Cardiovascular disease stratification based on ultrasound images of the carotid artery

Cardiovascular disease (CVD) can be identified through ultrasound scans of the arteries and more specific the common carotid artery (CCA). Measurement of the intima–media thickness (IMT) of the CCA is an established indicator of CVD. Several reports have indicated differences in the IMT of CCA and related then with various risk factors as well as their association with the risk of stroke. Along this direction; this chapter presents methods for the stratification of CVD based on manual and automated IMT measurements for both the left and right common carotid arteries. The results are based on a group of 1104 longitudinal ultrasound images acquired from 568 men and 536 women out of which 125 had cardiovascular symptoms (CVD). The main findings can be summarized as follows: (1) there was no significant difference between the CCA left side IMT and the right side IMT; (2) there were statistical significant differences for the IMT measurements between the normal group and the CVD group for both the left and the right sides; (3) there was an increasing linear relationship of the left and right IMT measurements with age for the normal group

Breast Cancer Brain Metastasis: The Potential Role of MRI Beyond Current Clinical Applications

Breast cancer brain metastasis (BCBM) represents a major clinical challenge. Can MRI help in advancements in the management of BCBM? This review discusses MRI developments and the corresponding potential advancements in BCBM management

An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images

An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images, vol. 652 IFIP, pp. 161 - 173In this study, we propose an automated system for the segmentation of cancer brain metastases (CBM) using MRI images. The goal is the correlation with regards to the primary cancer site. The segmentation of CBM is a challenging task due to their wide range in terms of number, shape, size and location in the brain. We experimented with the training of a modified U-Net convolutional neural network (CNN) using N = 3474 brain image slices for training, Nv = 579 for validation and NT = 579 for testing from the public dataset BrainMetShare. The proposed model was evaluated on the testing data (NT), on a lesion-cross section basis with areas from 2.8 to 1225.7 mm2 and yielded a mean Sensitivity (SE) 0.70 ± 0.30, Specificity (SP) 0.77 ± 0.26 and Dice similarity coefficient (DSC) of 0.73 ± 0.29 across the entire dataset. The present results show the good agreement of the proposed method with the ground truth

A Review on Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics

Breast cancer brain metastasis (BCBM) still remains a major clinical challenge. Current systemic treatments are often inadequate while diagnosis involves time-consuming series of neuro-imaging acquisitions and dangerous invasive biopsies. Automated image analysis systems for the identification, prediction and follow up of BCBM are therefore required. This review discusses the advancements in the automated MRI brain metastasis (BM) image analysis using radiomic features based classification. Seven BM segmentation studies, and three BCBM identification studies were considered eligible. The latter studies were based on either manual or semi-automated segmentation methods. Almost every fully automated BM segmentation method presented in the literature, reported a maximum dice similarity score (DSC) of 84%, but they resulted in a poor BM segmentation for brain areas less than 5 mm (0.06 ml). The multi-class prediction of BCBM approach, which is more representative for clinical applicability, is based on imaging features and resulted in an area under the curve (AUC) of 60%. Therefore, the need still exists for the development of automated image analysis methods for the identification, follow up and prediction of BCBM. The potential clinical usage of above methods entails further multi-center studies with comprehensive clinical data and multi-class modeling with vast and varying primary and metastatic brain tumors

EEmergency System to Support Emergency call Evaluation and Ambulance dispatch Procedures

The main purpose of this study was to create an electronic system (eEmergency system) in order to support, improve and help the procedure of handling emergency calls. An effort to reform the procedures followed for emergency call handling and Ambulance dispatch started on the Island of Cyprus since 2016; along that direction, a central call center was created. The present electronic system was designed for this call center. The main features are the support for ambulance fleet handling, the support for emergency call evaluation and triage procedure and the improvement of communication between the call center and the ambulance vehicles. The main components and the design of this system are outlined in this paper. The part of incident evaluation and ambulance handling, has been in daily practice for more than one year and since then more than 62000 calls were successfully handled and recorded with the use of this system. This system was successfully used from the beginning of the pandemic period of Covid-19

“Meleti” Speech and Language Development Support System

Through this study we are presenting a system that intents to support and monitor speech and language development of children with hearing impairment using hearing aids and/or cochlear implants, or children with language delays. The scope is to support children during their daily life. The system is mainly based on a set of applications for Android devices. These applications can be used anywhere the child and the parents are and they include several tasks presented to the child as a game. The main goal is to support sessions being done by the caregivers like reproducing words, sounds, small phrases etc. The system was created based on the four levels targeted during speech and language support sessions (auditory skills, receptive language, expressive language, speech / articulation). The results from system usage are being recorded from a server where specialists can monitor get results and act accordingly in order to improve the child’s performance. Initial design and development steps have been completed. The two first levels of the system have been tested on a small group of user with very encouraging results. Furthermore the development of several other modules related to the levels of language development will continue in order to cover all language development levels

Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%

Health and rescue services management system during a crisis event

Τhe performance of rescuers and personnel handling major emergencies or crisis events can be significantly improved through continuous training and through technology support. The work done in order to create a system has been discussed which can support both resources and victims during a crisis or major emergency event. More specifically, the system supports real-time management of firefighter teams, rescue teams, health services, and victims during a major disaster. It can be deployed in an ad hoc manner in the disaster area, as a stand-alone infrastructure (using its own telecommunications and power). It mainly consists of a control station, which is installed in the area command centre, the firefighters units, the rescuers units, the ambulance vehicles units, and the telemedicine units that can be used in order to support victim handling at the casualties clearing station. The system has been tested and improved through continuous communication with experts and through professional exercises; the results and conclusions are presented