Irbm最新文献

Background and Objectives

Tumor Treating Fields (TTFields) is an FDA-approved technique used in the treatment of glioblastoma. It consists in applying an electric field (EF) with a frequency of 200 kHz using two pairs of transducer arrays. During treatment planning, the NovoTAL system is used to strategically place the arrays on the head in regions that maximize the EF at the tumor. Current should be injected at least 18 hours/day and induce a minimum EF of 1 V/cm at the tumor. To avoid any thermal harm to the patient, the temperature of the scalp is kept around 39.5 °C by changing the injected current. The goal of this study was to investigate how accounting for the temperature of the scalp during treatment planning might affect the choice of the best layout suggested by NovoTAL. Furthermore, we also studied the sensitivity of the results to the metric used to evaluate the layouts.

Methods

We used a realistic head model with a virtual glioblastoma in our studies. Through the NovoTAL system we obtained five realistic array layouts and we predicted the best one for our model based on the approach currently implemented in this system. We then repeated the same type of analysis, but also accounting for the temperature during planning. In both cases we ranked the layouts based on three different criteria: the LMiPD and the LAPD (local minimum and local average power densities, respectively) in the tumor and the SAR (specific absorption rate) in the head

Results

Accounting for the temperature does not significantly affect the choice of the best layout provided that the arrays are at least 1 cm apart from each other. Otherwise, a common temperature hotspot occurs in the scalp between the closest transducers of the adjacent arrays, which limits how much current can be injected and consequently treatment effectiveness. Also, the choice of the best layout depends on the criterion used.

Conclusions

Accounting for the temperature might led to significant variations in the current injected. The LMiPD might be used as a first criterion to choose the best treatment layout, followed by the LAPD and then the SAR.

Objectives

Pressure ulcers are a great handicap for those who develop one. Pressure ulcers can take a long time to heal especially if detected late. These afflictions require a lot of time from the medical personnel and thus a great amount of money. We aim here to check the impact of continuous measurement on the performance of early pressure ulcer detection algorithms.

Material and methods

To detect pressure ulcers early on we use a simulation of a human buttocks to simulate the reaction of it to pressure. This simulation considers the most recent findings about pressure ulcers. In particular, the phenomenon of muscle stiffening when pressure is applied for a long period of time, and the reperfusion phenomenon. We can then simulate pressure captors on the outside interface of the buttocks to use these measurements for detection. We then determine the best threshold on the measured pressures to create standard algorithms that we compare to novel algorithms using an optimized threshold on a calculated damage based on the pressure measurement of the last 2 hours.

Results

We compare these different algorithms for the early detection of pressure ulcers and show the need to take the measurement variation in time for a better detection. The detection error is improved by 7.3% for balanced classes and 2.7% for a dataset with a majority of healthy buttocks.

Conclusion

We showed that taking the evolution of pressure instead of only instantaneous measurement can improve the early detection of pressure ulcer.

Objective

this paper presents an innovative graphical user interface to visualize the attitude of a sensing device in a three-dimensional space, serving a wide-range of medical applications.

Material and methods

based on inertial measurement units (IMU) or on magnetic, angular rate and gravity (MARG) sensors, a processing unit provides Euler angles using a sensor fusion technique to display the orientation of the device relative to the Earth frame in real-time. The device is schematized by linking six polygonal regions, and is subject to sequential rotations by updating the graph each 350 ms. We conduct comparative studies between the two sensing devices, i.e. IMUs and MARGs, as well as two orientation filters, namely Madgwick's algorithm and Mahony's algorithm.

Results

the accuracy of the system is reported as a function of (i) the sampling frequency, (ii) the sensing unit, and (iii) the orientation filter, following two elderly care applications, namely fall risk assessment and body posture monitoring. The experiments are conducted using public datasets. The corresponding results show that Madgwick's algorithm is best suited for low sampling rates, whereas MARG sensors are best suited for the detection of postural transitions.

Conclusion

this paper addresses the different aspects and discusses the limitations of attitude estimation systems, which are important tools to help clinicians in their diagnosis.

Objective

The most widespread and intrusive cancer type among women is breast cancer. Globally, this type of cancer causes more mortality among women, next to lung cancer. This made the researchers to focus more on developing effective Computer-Aided Detection (CAD) methodologies for the classification of such deadly cancer types. In order to improve the rate of survival and earlier diagnosis, an optimistic research methodology is required in the classification of breast cancer. Consequently, an improved methodology that integrates the principle of deep learning with metaheuristic and classification algorithms is proposed for the severity classification of breast cancer. Hence to enhance the recent findings, an improved CAD methodology is proposed for redressing the healthcare problem.

Material and Methods

The work intends to cast a light-of-research towards classifying the severities present in digital mammogram images. For evaluating the work, the publicly available MIAS, INbreast, and WDBC databases are utilized. The proposed work employs transfer learning for extricating the features. The novelty of the work lies in improving the classification performance of the weighted k-nearest neighbor (wKNN) algorithm using particle swarm optimization (PSO), dragon-fly optimization algorithm (DFOA), and crow-search optimization algorithm (CSOA) as a transformation technique i.e., transforming non-linear input features into minimal linear separable feature vectors.

Results

The results obtained for the proposed work are compared then with the Gaussian Naïve Bayes and linear Support Vector Machine algorithms, where the highest accuracy for classification is attained for the proposed work (CSOA-wKNN) with 84.35% for MIAS, 83.19% for INbreast, and 97.36% for WDBC datasets respectively.

Conclusion

The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.

Objectives

In this paper, we present a plugin for the optimal placement of sensors in a smart home. Our approach includes the Building Information Modeling (BIM) which is a plan that describes the building layout.

Material and methods

This plugin uses the CSTB EveBim viewer for loading IFC file representing the digital building's model. We use then, a mathematical model based on a mixed integer linear program, to determine the optimal sensor placement according to building and sensors characteristics.

Results

The results show the efficiency of the proposed algorithm and the developed plugin. We obtain an optimal solution after few seconds, and we show the sensor placement on the building digital model.

Conclusion

We show the relevance of the proposed plugin to equip room of retirement home or ambient assisted living in order to identify occupant activity for home support application.

Objectives

Because there are many categories, large morphological differences and few labels of urinary sediment components, and uneven data in urine sediment images recognition, the accuracy and recall rate of the existing urine sediment images recognition methods are not ideal. The main purpose of this paper is to improve the accuracy and recall of urine sediment image recognition by proposing a urine sediment image classification method based on semi-supervised learning.

Methods

This paper proposes a method based on semi-supervised learning to classify urine sediment images. This algorithm designs a re-parameterization network (US-RepNet) for low-resolution urine sediment microscopic images to extract complex features of urine sediment images. The dual attention module is introduced on Us-RepNet to increase the extraction of fine-grained features from urine sediment images. And the cross-entropy loss (C.E. loss) function is optimized to train an unbiased classifier to improve long-tailed distribution image classification.

Results

The experimental results show that the accuracy of proposed method can reach 94% with only a small amount of labeled data for 16 types of urine sediment images under long-tail distribution.

Conclusion

The algorithm can recognize most types, and reduces the need for labeled information, while achieving excellent recognition and classification performance. Comprehensive analysis shows that it can be used as a practical reference for urine sediment analysis.

Objective

Sleep is essential for human health. Bad sleep and sleep disorders have been increasingly prevalent and are gradually becoming a social problem that cannot be ignored. The current gold standard in sleep monitoring is polysomnography (PSG) allowing nearly complete approach. Unfortunately, this wealth of information is obtained at the cost of invasive system, only usable in hospital environment under the control of sleep experts. Therefore, the development of a wireless body network for long-term home sleep monitoring is a good way to achieve this in a less-intrusive, portable and autonomous way. In this paper, an overall architecture from the sensors to the user's display is presented with a focus on the main functions and hardware.

Method

The hardware architecture is composed of simple miniaturized wearable devices. Then, we introduce the chosen indicators for sleep monitoring and the algorithms developed for sleep stages classification. Finally we show the evaluation of our approach compared to the PSG. We illustrate the sleep stage classification during one night in the sleep unit of Toulouse University Hospital and highlight correlation between body temperature on extremities and Periodic Limb Movement during Sleep.

Results

Based on the confusion matrix analysis, the results show that the T1 method appears to be effective for the detection of awake and deep sleep in particular. For PLMS detection, we define the detection rules based on the foot movement data. The results show that the total number of PLMS and the number of PLMS distributed in each sleep stage detected by our foot module are both very close to the PSG. Furthermore, we have found correlations between body temperature and hypnogram and between body temperature on extremities and PLMS.

Conclusion

A wearable sensor system could be an alternative to PSG for long-term monitoring. Validation of the two proposed threshold-based algorithmic methods for sleep stage classification compared to the PSG gold standard shows good agreement, while the k-means based approach shows poor agreement with PSG. Furthermore, this method could be a good candidate for predicting periodic leg movements in sleep.

Objectives: the present manuscript introduces a health monitoring ecosystem called MonEco, to monitor and predict respiratory and cardiovascular disorders.

Material and methods: the system comprehends a tablet application called eCardio and two smart devices named CareUp and UpNEA. eCardio is an application available for iOS devices that predicts cardiovascular risk based on user' data and habits. CareUp is a smartwatch for blood pressure estimation and fitness tracking. UpNEA is a smart glove for sleep monitoring, detecting sleep disruptive breathing events.

Results: MonEco smart devices embed novel algorithms and top-notch home health care monitoring technologies. The user can access data collected via a web application hosted by a remote server (AeneA), allowing clinicians to follow up on a patient's health.

Conclusion: MonEco wants to inspire and disclose the architecture of a connected health monitoring ecosystem.

This review describes the use of ultrasound as a treatment modality for cardiovascular diseases. Ultrasound is widely used for diagnosis in cardiovascular pathology. However, it is not much used for therapeutic purposes. Therapeutic ultrasound includes thermal and mechanical effects. Therapeutic applications already available or still under research in venous, arterial, and cardiac diseases will be described.