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Zaher F, Diallo M, Achim AM, Joober R, Roy MA, Demers MF, Subramanian P, Lavigne KM, Lepage M, Gonzalez D, Zeljkovic I, Davis K, Mackinley M, Sabesan P, Lal S, Voppel A, Palaniyappan L. Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities. Schizophr Res 2024; 266:205-215. [PMID: 38428118 DOI: 10.1016/j.schres.2024.02.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/18/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.
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Affiliation(s)
- Farida Zaher
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Mariama Diallo
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Amélie M Achim
- Département de Psychiatrie et Neurosciences, Université Laval, Québec City, QC, Canada; Vitam - Centre de Recherche en Santé Durable, Québec City, QC, Canada; Centre de Recherche CERVO, Québec City, QC, Canada
| | - Ridha Joober
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Marc-André Roy
- Département de Psychiatrie et Neurosciences, Université Laval, Québec City, QC, Canada; Centre de Recherche CERVO, Québec City, QC, Canada
| | - Marie-France Demers
- Centre de Recherche CERVO, Québec City, QC, Canada; Faculté de Pharmacie, Université Laval, Québec City, QC, Canada
| | - Priya Subramanian
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada
| | - Katie M Lavigne
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Martin Lepage
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Daniela Gonzalez
- Prevention and Early Intervention Program for Psychosis, London Health Sciences Center, Lawson Health Research Institute, London, ON, Canada
| | - Irnes Zeljkovic
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada
| | - Kristin Davis
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Michael Mackinley
- Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada; Prevention and Early Intervention Program for Psychosis, London Health Sciences Center, Lawson Health Research Institute, London, ON, Canada
| | - Priyadharshini Sabesan
- Lakeshore General Hospital and Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Shalini Lal
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada; School of Rehabilitation, Faculty of Medicine, University of Montréal, Montréal, QC, Canada
| | - Alban Voppel
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Psychiatry, Schulich School of Medicine, Western University, London, ON, Canada; Robarts Research Institute, Western University, London, ON, Canada.
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Cummins N, Dineley J, Conde P, Matcham F, Siddi S, Lamers F, Carr E, Lavelle G, Leightley D, White KM, Oetzmann C, Campbell EL, Simblett S, Bruce S, Haro JM, Penninx BWJH, Ranjan Y, Rashid Z, Stewart C, Folarin AA, Bailón R, Schuller BW, Wykes T, Vairavan S, Dobson RJB, Narayan VA, Hotopf M. Multilingual markers of depression in remotely collected speech samples: A preliminary analysis. J Affect Disord 2023; 341:128-136. [PMID: 37598722 DOI: 10.1016/j.jad.2023.08.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data. METHODS We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features. RESULTS Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses. LIMITATIONS Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features. CONCLUSIONS Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.
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Affiliation(s)
- Nicholas Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Judith Dineley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- School of Psychology, University of Sussex, Falmer, UK; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Barcelona, Spain
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | - Ewan Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Grace Lavelle
- School of Psychology, University of Sussex, Falmer, UK
| | - Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Edward L Campbell
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; GTM research group, AtlanTTic Research Center, University of Vigo, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, King's College London, UK
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Amos A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragon Institute for Engineering Research, University of Zaragoza, Zaragoza, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Björn W Schuller
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany; GLAM - Group on Language, Audio, & Music, Imperial College London, London, UK
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King's College London, London, UK
| | | | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK
| | | | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King's College London, London, UK
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Oh J, Hwang S, Lee J. Enhancing X-ray-Based Wrist Fracture Diagnosis Using HyperColumn-Convolutional Block Attention Module. Diagnostics (Basel) 2023; 13:2927. [PMID: 37761294 PMCID: PMC10529517 DOI: 10.3390/diagnostics13182927] [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: 08/07/2023] [Revised: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 and DenseNet169 models bolstered by the HyperColumn and the CBAM, distinct improvements in fracture site prediction emerge. Significantly, when HyperColumn and CBAM integration is applied, both DenseNet169 and EfficientNet-B0 showed noteworthy accuracy improvements, with increases of approximately 0.69% and 0.70%, respectively. The HyperColumn-CBAM-DenseNet169 model particularly stood out, registering an uplift in the AUC score from 0.8778 to 0.9145. The incorporation of Grad-CAM technology refined the heatmap's focus, achieving alignment with expert-recognized fracture sites and alleviating the deep-learning challenge of heavy reliance on bounding box annotations. This innovative approach signifies potential strides in streamlining training processes and augmenting diagnostic precision in fracture detection.
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Affiliation(s)
- Joonho Oh
- Department of Mechanical Engineering, Chosun University, Gwangju 61452, Republic of Korea;
| | - Sangwon Hwang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea;
| | - Joong Lee
- Artificial Intelligence BigData Medical Center, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
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Voppel AE, de Boer JN, Brederoo SG, Schnack HG, Sommer IEC. Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders: A Combinatory Machine Learning Approach. Schizophr Bull 2023; 49:S163-S171. [PMID: 36305054 PMCID: PMC10031732 DOI: 10.1093/schbul/sbac142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND AND HYPOTHESIS Speech is a promising marker to aid diagnosis of schizophrenia-spectrum disorders, as it reflects symptoms like thought disorder and negative symptoms. Previous approaches made use of different domains of speech for diagnostic classification, including features like coherence (semantic) and form (acoustic). However, an examination of the added value of each domain when combined is lacking as of yet. Here, we investigate the acoustic and semantic domains separately and combined. STUDY DESIGN Using semi-structured interviews, speech of 94 subjects with schizophrenia-spectrum disorders (SSD) and 73 healthy controls (HC) was recorded. Acoustic features were extracted using a standardized feature-set, and transcribed interviews were used to calculate semantic word similarity using word2vec. Random forest classifiers were trained for each domain. A third classifier was used to combine features from both domains; 10-fold cross-validation was used for each model. RESULTS The acoustic random forest classifier achieved 81% accuracy classifying SSD and HC, while the semantic domain classifier reached an accuracy of 80%. Joining features from the two domains, the combined classifier reached 85% accuracy, significantly improving on separate domain classifiers. For the combined classifier, top features were fragmented speech from the acoustic domain and variance of similarity from the semantic domain. CONCLUSIONS Both semantic and acoustic analyses of speech achieved ~80% accuracy in classifying SSD from HC. We replicate earlier findings per domain, additionally showing that combining these features significantly improves classification performance. Feature importance and accuracy in combined classification indicate that the domains measure different, complementing aspects of speech.
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Affiliation(s)
- Alban E Voppel
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Janna N de Boer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sanne G Brederoo
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Utrecht University, Utrecht Institute of Linguistics OTS, Utrecht, the Netherlands
| | - Iris E C Sommer
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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Brigo F, Lorusso L, Walusinski O, Drouin E. Voices from the past: The pioneering use of the phonograph in neurology. Rev Neurol (Paris) 2023; 179:137-140. [PMID: 36150939 DOI: 10.1016/j.neurol.2022.06.007] [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: 03/28/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 10/14/2022]
Abstract
Since its discovery by the American inventor and industrialist Thomas Alva Edison (1847-1931) in 1877, the phonograph attracted much interest in the field of medicine. This article describes the earliest pioneering examples of the use of the phonograph in neurology. In France, the use of the phonograph for obtaining audio recordings of delusions and speech or language disturbances was first proposed by Victor Maurice Dupont (1857-1910) in 1889 and in Italy by the physician Gaetano Rummo (1853-1917), who had studied at La Salpêtrière under Jean-Martin Charcot (1825-1893). The applicability of the phonograph to the record of speech disturbances was illustrated in England by John Hughlings Jackson (1835-1911) and William Halse Rivers (1864-1922), and by William Hale White (1857-1949) and Cuthbert Hilton Golding-Bird (1848-1939) in 1891. Since then, audio recordings have been used rarely in neurology, a branch of medicine where the visual aspects dominate, to the extent that inspection can be enough to reach a definite clinical diagnosis. In the mid-20th century, the advent of audio and video recordings supplanted audio recordings alone, relegating them to a very marginal role.
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Affiliation(s)
- F Brigo
- Department of Neurology, Hospital of Merano (SABES-ASDAA), Merano, Italy.
| | - L Lorusso
- UOC Neurology and Stroke Unit, ASST Lecco, Merate, Italy
| | | | - E Drouin
- Service de neurologie, groupe hospitalier de l'institut catholique de Lille, GHICL, Lille, France
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