Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech

Chlasta, Karol; Wołk, Krzysztof

doi:10.3389/fpsyg.2020.623237

Cited by 24 publications

(22 citation statements)

References 33 publications

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“…Among the 25 studies applying feature-representation transfer, support vector machines and 'vanilla' neural networks were the most common methods used as the final model producing outputs based on the feature representations. Seven studies applied and compared multiple methods for this purpose [18][19][20][21][22][23][24].…”

Section: Resultsmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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Section: Resultsmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

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Section: Resultsmentioning

confidence: 99%

“…Despite the increasing interest and opportunities for relatively easy and cheap data collection, we only found 10 studies that used transfer learning on audio data. However, these studies covered a variety of fields in medicine (and corresponding audio signals): neurology (speech and electromyography [21, 22, 86, 87]), cardiology (heart sound [88, 89]), pulmonology (respiratory sounds [90, 91]), infectious diseases (cough [92]), and otorhinolaryngology (breathing [93]).…”

Section: Discussionmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: a scoping review

Ebbehøj

Thunbo

Andersen

et al. 2021

Preprint

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and Findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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“…Except for the text-based pre-trained models, audio and image-based pre-trained models also have been explored in speechbased AD detection. Chlasta, K. et al [48] trained modified VGGNet architecture to extract acoustic embedding, while Gauder, L. et al [49] trained wav2vec 2.0 framework to extract acoustic embedding vector, of which both added modified CNN modules for classification, reaching 62.5% and 78.9% accuracy, respectively.…”

Section: Comparisons Of Methods For the Adress Challengementioning

confidence: 99%

Deep learning-based speech analysis for Alzheimer’s disease detection: a literature review

Yang¹,

Ding³

et al. 2022

Alz Res Therapy

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Background Alzheimer’s disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared with traditional detection methods such as traditional scale tests, electroencephalograms, and magnetic resonance imaging, speech analysis is more convenient for automatic large-scale Alzheimer’s disease detection and has attracted extensive attention from researchers. In particular, deep learning-based speech analysis and language processing techniques for Alzheimer’s disease detection have been studied and achieved impressive results. Methods To integrate the latest research progresses, hundreds of relevant papers from ACM, DBLP, IEEE, PubMed, Scopus, Web of Science electronic databases, and other sources were retrieved. We used these keywords for paper search: (Alzheimer OR dementia OR cognitive impairment) AND (speech OR voice OR audio) AND (deep learning OR neural network). Conclusions Fifty-two papers were finally retained after screening. We reviewed and presented the speech databases, deep learning methods, and model performances of these studies. In the end, we pointed out the mainstreams and limitations in the current studies and provided a direction for future research.

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Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech

Cited by 24 publications

References 33 publications

Transfer learning for non-image data in clinical research: A scoping review

Transfer learning for non-image data in clinical research: A scoping review

Transfer learning for non-image data in clinical research: a scoping review

Deep learning-based speech analysis for Alzheimer’s disease detection: a literature review

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