Topic
Dysarthria
About: Dysarthria is a research topic. Over the lifetime, 2402 publications have been published within this topic receiving 56554 citations.
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Abstract: Severe hemophilia A and moyamoya (SHAM) syndrome is a rare condition that combines hemophilia A and moyamoya disease (MMD) due to an Xq28 microdeletion encompassing the F8 and BRCC3 genes. Here, we report the case of a 19-year-old male patient with hemophilia A and hypogonadism that presented with right-sided hemiparesis and dysarthria. Brain magnetic resonance imaging and angiography revealed an ischemic lesion in the left lobe and stenosis of both middle cerebral arteries with a concomitant thick vascular network, compatible with moyamoya disease. Next-generation sequence revealed a large Xq28 deletion compatible with SHAM syndrome. The patient was treated with acetylsalicylic acid and neurosurgical intervention was scheduled. Our patient is one of the few cases reported in the literature with Xq28 microdeletion encompassing the F8, hemophilia A causative gene, and BRCC3, responsible for MMD, presenting with a compound phenotype that included neurological manifestations and hypogonadism. In conclusion, diagnosis of MMD should be considered in any male, young patient with symptoms of ischemic stroke with no obvious explanation, and especially in patients with known hemophilia, since a relationship between the two conditions has been documented.
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TL;DR: The authors studied the features of dialogues between people with Parkinson's disease (PwPD) and other people in communicative contexts and found that PD leads to changes in verbal communications.
Abstract: Parkinson's disease (PD) leads to changes in verbal communications. The focus of most studies to date has been on speech impairment, which is specifically referred to as dysarthria. Although these studies are crucial to understanding the impact of PD on verbal communication, they do not focus on the features of dialogues between people with PD (PwPD) and other people in communicative contexts.
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TL;DR: The results hold promise for the use of AI with this clinical population, although a full range of speech severity needs to be evaluated in future work, as well as the effect of different speaking tasks on ASR.
Abstract: Background Most individuals with Parkinson disease (PD) experience a degradation in their speech intelligibility. Research on the use of automatic speech recognition (ASR) to assess intelligibility is still sparse, especially when trying to replicate communication challenges in real-life conditions (ie, noisy backgrounds). Developing technologies to automatically measure intelligibility in noise can ultimately assist patients in self-managing their voice changes due to the disease. Objective The goal of this study was to pilot-test and validate the use of a customized web-based app to assess speech intelligibility in noise in individuals with dysarthria associated with PD. Methods In total, 20 individuals with dysarthria associated with PD and 20 healthy controls (HCs) recorded a set of sentences using their phones. The Google Cloud ASR API was used to automatically transcribe the speakers’ sentences. An algorithm was created to embed speakers’ sentences in +6-dB signal-to-noise multitalker babble. Results from ASR performance were compared to those from 30 listeners who orthographically transcribed the same set of sentences. Data were reduced into a single event, defined as a success if the artificial intelligence (AI) system transcribed a random speaker or sentence as well or better than the average of 3 randomly chosen human listeners. These data were further analyzed by logistic regression to assess whether AI success differed by speaker group (HCs or speakers with dysarthria) or was affected by sentence length. A discriminant analysis was conducted on the human listener data and AI transcriber data independently to compare the ability of each data set to discriminate between HCs and speakers with dysarthria. Results The data analysis indicated a 0.8 probability (95% CI 0.65-0.91) that AI performance would be as good or better than the average human listener. AI transcriber success probability was not found to be dependent on speaker group. AI transcriber success was found to decrease with sentence length, losing an estimated 0.03 probability of transcribing as well as the average human listener for each word increase in sentence length. The AI transcriber data were found to offer the same discrimination of speakers into categories (HCs and speakers with dysarthria) as the human listener data. Conclusions ASR has the potential to assess intelligibility in noise in speakers with dysarthria associated with PD. Our results hold promise for the use of AI with this clinical population, although a full range of speech severity needs to be evaluated in future work, as well as the effect of different speaking tasks on ASR.
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TL;DR: For patients with probable or definite motor neuron disease according to established criteria, the recent introduction of the antiglutamate drug riluzole gives a mean prolongation of life after diagnosis of some 3 months.
Abstract: Motor neuron disease is still one of the most dramatic neurological conditions; it reflects a range of which one end encompasses patients with the bulbar form of amyotrophic lateral sclerosis who have dysarthria and dysphagia and have a dramatically shortened life expectancy. For patients with probable or definite motor neuron disease according to established criteria, the recent introduction of the antiglutamate drug riluzole gives a mean prolongation of life after diagnosis of some 3 months.1 The other end of the range of motor neuron disease is formed by patients with so called multifocal motor neuropathy. This is characterised by lower motor neuron dysfunction in the areas of selected motor nerves. Neurophysiological demonstration of conduction block over these nerves is pivotal for the diagnosis.2 Before the discovery of multifocal motor neuropathy as a separate and treatable condition, patients with …
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