A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

• URL: http://arxiv.org/abs/2309.09038v1
• Date: Sat, 16 Sep 2023 16:24:11 GMT
• Title: A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis
• Authors: Lucia Migliorelli, Daniele Berardini, Kevin Cela, Michela Coccia, Laura Villani, Emanuele Frontoni, Sara Moccia
• Abstract summary: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. This work presents a store-and-forward telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria.
• Score: 8.275082697744084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background and objectives: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patient management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. Methods: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. Results: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. Discussion and conclusions: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria.

Related papers

• AutoRG-Brain: Grounded Report Generation for Brain MRI [57.22149878985624]
  Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. We initiate a series of work on grounded Automatic Report Generation (AutoRG) This system supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized findings.
  arXiv  Detail & Related papers  (2024-07-23T17:50:00Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases [0.2686968510141288]
  Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements.
  arXiv  Detail & Related papers  (2024-04-19T16:34:15Z)
• Estimating the severity of dental and oral problems via sentiment classification over clinical reports [0.8287206589886879]
  Analyzing authors' sentiments in texts can be practical and useful in various fields, including medicine and dentistry. Deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect severity level of patient's problem.
  arXiv  Detail & Related papers  (2024-01-17T14:33:13Z)
• Deep Learning Approaches for Seizure Video Analysis: A Review [40.1521024778093]
  Computer-aided video analysis of seizures has emerged as a natural avenue. Deep learning and computer vision approaches have driven substantial advancements. Main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization.
  arXiv  Detail & Related papers  (2023-12-18T04:45:41Z)
• The Face of Affective Disorders [7.4005714204825646]
  We study the statistical properties of facial behaviour altered by the regulation of brain arousal in the clinical domain of psychiatry. We name the presented measurement in the sense of the classical scalp based obtrusive sensors Opto Electronic Encephalography (OEG) which relies solely on modern camera based real-time signal processing and computer vision.
  arXiv  Detail & Related papers  (2022-08-02T11:28:17Z)
• fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits [62.997667081978825]
  We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI) The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session.
  arXiv  Detail & Related papers  (2021-12-21T06:52:48Z)
• Learning Personal Representations from fMRIby Predicting Neurofeedback Performance [52.77024349608834]
  We present a deep neural network method for learning a personal representation for individuals performing a self neuromodulation task, guided by functional MRI (fMRI) The representation is learned by a self-supervised recurrent neural network, that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation.
  arXiv  Detail & Related papers  (2021-12-06T10:16:54Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation [33.818136671925444]
  We propose the neural-symbolic learning framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning. Our algorithm remarkably exceeds existing methods in the detection of spinal structures.
  arXiv  Detail & Related papers  (2020-04-28T15:06:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.