals with poststroke aphasia (PSA) as well as primary progressive aphasia (PPA), and quantitative assessment of connected speech has emerged as a widely used approach across both etiologies. The purpose of this review was to provide a clearer picture on the range, nature, and utility of individual quantitative speech/language measures and methods used to assess connected speech fluency in PSA and PPA, and to compare approaches across etiologies. METHOD We conducted a scoping review of literature published between 2012 and 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Forty-five studies were included in the review. Literature was charted and summarized by etiology and characteristics of included patient populations and method(s) used for derivation and analysis of speech/language features. For a subset of included articles, we also charted the individual quantitative speech/language features reported and the level of significance of reported results. RESULTS Results showed that similar methodological approaches have been used to quantify connected speech fluency in both PSA and PPA. Two hundred nine individual speech-language features were analyzed in total, with low levels of convergence across etiology on specific features but greater agreement on the most salient features. The most useful features for differentiating fluent from nonfluent aphasia in both PSA and PPA were features related to overall speech quantity, speech rate, or grammatical competence. CONCLUSIONS Data from this review demonstrate the feasibility and utility of quantitative approaches to index connected speech fluency in PSA and PPA. We identified emergent trends toward automated analysis methods and data-driven approaches, which offer promising avenues for clinical translation of quantitative approaches. There is a further need for improved consensus on which subset of individual features might be most clinically useful for assessment and monitoring of fluency. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.25537237.
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Affiliation(s)
- Claire Cordella
- Department of Speech, Language and Hearing Sciences, Boston University, MA
| | - Lauren Di Filippo
- Department of Speech, Language and Hearing Sciences, Boston University, MA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, MA
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, MA
| | - Swathi Kiran
- Department of Speech, Language and Hearing Sciences, Boston University, MA
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B.T B, Chen JM. Performance Assessment of ChatGPT versus Bard in Detecting Alzheimer's Dementia. Diagnostics (Basel) 2024; 14:817. [PMID: 38667463 PMCID: PMC11048951 DOI: 10.3390/diagnostics14080817] [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: 03/06/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Large language models (LLMs) find increasing applications in many fields. Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) are assessed in their current form, as publicly available, for their ability to recognize Alzheimer's dementia (AD) and Cognitively Normal (CN) individuals using textual input derived from spontaneous speech recordings. A zero-shot learning approach is used at two levels of independent queries, with the second query (chain-of-thought prompting) eliciting more detailed information than the first. Each LLM chatbot's performance is evaluated on the prediction generated in terms of accuracy, sensitivity, specificity, precision, and F1 score. LLM chatbots generated a three-class outcome ("AD", "CN", or "Unsure"). When positively identifying AD, Bard produced the highest true-positives (89% recall) and highest F1 score (71%), but tended to misidentify CN as AD, with high confidence (low "Unsure" rates); for positively identifying CN, GPT-4 resulted in the highest true-negatives at 56% and highest F1 score (62%), adopting a diplomatic stance (moderate "Unsure" rates). Overall, the three LLM chatbots can identify AD vs. CN, surpassing chance-levels, but do not currently satisfy the requirements for clinical application.
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Affiliation(s)
- Balamurali B.T
- Science, Mathematics & Technology (SMT), Singapore University of Technology & Design, 8 Somapah Rd, Singapore 487372, Singapore
| | - Jer-Ming Chen
- Science, Mathematics & Technology (SMT), Singapore University of Technology & Design, 8 Somapah Rd, Singapore 487372, Singapore
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Kaser AN, Lacritz LH, Winiarski HR, Gabirondo P, Schaffert J, Coca AJ, Jiménez-Raboso J, Rojo T, Zaldua C, Honorato I, Gallego D, Nieves ER, Rosenstein LD, Cullum CM. A novel speech analysis algorithm to detect cognitive impairment in a Spanish population. Front Neurol 2024; 15:1342907. [PMID: 38638311 PMCID: PMC11024431 DOI: 10.3389/fneur.2024.1342907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Objective Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Method Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Results Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Conclusion Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.
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Affiliation(s)
- Alyssa N. Kaser
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Laura H. Lacritz
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Holly R. Winiarski
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Jeff Schaffert
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Alberto J. Coca
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
- Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, United Kingdom
| | | | - Tomas Rojo
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | - Carla Zaldua
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | | | | | - Emmanuel Rosario Nieves
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - Leslie D. Rosenstein
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - C. Munro Cullum
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurological Surgery, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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Zhao X, Wen H, Xu G, Pang T, Zhang Y, He X, Hu R, Yan M, Chen C, Wu X, Xu X. Validity, feasibility, and effectiveness of a voice-recognition based digital cognitive screener for dementia and mild cognitive impairment in community-dwelling older Chinese adults: A large-scale implementation study. Alzheimers Dement 2024; 20:2384-2396. [PMID: 38299756 PMCID: PMC11032546 DOI: 10.1002/alz.13668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/03/2023] [Accepted: 12/03/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION We investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants. METHODS Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening. HIGHLIGHTS To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.
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Affiliation(s)
- Xuhao Zhao
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Haoxuan Wen
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Guohai Xu
- DAMO Academy, Alibaba GroupHangzhouZhejiangP. R. China
| | - Ting Pang
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Yaping Zhang
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Xindi He
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Ruofei Hu
- DAMO Academy, Alibaba GroupHangzhouZhejiangP. R. China
| | - Ming Yan
- DAMO Academy, Alibaba GroupHangzhouZhejiangP. R. China
| | - Christopher Chen
- Department of PharmacologyYong Loo Lin School of MedicineMemory, Ageing, and Cognition Centre (MACC)National University of SingaporeSingaporeSingapore
| | - Xifeng Wu
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
| | - Xin Xu
- School of Public Health, The Second Affiliated Hospital of School of MedicineZhejiang UniversityHangzhouZhejiangP. R. China
- Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceHangzhouZhejiangP. R. China
- Department of PharmacologyYong Loo Lin School of MedicineMemory, Ageing, and Cognition Centre (MACC)National University of SingaporeSingaporeSingapore
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7
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García-Gutiérrez F, Alegret M, Marquié M, Muñoz N, Ortega G, Cano A, De Rojas I, García-González P, Olivé C, Puerta R, García-Sanchez A, Capdevila-Bayo M, Montrreal L, Pytel V, Rosende-Roca M, Zaldua C, Gabirondo P, Tárraga L, Ruiz A, Boada M, Valero S. Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum. Alzheimers Res Ther 2024; 16:26. [PMID: 38308366 PMCID: PMC10835990 DOI: 10.1186/s13195-024-01394-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: 11/07/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum. METHODS Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information. RESULTS The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability. CONCLUSIONS In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.
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Affiliation(s)
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nathalia Muñoz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Gemma Ortega
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Itziar De Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Clàudia Olivé
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Raquel Puerta
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ainhoa García-Sanchez
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - María Capdevila-Bayo
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Maitee Rosende-Roca
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | | | | | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain.
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain.
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Zhang C, Lei X, Ma W, Long J, Long S, Chen X, Luo J, Tao Q. Diagnosis Framework for Probable Alzheimer's Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features. J Alzheimers Dis 2024; 97:1125-1137. [PMID: 38189751 DOI: 10.3233/jad-230703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
BACKGROUND Emotion and cognition are intercorrelated. Impaired emotion is common in populations with Alzheimer's disease (AD) and mild cognitive impairment (MCI), showing promises as an early detection approach. OBJECTIVE We aim to develop a novel automatic classification tool based on emotion features and machine learning. METHODS Older adults aged 60 years or over were recruited among residents in the long-term care facilities and the community. Participants included healthy control participants with normal cognition (HC, n = 26), patients with MCI (n = 23), and patients with probable AD (n = 30). Participants watched emotional film clips while multi-dimensional emotion data were collected, including mental features of Self-Assessment Manikin (SAM), physiological features of electrodermal activity (EDA), and facial expressions. Emotional features of EDA and facial expression were abstracted by using continuous decomposition analysis and EomNet, respectively. Bidirectional long short-term memory (Bi-LSTM) was used to train classification model. Hybrid fusion was used, including early feature fusion and late decision fusion. Data from 79 participants were utilized into deep machine learning analysis and hybrid fusion method. RESULTS By combining multiple emotion features, the model's performance of AUC value was highest in classification between HC and probable AD (AUC = 0.92), intermediate between MCI and probable AD (AUC = 0.88), and lowest between HC and MCI (AUC = 0.82). CONCLUSIONS Our method demonstrated an excellent predictive power to differentiate HC/MCI/AD by fusion of multiple emotion features. The proposed model provides a cost-effective and automated method that can assist in detecting probable AD and MCI from normal aging.
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Affiliation(s)
- Chunchao Zhang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- Division of Medical Psychology and Behaviour Science, School of Medicine, Jinan University, Guangzhou, China
| | - Xiaolin Lei
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Wenhao Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- Division of Medical Psychology and Behaviour Science, School of Medicine, Jinan University, Guangzhou, China
| | - Jinyi Long
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Shun Long
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Xiang Chen
- Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jun Luo
- Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qian Tao
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- Division of Medical Psychology and Behaviour Science, School of Medicine, Jinan University, Guangzhou, China
- Neuroscience and Neurorehabilitation Institute, University of Health and Rehabilitation Science, Qingdao, China
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Romano MF, Shih LC, Paschalidis IC, Au R, Kolachalama VB. Large Language Models in Neurology Research and Future Practice. Neurology 2023; 101:1058-1067. [PMID: 37816646 PMCID: PMC10752640 DOI: 10.1212/wnl.0000000000207967] [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: 06/01/2023] [Accepted: 09/06/2023] [Indexed: 10/12/2023] Open
Abstract
Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.
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Affiliation(s)
- Michael F Romano
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Ludy C Shih
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Ioannis C Paschalidis
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Rhoda Au
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Vijaya B Kolachalama
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA.
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10
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Matt JE, Rizzo DM, Javed A, Eppstein MJ, Manukyan V, Gramling C, Dewoolkar AM, Gramling R. An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences. J Palliat Med 2023; 26:1627-1633. [PMID: 37440175 DOI: 10.1089/jpm.2023.0087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023] Open
Abstract
Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.
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Affiliation(s)
- Jeremy E Matt
- Graduate Program in Complex Systems and Data Science, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, Vermont, USA
| | - Donna M Rizzo
- Department of Civil and Environmental Engineering, University of Vermont, Burlington, Vermont, USA
| | - Ali Javed
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford University, Stanford, California, USA
| | - Margaret J Eppstein
- Department of Computer Science, University of Vermont, Burlington, Vermont, USA
| | | | - Cailin Gramling
- Graduate Program in Complex Systems and Data Science, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, Vermont, USA
| | - Advik Mandar Dewoolkar
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, Vermont, USA
| | - Robert Gramling
- Department of Family Medicine, University of Vermont, Burlington, Vermont, USA
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11
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Engineer M, Kot S, Dixon E. Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis. JMIR Form Res 2023; 7:e48143. [PMID: 37878351 PMCID: PMC10632922 DOI: 10.2196/48143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Past research in the Western context found that people with dementia search for digital dementia information in peer-reviewed medical research articles, dementia advocacy and medical organizations, and blogs written by other people with dementia. This past work also demonstrated that people with dementia do not perceive English digital dementia information as emotionally or cognitively accessible. OBJECTIVE In this study, we sought to investigate the readability; linguistic, psychological, and emotional characteristics; and target audiences of digital dementia information. We conducted a textual analysis of 3 different types of text-based digital dementia information written in English: 300 medical articles, 35 websites, and 50 blogs. METHODS We assessed the text's readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level measurements, as well as tone, analytical thinking, clout, authenticity, and word frequencies using a natural language processing tool, Linguistic Inquiry and Word Count Generator. We also conducted a thematic analysis to categorize the target audiences for each information source and used these categorizations for further statistical analysis. RESULTS The median Flesch-Kincaid Grade Level readability score and Flesch Reading Ease score for all types of information (N=1139) were 12.1 and 38.6, respectively, revealing that the readability scores of all 3 information types were higher than the minimum requirement. We found that medical articles had significantly (P=.05) higher word count and analytical thinking scores as well as significantly lower clout, authenticity, and emotional tone scores than websites and blogs. Further, blogs had significantly (P=.48) higher word count and authenticity scores but lower analytical scores than websites. Using thematic analysis, we found that most of the blogs (156/227, 68.7%) and web pages (399/612, 65.2%) were targeted at people with dementia. Website information targeted at a general audience had significantly lower readability scores. In addition, website information targeted at people with dementia had higher word count and lower emotional tone ratings. The information on websites targeted at caregivers had significantly higher clout and lower authenticity scores. CONCLUSIONS Our findings indicate that there is an abundance of digital dementia information written in English that is targeted at people with dementia, but this information is not readable by a general audience. This is problematic considering that people with <12 years of education are at a higher risk of developing dementia. Further, our findings demonstrate that digital dementia information written in English has a negative tone, which may be a contributing factor to the mental health crisis many people with dementia face after receiving a diagnosis. Therefore, we call for content creators to lower readability scores to make the information more accessible to a general audience and to focus their efforts on providing information in a way that does not perpetuate overly negative narratives of dementia.
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Affiliation(s)
- Margi Engineer
- Computer Science Department, Clemson University, Clemson, SC, United States
| | - Sushant Kot
- Computer Science Department, Clemson University, Clemson, SC, United States
| | - Emma Dixon
- Human Centered Computing Department, Clemson University, Clemson, SC, United States
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12
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Parsapoor M. AI-based assessments of speech and language impairments in dementia. Alzheimers Dement 2023; 19:4675-4687. [PMID: 37578167 DOI: 10.1002/alz.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 08/15/2023]
Abstract
Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.
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Affiliation(s)
- Mahboobeh Parsapoor
- Centre de Recherche Informatique de Montréal: CRIM, Montreal, Quebec, Canada
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13
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Xiang C, Ai W, Zhang Y. Language dysfunction correlates with cognitive impairments in older adults without dementia mediated by amyloid pathology. Front Neurol 2023; 14:1051382. [PMID: 37265466 PMCID: PMC10230042 DOI: 10.3389/fneur.2023.1051382] [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: 09/22/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Background Previous studies have explored the application of non-invasive biomarkers of language dysfunction for the early detection of Alzheimer's disease (AD). However, language dysfunction over time may be quite heterogeneous within different diagnostic groups. Method Patient demographics and clinical data were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for the participants without dementia who had measures of cerebrospinal fluid (CSF) biomarkers and language dysfunction. We analyzed the effect of longitudinal neuropathological and clinical correlates in the pathological process of semantic fluency and confrontation naming. The mediation effects of AD biomarkers were also explored by the mediation analysis. Result There were 272 subjects without dementia included in this analysis. Higher rates of decline in semantic fluency and confrontation naming were associated with a higher risk of progression to MCI or AD, and a greater decline in cognitive abilities. Moreover, the rate of change in semantic fluency was significantly associated with Aβ deposition, while confrontation naming was significantly associated with both amyloidosis and tau burden. Mediation analyses revealed that both confrontation naming and semantic fluency were partially mediated by the Aβ aggregation. Conclusion In conclusion, the changes in language dysfunction may partly stem from the Aβ deposition, while confrontation naming can also partly originate from the increase in tau burden. Therefore, this study sheds light on how language dysfunction is partly constitutive of mild cognitive impairment and dementia and therefore is an important clinical predictor.
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Affiliation(s)
- Chunchen Xiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Weiping Ai
- Department of Neurology, Zhangjiakou First Hospital, Zhangjiakou, China
| | - Yumei Zhang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
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14
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Karjadi C, Xue C, Cordella C, Kiran S, Paschalidis IC, Au R, Kolachalama VB. Fusion of Low-Level Descriptors of Digital Voice Recordings for Dementia Assessment. J Alzheimers Dis 2023; 96:507-514. [PMID: 37840494 PMCID: PMC10657667 DOI: 10.3233/jad-230560] [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] [Accepted: 08/29/2023] [Indexed: 10/17/2023]
Abstract
Digital voice recordings can offer affordable, accessible ways to evaluate behavior and function. We assessed how combining different low-level voice descriptors can evaluate cognitive status. Using voice recordings from neuropsychological exams at the Framingham Heart Study, we developed a machine learning framework fusing spectral, prosodic, and sound quality measures early in the training cycle. The model's area under the receiver operating characteristic curve was 0.832 (±0.034) in differentiating persons with dementia from those who had normal cognition. This offers a data-driven framework for analyzing minimally processed voice recordings for cognitive assessment, highlighting the value of digital technologies in disease detection and intervention.
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Affiliation(s)
- Cody Karjadi
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Departments of Anatomy & Neurobiology and Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Chonghua Xue
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | | | - Swathi Kiran
- Sargent College, Boston University, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
| | - Ioannis Ch. Paschalidis
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
- Departments of Electrical & Computer Engineering, Systems Engineering and Biomedical Engineering, Boston University, Boston, MA, USA
| | - Rhoda Au
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Departments of Anatomy & Neurobiology and Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Alzheimer’s Disease Research Center, Boston University, Boston, MA, USA
| | - Vijaya B. Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA
- Alzheimer’s Disease Research Center, Boston University, Boston, MA, USA
- Department of Computer Science, Boston University, Boston, MA, USA
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15
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Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer's Disease Using Voice. Brain Sci 2022; 13:brainsci13010028. [PMID: 36672010 PMCID: PMC9856143 DOI: 10.3390/brainsci13010028] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
There is currently no simple, widely available screening method for Alzheimer's disease (AD), partly because the diagnosis of AD is complex and typically involves expensive and sometimes invasive tests not commonly available outside highly specialized clinical settings. Here, we developed an artificial intelligence (AI)-powered end-to-end system to detect AD and predict its severity directly from voice recordings. At the core of our system is the pre-trained data2vec model, the first high-performance self-supervised algorithm that works for speech, vision, and text. Our model was internally evaluated on the ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech only) dataset containing voice recordings of subjects describing the Cookie Theft picture, and externally validated on a test dataset from DementiaBank. The AI model can detect AD with average area under the curve (AUC) of 0.846 and 0.835 on held-out and external test set, respectively. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.9616). Moreover, the model can reliably predict the subject's cognitive testing score solely based on raw voice recordings. Our study demonstrates the feasibility of using the AI-powered end-to-end model for early AD diagnosis and severity prediction directly based on voice, showing its potential for screening Alzheimer's disease in a community setting.
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