A preliminary study on self-care telemonitoring of dysarthria in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a rare neuromuscular disease which may cause impairments in oro-facial musculature. Most of the individuals with SMA present bulbar signs such as flaccid dysarthria which mines their abilities to speak and, as consequence, their psychic balance. To support clinicians, recent work has demonstrated the feasibility of video-based techniques for assessing the oro-facial functions in patients with neurological disorders such as amyotrophic lateral sclerosis. However, no work has so far focused on automatic and quantitative monitoring of dysarthria in SMA. To overcome limitations this work's aim is to propose a cloud-based store-and-forward telemonitoring system for automatic and quantitative evaluation of oro-facial muscles in individuals with SMA. The system integrates a convolutional neural network (CNN) aimed at identifying the position of facial landmarks from video recordings acquired via a web application by an SMA patient.Clinical relevance- The proposed work is in the preliminary stage, but it represents the first step towards a better understanding of the bulbar-functions' evolution in patients with SMA.

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

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 patients' 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.

TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images.

Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainable deep-learning algorithm for accurate limb-pose estimation from depth images. The algorithm consists of a convolutional neural network (TwinEDA) relying on architectural blocks that require limited computation while ensuring high performance in prediction. To ascertain its low computational costs and assess its application in on-the-edge computing, TwinEDA was additionally deployed on a cost-effective single-board computer. The network was validated on a dataset of 27,000 depth video frames collected during the actual clinical practice from 27 preterm infants. When compared to the main state-of-the-art competitor, TwinEDA is twice as fast to predict a single depth frame and four times as light in terms of memory, while performing similarly in terms of Dice similarity coefficient (0.88). This result suggests that the pursuit of efficiency does not imply the detriment of performance. This work is among the first to propose an automatic and sustainable limb-position estimation approach for preterm infants. This represents a significant step towards the development of broadly accessible clinical monitoring applications.

Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.

Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting preterm-birth complications. With such a view, in this work we propose a deep-learning framework for preterm infants' pose estimation from depth videos acquired in the actual clinical practice. The pipeline consists of two consecutive convolutional neural networks (CNNs). The first CNN (inherited from our previous work) acts to roughly predict joints and joint-connections position, while the second CNN (Asy-regression CNN) refines such predictions to trace the limb pose. Asy-regression relies on asymmetric convolutions to temporally optimize both the training and predictions phase. Compared to its counterpart without asymmetric convolutions, Asy-regression experiences a reduction in training and prediction time of 66% , while keeping the root mean square error, computed against manual pose annotation, merely unchanged. Research mostly works to develop highly accurate models, few efforts have been invested to make the training and deployment of such models time-effective. With a view to make these monitoring technologies sustainable, here we focused on the second aspect and addressed the problem of designing a framework as trade-off between reliability and efficiency.

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients.

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.