Evaluation of speech-based protocol for detection of early-stage dementia

Satt, Aharon; Sorin, Alexander; Toledo-Ronen, Orith; Barkan, Oren; Kompatsiaris, Ioannis; Kokonozi, Athina; Tsolaki, Magda

doi:10.21437/interspeech.2013-32

Cited by 46 publications

(20 citation statements)

References 2 publications

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“…Even when the dataset contains three or four groups (e.g. HC, SCI, MCI, AD), most studies only report pairwise group comparisons [57][58][59][60][61]. Out of 51 reviewed papers, only seven did attempt three-way [49,[62][63][64] or four-way [12,65,66] classification.…”

Section: Study Aim and Design (Table 5: Spicmo)mentioning

confidence: 99%

“…Some studies report no demographics [6,66,68,73,74], only age [10,75], only age and gender [61,76,77], or only age and education [70,78]. An exception is the dataset AZTIAHORE [79,80], which contains the youngest healthy group (20-90 years old) and a typical AD group (68-98 years old), introducing potential biases due to this imbalance.…”

Section: Population (Table 5: Spicmo)mentioning

confidence: 99%

“…The comparison groups are based on diagnosis groups, which in turn are established with the results of such assessments. Therefore, papers lacking that information do not specify their criteria for group assignment [60,61,75,79,80,[82][83][84][85]. This could be problematic, since the field currently revolves around diagnostic categories, trying to identify such categories through speech data.…”

Section: Interventions (Table 5: Spicmo)mentioning

confidence: 99%

“…A few studies (6) work with audio files and associated ASR transcriptions [12,43,62,63,77,99]. Another group of studies ( 14), use solely voice recordings [49,57,60,61,64,65,71,79,80,82,84,89,97,100]. More than half of the studies (55%) rely, at least partially, on manually transcribed data.…”

Section: Data Type (Table 6: Data Details)mentioning

confidence: 99%

“…As expected, a number of studies (41%) were conducted through English. However, there is a fair amount of papers using data in a variety of languages, including: Italian [91], Portuguese [57,90], Chinese and Taiwanese [82], French [49,69,77,96,102], Hungarian [62,63,99], Spanish [83,89,100], Swedish [7,59,87], Japanese [64,71,94], Turkish [84], Persian [65], Greek [61,88], German [68,74] or reported as multilingual [60,79,80]. This is essential if screening methodologies for AD are to be implemented worldwide [103].…”

Section: Language (Table 6: Data Details)mentioning

confidence: 99%

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Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

152

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Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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Section: Study Aim and Design (Table 5: Spicmo)mentioning

confidence: 99%

Section: Population (Table 5: Spicmo)mentioning

confidence: 99%

Section: Interventions (Table 5: Spicmo)mentioning

confidence: 99%

Section: Data Type (Table 6: Data Details)mentioning

confidence: 99%

Section: Language (Table 6: Data Details)mentioning

confidence: 99%

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Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

152

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Clinical classification of memory and cognitive impairment with multimodal digital biomarkers

Banks,

Higgins,

Greene

et al. 2024

Alz & Dem Diag Ass & Dis Mo

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INTRODUCTIONEarly detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline.METHODSTo evaluate multi‐modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test‐determined memory impairment.RESULTSMultimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics models.DISCUSSIONThe current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.

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A novel approach of boundary preservative apparel detection and classification of fashion images using deep learning

Murtaza

Sharif

Yasmin

et al. 2022

Math Methods in App Sciences

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Visual analysis of fashion images gain much attention in the fashion industry due to its commercial and social importance. In recent years, deep learning techniques offer overwhelming progress in improving the accuracy of fine‐grained apparel segmentation with accurate bounding box prediction. The baseline pixel‐based masking techniques show excellent performance in object detection and segmentation but sometimes ignores the boundary of objects, resulting in uneven and complicated segmentation masks. Moreover, it is time taking to generate a multi‐scale feature map against each anchor box. To remedy this problem, a more accurate, faster, and suitable deep learning architecture is proposed that accurately detects, classify, and performs fine‐grained segmentation of cloth products in a single platform. In this paper, initially, an Object Class Head Detector model is proposed in which the baseline Mask‐RCNN model is used as a reference model. Here, we replace the Region Proposal Network with the proposed modified YoloV2 model to locate apparel products with its class prediction. The modified YoloV2 model has more capability to detect tiny objects because of local and high‐level feature fusion. The goal of this step is to accurately locate the objects in minimum time intervals. Furthermore, the predicted bounding box is converted to object shape offsets using deep snake architecture that tightly fits onto the apparel shape. It can improve the accuracy of cloth shape segmentation by preserving object contours. The proposed architecture is empirically validated on various existing fashion image datasets. The experimental results illustrate that the proposed architecture performs better on the Deepfashion2 dataset with mAP of 86.86%, as compared to other state‐of‐the‐art deep learning models.

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Evaluation of speech-based protocol for detection of early-stage dementia

Cited by 46 publications

References 2 publications

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Clinical classification of memory and cognitive impairment with multimodal digital biomarkers

A novel approach of boundary preservative apparel detection and classification of fashion images using deep learning

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