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Chen Z, Liang N, Li H, Zhang H, Li H, Yan L, Hu Z, Chen Y, Zhang Y, Wang Y, Ke D, Shi N. Exploring explainable AI features in the vocal biomarkers of lung disease. Comput Biol Med 2024; 179:108844. [PMID: 38981214 DOI: 10.1016/j.compbiomed.2024.108844] [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: 01/02/2024] [Revised: 05/15/2024] [Accepted: 06/04/2024] [Indexed: 07/11/2024]
Abstract
This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haoyuan Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lijiao Yan
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ziteng Hu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yujing Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dandan Ke
- Special Disease Clinic, Huaishuling Branch of Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
| | - Nannan Shi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
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Fusaroli M, Simonsen A, Borrie SA, Low DM, Parola A, Raschi E, Poluzzi E, Fusaroli R. Identifying Medications Underlying Communication Atypicalities in Psychotic and Affective Disorders: A Pharmacovigilance Study Within the FDA Adverse Event Reporting System. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023; 66:3242-3259. [PMID: 37524118 DOI: 10.1044/2023_jslhr-22-00739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
PURPOSE Communication atypicalities are considered promising markers of a broad range of clinical conditions. However, little is known about the mechanisms and confounders underlying them. Medications might have a crucial, relatively unknown role both as potential confounders and offering an insight on the mechanisms at work. The integration of regulatory documents with disproportionality analyses provides a more comprehensive picture to account for in future investigations of communication-related markers. The aim of this study was to identify a list of drugs potentially associated with communicative atypicalities within psychotic and affective disorders. METHOD We developed a query using the Medical Dictionary for Regulatory Activities to search for communicative atypicalities within the FDA Adverse Event Reporting System (updated June 2021). A Bonferroni-corrected disproportionality analysis (reporting odds ratio) was separately performed on spontaneous reports involving psychotic, affective, and non-neuropsychiatric disorders, to account for the confounding role of different underlying conditions. Drug-adverse event associations not already reported in the Side Effect Resource database of labeled adverse drug reactions (unexpected) were subjected to further robustness analyses to account for expected biases. RESULTS A list of 291 expected and 91 unexpected potential confounding medications was identified, including drugs that may irritate (inhalants) or desiccate (anticholinergics) the larynx, impair speech motor control (antipsychotics), or induce nodules (acitretin) or necrosis (vascular endothelial growth factor receptor inhibitors) on vocal cords; sedatives and stimulants; neurotoxic agents (anti-infectives); and agents acting on neurotransmitter pathways (dopamine agonists). CONCLUSIONS We provide a list of medications to account for in future studies of communication-related markers in affective and psychotic disorders. The current test case illustrates rigorous procedures for digital phenotyping, and the methodological tools implemented for large-scale disproportionality analyses can be considered a road map for investigations of communication-related markers in other clinical populations. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.23721345.
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Affiliation(s)
- Michele Fusaroli
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Italy
| | - Arndis Simonsen
- Psychosis Research Unit, Department of Clinical Medicine, Aarhus University, Denmark
- Interacting Minds Centre, School of Culture and Society, Aarhus University, Denmark
| | - Stephanie A Borrie
- Department of Communicative Disorders and Deaf Education, Utah State University, Logan
| | - Daniel M Low
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge
- Speech and Hearing Bioscience and Technology Program, Harvard Medical School, Boston, MA
| | - Alberto Parola
- Department of Psychology, University of Turin, Italy
- Department of Linguistics, Cognitive Science and Semiotics, School of Communication and Culture, Aarhus University, Denmark
| | - Emanuel Raschi
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Italy
| | - Elisabetta Poluzzi
- Pharmacology Unit, Department of Medical and Surgical Sciences, University of Bologna, Italy
| | - Riccardo Fusaroli
- Interacting Minds Centre, School of Culture and Society, Aarhus University, Denmark
- Department of Linguistics, Cognitive Science and Semiotics, School of Communication and Culture, Aarhus University, Denmark
- Linguistic Data Consortium, School of Arts & Sciences, University of Pennsylvania, Philadelphia
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Chu CS, Wang DY, Liang CK, Chou MY, Hsu YH, Wang YC, Liao MC, Chu WT, Lin YT. Automated Video Analysis of Audio-Visual Approaches to Predict and Detect Mild Cognitive Impairment and Dementia in Older Adults. J Alzheimers Dis 2023; 92:875-886. [PMID: 36847001 DOI: 10.3233/jad-220999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND Early identification of different stages of cognitive impairment is important to provide available intervention and timely care for the elderly. OBJECTIVE This study aimed to examine the ability of the artificial intelligence (AI) technology to distinguish participants with mild cognitive impairment (MCI) from those with mild to moderate dementia based on automated video analysis. METHODS A total of 95 participants were recruited (MCI, 41; mild to moderate dementia, 54). The videos were captured during the Short Portable Mental Status Questionnaire process; the visual and aural features were extracted using these videos. Deep learning models were subsequently constructed for the binary differentiation of MCI and mild to moderate dementia. Correlation analysis of the predicted Mini-Mental State Examination, Cognitive Abilities Screening Instrument scores, and ground truth was also performed. RESULTS Deep learning models combining both the visual and aural features discriminated MCI from mild to moderate dementia with an area under the curve (AUC) of 77.0% and accuracy of 76.0% . The AUC and accuracy increased to 93.0% and 88.0%, respectively, when depression and anxiety were excluded. Significant moderate correlations were observed between the predicted cognitive function and ground truth, and the correlation was strong excluding depression and anxiety. Interestingly, female, but not male, exhibited a correlation. CONCLUSION The study showed that video-based deep learning models can differentiate participants with MCI from those with mild to moderate dementia and can predict cognitive function. This approach may offer a cost-effective and easily applicable method for early detection of cognitive impairment.
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Affiliation(s)
- Che-Sheng Chu
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan.,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Di-Yuan Wang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Kuang Liang
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Geriatric Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Internal Medicine, Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Kaohsiung City, Taiwan
| | - Ming-Yueh Chou
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Geriatric Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan
| | - Ying-Hsin Hsu
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Department of Internal Medicine, Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Kaohsiung City, Taiwan.,Chia Nan University, Tainan, Taiwan, Tainan City, Taiwan
| | - Yu-Chun Wang
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Mei-Chen Liao
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Wei-Ta Chu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Yu-Te Lin
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Pharmacy, Tajen University, Pingtung, Taiwan, Yanpu Township, Pingtung County, Taiwan
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Dikaios K, Rempel S, Dumpala SH, Oore S, Kiefte M, Uher R. Applications of Speech Analysis in Psychiatry. Harv Rev Psychiatry 2023; 31:1-13. [PMID: 36608078 DOI: 10.1097/hrp.0000000000000356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
ABSTRACT The need for objective measurement in psychiatry has stimulated interest in alternative indicators of the presence and severity of illness. Speech may offer a source of information that bridges the subjective and objective in the assessment of mental disorders. We systematically reviewed the literature for articles exploring speech analysis for psychiatric applications. The utility of speech analysis depends on how accurately speech features represent clinical symptoms within and across disorders. We identified four domains of the application of speech analysis in the literature: diagnostic classification, assessment of illness severity, prediction of onset of illness, and prognosis and treatment outcomes. We discuss the findings in each of these domains, with a focus on how types of speech features characterize different aspects of psychopathology. Models that bring together multiple speech features can distinguish speakers with psychiatric disorders from healthy controls with high accuracy. Differentiating between types of mental disorders and symptom dimensions are more complex problems that expose the transdiagnostic nature of speech features. Convergent progress in speech research and computer sciences opens avenues for implementing speech analysis to enhance objectivity of assessment in clinical practice. Application of speech analysis will need to address issues of ethics and equity, including the potential to perpetuate discriminatory bias through models that learn from clinical assessment data. Methods that mitigate bias are available and should play a key role in the implementation of speech analysis.
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Affiliation(s)
- Katerina Dikaios
- From: Dalhousie University, Department of Psychiatry, Halifax, NS (Ms. Dikaios, Dr. Uher); Novia Scotia Health, Halifax, NS (Ms. Rempel); Faculty of Computer Science, Dalhousie University, and Vector Institute for Artificial Intelligence, University of Toronto (Mr. Dumpala, Dr. Oore); School of Communication Sciences and Disorders, Dalhousie University (Dr. Kiefte)
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Tonn P, Seule L, Degani Y, Herzinger S, Klein A, Schulze N. Evaluation of a Digital Content-free Speech Analysis Tool to Measure Affective Distress in Mental Health (Preprint). JMIR Form Res 2022; 6:e37061. [PMID: 36040767 PMCID: PMC9472064 DOI: 10.2196/37061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Peter Tonn
- Neuropsychiatric Center of Hamburg, Hamburg, Germany
| | - Lea Seule
- Neuropsychiatric Center of Hamburg, Hamburg, Germany
| | | | | | | | - Nina Schulze
- Neuropsychiatric Center of Hamburg, Hamburg, Germany
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Shen J, Zhang S, Tong Y, Dong X, Wang X, Fu G, Zhao L, Wu M, Yin Y, Wang Y, Liu NH, Wu J, Li J. Establishment and psychometric characteristics of emotional words list for suicidal risk assessment in speech emotion recognition. Front Psychiatry 2022; 13:1022036. [PMID: 36440401 PMCID: PMC9691664 DOI: 10.3389/fpsyt.2022.1022036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/27/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Emotional disturbance is an important risk factor of suicidal behaviors. To ensure speech emotion recognition (SER), a novel technique to evaluate emotional characteristics of speech, precision in labeling emotional words is a prerequisite. Currently, a list of suicide-related emotional word is absent. The aims of this study were to establish an Emotional Words List for Suicidal Risk Assessment (EWLSRA) and test the reliability and validity of the list in a suicide-related SER task. METHODS Suicide-related emotion words were nominated and discussed by 10 suicide prevention professionals. Sixty-five tape-recordings of calls to a large psychological support hotline in China were selected to test psychometric characteristics of the EWLSRA. RESULTS The results shows that the EWLSRA consists of 11 emotion words which were highly associated with suicide risk scores and suicide attempts. Results of exploratory factor analysis support one-factor model of this list. The Fleiss' Kappa value of 0.42 indicated good inter-rater reliability of the list. In terms of criteria validities, indices of despair (Spearman ρ = 0.54, P < 0.001), sadness (ρ = 0.37, P = 0.006), helplessness (ρ = 0.45, P = 0.001), and numbness (ρ = 0.35, P = 0.009) were significantly associated with suicidal risk scores. The index of the emotional word of numbness in callers with suicide attempt during the 12-month follow-up was significantly higher than that in callers without suicide attempt during the follow-up (P = 0.049). CONCLUSION This study demonstrated that the EWLSRA has adequate psychometric performance in identifying suicide-related emotional words of recording of hotline callers to a national wide suicide prevention line. This list can be useful for SER in future studies on suicide prevention.
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Affiliation(s)
- Juan Shen
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China
| | - Shuo Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Yongsheng Tong
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China.,Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Xiangmin Dong
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Xuelian Wang
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China.,Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Guanghui Fu
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Liting Zhao
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China
| | - Mengjie Wu
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China.,Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yi Yin
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China.,Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yuehua Wang
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China
| | - Nancy H Liu
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Jianlan Wu
- Beijing Suicide Research and Prevention Center, Beijing Huilongguan Hospital, Beijing, China.,WHO Collaborating Center for Research and Training in Suicide Prevention, Beijing, China.,Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
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