Irbm最新文献

Objectives

The well-being of people depends in part on the sense of freedom, and one aspect is certainly the possibility for people to remain at home. However, there is a need for “following” the movements and, if possible, the activity of the person. The problem is that very few home systems make it possible to have these data at a reasonable price, and at an acceptable reliability level. We offer a simple to use, reliable and energy self-sufficient person location system. People are the first “targets”, but objects could be involved.

Material and methods

The system is described and their performance analyzed in real conditions of use. The positioning algorithms are explained and the practical implementations described.

Results

First results on the activity of a person at home are presented as well as some tracks on the type of data processing that could be considered.

The simplicity of deployment is shown and the usefulness of the available data is discussed in the context of home care of an elderly person as well as the monitoring of hospital equipment.

Conclusion

Our approach provides simplicity of implementation and very high reliability in real time, without aiming for high accuracy in all cases. Conceptually taking into account the high variability of indoor radio measurements makes it possible to significantly increase the reliability of the geo-data produced. Moreover, we will mention two real deployments and the associated performances obtained, carried out in order to follow the behavior of an old autonomous man living alone at home, and in another hand to follow the stretchers of the emergency department of a French hospital.

Objectives

Frailty is a geriatric syndrome characterized by sarcopenia and physiological impairment. Although the majority of older adults wish to age at home, being frail threatens this choice since it increases the risk of falls and loss of functional independence. Hence, frailty screening and early detection are needed to stop or at least slow down the physical weakening process. In this paper, we present a review in which we discuss the proposed methods from the literature that targets frailty detection and analysis, starting from traditional clinical tools then introducing data-driven studies before highlighting the importance of fully automated systems.

Material and methods

We conducted a review study by searching several databases such as Google Scholar, IEEE Xplore, MDPI, and ScienceDirect to name a few. This work presents clinical tools and classical performance tests to assess the health status and the physical function, as well as statistical and observational studies to analyze the frailty syndrome. Moreover, we discuss briefly the work of our research team in this context, represented by the development of a telemonitoring system which aims at the transition from a curative to a preventive model.

Results

Firstly, this review points out the absence of a gold standard to detect frailty in older individuals. Secondly, it discusses the limitations of self-reported measures/questionnaires and other traditional performance tests which are based on subjective data and done under supervised conditions. Thirdly, our study emphasizes the lack of robust approaches that target the early detection of frailty and the prediction of a future risk of physical worsening. We propose new research directions based, on the one hand, on automatic activity identification and tracking and, on the other hand, on the analysis of spontaneous speech of elderly.

Conclusion

This paper describes research findings and highlights the existing gaps in the context of frailty, and serves as a state of the art for researchers. Additionally, this work suggests future research directions regarding the early detection and prevention of frailty.

Objectives

This study aims to identify the dielectric constant of the carotid atherosclerotic plaques and categorise them using a CSRR based microwave sensor.

Material and methods

A Complementary Split Ring Resonator (CSRR) at 2.3 GHz measured 33 samples of carotid plaques obtained after endarterectomy. HFSS software simulations were employed to substantiate the measurements. Histological analyses were performed simultaneously to classify the plaques.

Results

The constant dielectric of dangerous carotid plaques identified by histology was much higher than that of low-risk calcified carotid plaques. Microwave data were pertinent to the simulations.

Conclusion

The current study, performed on ex-vivo carotid plaques, illustrates the sensor's ability to differentiate plaques with diverse components. Calcified low-risk plaques displayed distinct values from dangerous soft plaques. Further statistical correlation of the 33 samples is required. After validation, an in-vivo prototype will be designed and tested.

Introduction

Although the many advantages of Interventional Radiology not only being a minimally invasive surgery but also providing minimal risk of infection for the patient, this procedure could cause serious damage (radio dermatitis) to the patient and surgeons if exposed for long periods to the X-ray radiation. Some medical solutions have been found, but need the installation of extra equipment in the operating room.

Objectives

The aim of the Medic@ team is to reduce the doses of X-rays using sensors integrated into the catheter to reconstruct images without the need of continuous imaging. To do that, accurate and reliable information on the position of the catheter is required to correct the drift of the catheter's sensors. The use of artificial intelligence with a U-Net convolutional neural network is a possible solution for detecting the entire catheter (body and head) and for obtaining precise coordinates in X-ray images.

Material and methods

The use of artificial intelligence with a U-Net convolutional neural network is a possible solution for detecting the entire catheter (body and head) and for obtaining precise coordinates in X-ray images. We have created and used synthetic data to generate training datasets and videos that simulate real-world operations because we only have low quantity of data.

Results

The results using the metrics binary cross entropy and dice loss testing in the synthetic data are 0. 048 and 0.98 respectively. We have also tested to predict catheter shapes on some real images; in a general way, the results show good approximation in the detection of the head of the catheter (around 3.1 pixels) using Euclidean distance. Finally, the predictions are also robust in blurry synthetic images using 5, 10 and 15 kernel sizes; in this case, the binary cross entropy in all the cases is less than 0.05 and the dice loss in all the cases is more than 0.98.

Conclusions

The methodology used to create synthetic images and videos seems to be correct. The predictions in the detection of the shape of catheters, after training with synthetic images calibrated with the same histogram of the real images, show very good results in the metrics: binary cross entropy and dice loss. The same for the case of blurry images. The tests in the few real images are encouraging because the error detection in the head of the catheter is small (<3.1 pixels). More tests with real data are still necessary for validating this first approach.

Objective

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a powerful genome editing technology. Guide RNA (gRNA) plays an essential guiding role in the CRISPR system by complementary base pairing with target DNA. Since the CRISPR targeting mechanism problem has not yet been fully resolved, it remains a challenge to predict gRNA on-target efficiency. Current gRNA design tools often lack efficient information extraction and cannot learn the target efficiency patterns thoroughly.

Material and methods

In this study, CRISPR-OTE is proposed to consider both multi-dimensional sequence information and important complementary prior knowledge based on a simple but effective framework. CRISPR-OTE consists of the local-contextual information branch and the prior knowledge branch. The local-contextual information branch extracts multi-dimensional sequence features from the DNA primary sequence by a parallel framework of Convolutional Neural Networks (CNN) and bidirectional Long Short-Term Memory networks (biLSTM). The prior knowledge branch selects the optimal subset of physicochemical features to provide the neural network with complementary knowledge, such as complex secondary structures. A simple feature fusion strategy is also adopted to fully utilize multi-modal data from the two branches.

Results

The experimental results show that the optimal subset of physicochemical features (RNA secondary structure and melting temperature of 34nt target) can effectively improve the prediction performance. Additionally, combining multi-dimensional sequence features and multi-modal features can extract information more comprehensively. Through transfer learning, CRISPR-OTE trained on the CRISPR-Cpf1 system can also be successfully applied to the CRISPR-Cas9 system.

Conclusion

The performance of CRISPR-OTE is superior to other methods in different CRISPR systems and species. Therefore, CRISPR-OTE is a simple on-target efficiency prediction framework with better accuracy and generalization performance.

Inner ear disorders' treatment remains challenging due to anatomical barriers. Robotic assistance seems to be a promising approach to enhance inner ear treatments and, more particularly, lead to effective targeted drug delivery into the human cochlea. In this paper we present a combination of a micro-macro system that was designed and realized in order to efficiently control the navigation of magnetic nanoparticles in an open-loop scheme throughout the cochlea, considering that the magnetic particles cannot be located in real time.

In order to respect the anatomical constraints, we established the characteristics that the new platform must present then proceeded to the design of the latter. The developed system is composed of a magnetic actuator that aims to guide nanoparticles into the cochlea. Mounted on a robotic manipulator, it ensures its positioning around the patient's head. The magnetic device integrates four parallelepiped-rectangle permanent magnets. Their arrangement in space, position and orientation, allows the creation of an area of convergence of magnetic forces where nanoparticles can be pushed/pulled to. To ensure the reachability of the desired orientations and positions, a 3 DOF robot based on a Remote Centre of Motion (RCM) mechanism was developed. It features three concurrent rotational joints that generate a spherical workspace around the head. The control of the latter is based on kinematic models.

A prototype of this platform was realized to validate the actuation process. Both magnetic actuator and robotic manipulator were realized using an additive manufacturing approach. We also designed a virtual human head with a life-size cochlea inside. A laser was mounted on the end effector to track the positioning of the actuator. This permitted to experimentally prove the capacity of the robotic system to reach the desired positions and orientations in accordance with the medical needs.

This promising robotic approach, makes it possible to overcome anatomical barriers and steer magnetic nanoparticles to a targeted location in the inner ear and, more precisely, inside the cochlea.

Objective

Prediction of human skull shape from head is a complex and challenging engineering task for the development of a computer-aided vision system. Skull-to-face generation has been commonly performed in forensic facial reconstruction. Classical statistical approaches were usually used. However, the head-to-skull relationship is still misunderstood. Recently, novel deep learning (DL) models have showed their efficiency and robustness for a large range of applications. The present study aimed to develop a novel approach based on deep learning models to reconstruct the human skull shape from head.

Material and methods

A head-to-skull generation workflow was developed and evaluated. A database of computed tomography (CT) images of 209 subjects was established for training and testing purposes. Three-dimension (3-D) head and skull geometries were reconstructed and then their respective descriptors (head/skull volumes, sampling feature points and point-to-center distances, head-skull thickness, Gaussian curvatures) were extracted. Two deep learning models (regression neural network and long-short term memory (LSTM)) were implemented and evaluated with different learning configurations. A 10-fold cross-validation was performed. Finally, the best and worst predicted cases were analyzed and discussed.

Results

The mean errors from 10-fold cross-validation showed a better accuracy level for the regression neural network model according to the long short-term memory model. The mean error between the DL-predicted skull shapes and CT-based skull shapes ranges from 1.67 mm to 3.99 mm by using the regression deep learning model and the best learning configuration. The volume deviation between predicted skull shapes and CT-based skull shapes is smaller than 5%.

Conclusions

The present study suggested that regression deep learning model allows human skull to be predicted from a given head with a good level of accuracy. This opens new avenues for the rapid generation of human skull shape from visual sensors (e.g. Microsoft Kinect) toward a computer-aided vision system for facial mimic rehabilitation. As perspectives, muscle network will be incorporated into the present workflow. Then, facial mimic movements will be tracked and animated to evaluate and optimize the rehabilitation movements and exercises.

Objectives

When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.

Material and methods

The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.

Results

In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.

Conclusion

The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.

Objective

Over the years, microwave techniques have demonstrated their ability to characterise biological tissues. This study aimed to employ this approach to investigate the changes in the finger's glucose levels and to develop a sensitive sensor that people with diabetes can use.

Materials and methods

A simplified four-layer tissue model of the human fingertip was developed to validate the sensor's ability to detect variations in glucose levels. 3D electromagnetic simulations of the sensor with human fingertips inserted in the sensing region while varying the pressure were performed and compared to obtained experimental results using a VNA (vector network analyser).

Results

When varying the finger layers thicknesses independently, it was observed that the change in the skin layer thickness influences the frequency the most. It was also noticed that the higher the finger pressure, the more the resonance shifted towards low frequencies with a decreasing magnitude.

Conclusion

The achieved results show the impact of the finger's pressure on the sensor. Further investigations are in progress to obtain a good reproducibility of experimental results using a best-fitted pressure protocol on diabetic subjects.