The Ultrasound Window Into Vascular Ageing: A Technology Review by the VascAgeNet COST Action

Arteriosclerosis; Ultrasound; Vascular ageingArteriosclerosi; Ecografia; Envelliment vascularArteriosclerosis; Ecografía; Envejecimiento vascularNon-invasive ultrasound (US) imaging enables the assessment of the properties of superficial blood vessels. Various modes can be used for vascular characteristics analysis, ranging from radiofrequency (RF) data, Doppler- and standard B/M-mode imaging, to more recent ultra-high frequency and ultrafast techniques. The aim of the present work was to provide an overview of the current state-of-the-art non-invasive US technologies and corresponding vascular ageing characteristics from a technological perspective. Following an introduction about the basic concepts of the US technique, the characteristics considered in this review are clustered into: 1) vessel wall structure; 2) dynamic elastic properties, and 3) reactive vessel properties. The overview shows that ultrasound is a versatile, non-invasive, and safe imaging technique that can be adopted for obtaining information about function, structure, and reactivity in superficial arteries. The most suitable setting for a specific application must be selected according to spatial and temporal resolution requirements. The usefulness of standardization in the validation process and performance metric adoption emerges. Computer-based techniques should always be preferred to manual measures, as long as the algorithms and learning procedures are transparent and well described, and the performance leads to better results. Identification of a minimal clinically important difference is a crucial point for drawing conclusions regarding robustness of the techniques and for the translation into practice of any biomarker.This article is based upon work from COST Action CA18216 VascAgeNet, supported by COST (European Cooperation in Science and Technology, www.cost.eu). A.G. has received funding from “La Caixa” Foundation (LCF/BQ/PR22/11920008). R.E.C is supported by the National Health and Medical Research Council of Australia (reference: 2009005) and by a National Heart Foundation Future Leader Fellowship (reference: 105636). J.A. acknowledges support from the British Heart Foundation [PG/15/104/31913], the Wellcome EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z], and the Cardiovascular MedTech Co-operative at Guy's and St Thomas' NHS Foundation Trust [MIC-2016-019]

Development of clinical decision support systems for the management of diabetes mellitus

In the present PhD thesis, the study and development of decision support systems for the prevention, diagnosis, and treatment of diabetes has been conducted.In the first part of the thesis, a comparative assessment of different machine learning and statistical methodologies towards the development of risk prediction models for the incidence and the evolution of Type 2 Diabetes Mellitus has been orchestrated. The use of ensembles of classifiers, and specifically ensembles of feed forward neural networks, for the prediction of Diabetes Mellitus for Pima Indian women and Cardiovascular Diseases for patients with Type 2 Diabetes Mellitus has been examined. Several classifiers have been developed, others follow the Bagging paradigm, others are ensembles of Feed-forward Neural Networks with different numbers of hidden neurons or layers, others follow the Binary Logistic Regression paradigm, others follow the Bayesian approach, and others are variations of Decision Trees. It has been shown that the ensembles of classifiers that have been trained following the Bagging approach and the ensembles of feed forward neural networks with different numbers of hidden neurons or layers have achieved the highest levels of prediction accuracy and their predictions are closer to the real risk scores, as indicated by the results of the Hosmer-Lemeshow test. The obtained results justify that ensembles of Artificial Neural Networks can significantly contribute in predicting the incidence of T2DM or its complications by having the capacity to handle the unbalanced nature, which usually occurs in medical datasets, and furthermore to capture an individual’s health evolution.In the second part of this thesis, feature selection is conducted in order to find the most critical clinical features which are strongly related with the incidence of fatal and non fatal Cardiovascular Disease in patients with Type 2 Diabetes Mellitus. The proposed system is based on the use of a Genetic Algorithm with a fitness function that depends on the classification sensitivity and accuracy of a Dual Weighted K-Nearest Neighbours classifier. The best subsets of features proposed by the implemented algorithm include the most common risk factors, such as age at diagnosis, duration of diagnosed diabetes, glycosylated haemoglobin (HbA1c), cholesterol concentration, and smoking habit, but also factors related to the presence of other diabetes complications and the use of antihypertensive and diabetes treatment drugs (i.e. proteinuria, calcium antagonists, b-blockers, diguanides and insulin).In the third part of this thesis, a food recognition system is proposed, which consists of two modules performing feature extraction and classification of food images, for the automatic assessment of carbohydrates (CHO) in the meals of diabetic patients. In an automatic food recognition system, the user first takes a photograph of the upcoming meal with the camera of his mobile phone. Then, the image is processed so that the different types of food are divided from each other and segmented in different areas of the image. A series of features are extracted from each segmented area and are fed to a classifier, which decides what kind of food is represented by each segmented area. Then, the volume of each segmented area is calculated and the total CHO of the depicted meal are estimated. The combination of Speeded Up Robust Features (SURF), Color and Local Binary Pattern (LBP) features is examined in this thesis, since SURF ensures that spatial intensity patterns are captured, and Color and LBP features ensure stability and distinctiveness. Moreover, a novel modified version of the All-And-One (M-A&O) SVM classifier for multiclass classification problems is proposed and its performance is assessed against classification methods based on SVM or the K-Nearest Neighbour approaches including the One-Against-All (OAA) SVM, the One-Against-One (OAO) SVM, the All-And-One (A&O) SVM, the Weighted K-Nearest Neighbour (WKNN) classifier, the Dual Weighted K-Nearest Neighbour (DWKNN) classifier, and the K-Nearest Neighbour Equality (KNNE) classifier. The results show the importance of color features in discriminating different food classes and the superiority of the M-A&O SVM classifier in terms of classification accuracy.Στην παρούσα διατριβή εξετάζεται η ανάπτυξη υπολογιστικών συστημάτων υποστήριξης ιατρικών αποφάσεων για την πρόληψη, διάγνωση και θεραπεία του Σακχαρώδους Διαβήτη (ΣΔ).Στο πρώτο μέρος της διατριβής εξετάζεται η χρήση των συνόλων ταξινομητών, και ιδιαίτερα των συνόλων νευρωνικών δικτύων πρόσθιας τροφοδότησης, για την πρόβλεψη της εμφάνισης του ΣΔ σε γυναίκες της ινδιάνικης φυλής Πίμα και για την πρόβλεψη της εμφάνισης καρδιαγγειακών επιπλοκών σε βάθος πενταετίας σε ασθενείς με ΣΔ Τύπου 2. Οι ταξινομητές που εξετάζονται βασίζονται σε σύνολα νευρωνικών δικτύων εκπαιδευμένα με τη μέθοδο bagging, σύνολα νευρωνικών δικτύων πρόσθιας τροφοδότησης διαφορετικού αριθμού κρυμμένων νευρώνων και επιπέδων, ταξινομητές δυαδικής πανινδρόμησης, παραλλαγές Μπεϋζιανών δικτύων, και παραλλαγές των δένδρων απόφασης. Αποδεικνύεται ότι τα σύνολα νευρωνικών δικτύων που έχουν εκπαιδευτεί με τη μέθοδο bagging και τα σύνολα νευρωνικών δικτύων πρόσθιας τροφοδότησης επιτυγχάνουν τα καλύτερα αποτελέσματα ως προς την ακρίβεια ταξινόμησης και το εμβαδόν κάτω από την καμπύλη Receiver Operating Characteristic (ROC), καθώς και τα καλύτερα αποτελέσματα ως προς τη συμφωνία των εκτιμώμενων πιθανοτήτων εμφάνισης του ΣΔ ή των επιπλοκών του με τις παρατηρούμενες αντίστοιχες πιθανότητες.Στο δεύτερο μέρος της διατριβής παρουσιάζεται η ανάπτυξη συστήματος τεχνητής νοημοσύνης για τον προσδιορισμό παραγόντων που αλληλεπιδρούν και επηρεάζουν τον κίνδυνο ανάπτυξης καρδιαγγειακών νοσημάτων σε ασθενείς με ΣΔ Τύπου 2. Η μεθοδολογία που αναπτύσσεται βασίζεται στην προσέγγιση Περιτυλίγματος (Wrapper) για την επιλογή των πιο σημαντικών χαρακτηριστικών που επηρεάζουν την εμφάνιση καρδιαγγειακού νοσήματος σε ασθενείς με ΣΔ Τύπου 2, και υλοποιείται με ένα Γενετικό Αλγόριθμο με συνάρτηση καταλληλότητας που βασίζεται στην ευαισθησία και την ακρίβεια της ταξινόμησης ενός ταξινομητή Κ-Κοντινότερων Γειτόνων με Διπλά Βάρη. Τα σημαντικότερα χαρακτηριστικά που επιλέχθηκαν περιλαμβάνουν δημογραφικά δεδομένα του ασθενούς, δεδομένα σχετικά με την ύπαρξη άλλων μακροπρόθεσμων επιπλοκών και δεδομένα σχετικά με την αντιδιαβητική αγωγή του ασθενούς.Στο τρίτο μέρος της διατριβής περιγράφεται η ανάπτυξη ενός υποσυστήματος εξαγωγής χαρακτηριστικών και ταξινόμησης εικόνων τροφής για την υποβοήθηση ατόμων με ΣΔ στον υπολογισμό της ποσότητας των υδατανθράκων στο γεύμα τους. Χρησιμοποιούνται φωτογραφίες γευμάτων από κινητό τηλέφωνο, οι οποίες στη συνέχεια υποβάλλονται σε επεξεργασία για την εξαγωγή πληροφορίας σχετικής με την περιεκτικότητα του γεύματος σε υδατάνθρακες. Για την ταξινόμηση των εικόνων τροφής είναι απαραίτητη η εξαγωγή κατάλληλων χαρακτηριστικών, όπως χαρακτηριστικών που μπορούν να εκφράσουν τα χωρικά πρότυπα της έντασης, και χαρακτηριστικών σχετικών με το χρώμα και την υφή των τροφών. Για την ταξινόμηση των εικόνων τροφής ελέγχθηκε και αξιολογήθηκε η χρήση ταξινομητών που ακολουθούν την προσέγγιση του ταξινομητή Κ-Κοντινότερων Γειτόνων και ταξινομητών πολλαπλών κλάσεων που βασίζονται σε Μηχανές Διανυσμάτων Υποστήριξης. Αποδείχθηκε ότι τα χαρακτηριστικά χρώματος συμβάλλουν περισσότερο στη βελτίωση των αποτελεσμάτων της ταξινόμησης, ενώ, ο τροποποιημένος ταξινομητής Όλοι-Και-Ένας με Μηχανές Διανυσμάτων Υποστήριξης επιτυγχάνει τα καλύτερα αποτελέσματα ως προς την ακρίβεια ταξινόμησης

A Multimodal Approach for Real Time Recognition of Engagement towards Adaptive Serious Games for Health

In this article, an unobtrusive and affordable sensor-based multimodal approach for real time recognition of engagement in serious games (SGs) for health is presented. This approach aims to achieve individualization in SGs that promote self-health management. The feasibility of the proposed approach was investigated by designing and implementing an experimental process focusing on real time recognition of engagement. Twenty-six participants were recruited and engaged in sessions with a SG that promotes food and nutrition literacy. Data were collected during play from a heart rate sensor, a smart chair, and in-game metrics. Perceived engagement, as an approximation to the ground truth, was annotated continuously by participants. An additional group of six participants were recruited for smart chair calibration purposes. The analysis was conducted in two directions, firstly investigating associations between identified sitting postures and perceived engagement, and secondly evaluating the predictive capacity of features extracted from the multitude of sources towards the ground truth. The results demonstrate significant associations and predictive capacity from all investigated sources, with a multimodal feature combination displaying superiority over unimodal features. These results advocate for the feasibility of real time recognition of engagement in adaptive serious games for health by using the presented approach

Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based On DCE-MRI

International audienceAlthough Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms , namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93,18 % Accuracy)

The Ultrasound Window Into Vascular Ageing:A Technology Review by the VascAgeNet COST Action

Non-invasive ultrasound (US) imaging enables the assessment of the properties of superficial blood vessels. Various modes can be used for vascular characteristics analysis, ranging from radiofrequency (RF) data, Doppler- and standard B/M-mode imaging, to more recent ultra-high frequency and ultrafast techniques. The aim of the present work was to provide an overview of the current state-of-the-art non-invasive US technologies and corresponding vascular ageing characteristics from a technological perspective. Following an introduction about the basic concepts of the US technique, the characteristics considered in this review are clustered into: 1) vessel wall structure; 2) dynamic elastic properties, and 3) reactive vessel properties. The overview shows that ultrasound is a versatile, non-invasive, and safe imaging technique that can be adopted for obtaining information about function, structure, and reactivity in superficial arteries. The most suitable setting for a specific application must be selected according to spatial and temporal resolution requirements. The usefulness of standardization in the validation process and performance metric adoption emerges. Computer-based techniques should always be preferred to manual measures, as long as the algorithms and learning procedures are transparent and well described, and the performance leads to better results. Identification of a minimal clinically important difference is a crucial point for drawing conclusions regarding robustness of the techniques and for the translation into practice of any biomarker