Abstract: Background and Aims: Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level.
Subjects and Methods: Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L).
Results: The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively).
Conclusions: Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range.
Abstract: A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are i) simultaneous adjustment of both the insulin basal rate and the bolus dose, ii) initialization based on clinical procedures, and iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycaemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of +/-25% in the estimation of carbohydrates.
The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children.
The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycaemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.
Abstract: Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented. Methods: We compared an autoregressive (AR) model using only glucose information, an AR model with external insulin input (ARX), and an artificial neural network (ANN) using both glucose and insulin information. Online adaptive models were used to account for the intra- and inter-subject variability of the population with diabetes. The evaluation of the predictive ability included prediction horizons (PHs) of 30 min and 45 min. Results: The AR model presented root mean square error (RMSE) values of 14.0-21.6 mg/dL and correlation coefficients (CCs) of 0.92-0.95 for PH=30 min and 23.2-35.9 mg/dL and 0.79-0.87, respectively, for PH=45 min. The respective values for the ARX models were slightly better (PH=30 min, 13.3-18.8 mg/dL and 0.94-0.96; PH=45 min, 22.8-29.4 mg/dL and 0.83-0.88). For the ANN, the RMSE values ranged from 2.8 to 6.3 mg/dL, and the CC was 0.99 for all cases and PHs. The sensitivity of hypoglycemia prediction was 78% for AR, 81% for ARX, and 96% for ANN for PH=30 min and 65%, 67%, and 95%, respectively, for PH=45 min. The corresponding specificities were 96%, 96%, and 99% for PH=30 min and 93%, 93%, and 99% for PH=45 min. Conclusions: The ANN appears to be more appropriate for the prediction of glucose profile based on glucose and insulin data.
Abstract: The present paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a Nonlinear Model Predictive Controller (NMPC) which uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patients information regarding meal intake, glucose measurements and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVA T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, inter-patient variability, intraday variation in physiological parameters and errors in meal amount estimations.
Abstract: BACKGROUND: Through this paper, we present the initial steps for the creation of an integrated platform for the provision of a series of eHealth tools and services to both citizens and travelers in isolated areas of thesoutheast Mediterranean, and on board ships travelling across it. The platform was created through an INTERREG IIIB ARCHIMED project called INTERMED.
METHODS: The support of primary healthcare, home care and the continuous education of physicians are the three major issues that the proposed platform is trying to facilitate. The proposed system is based on state-of-the-art telemedicine systems and is able to provide the following healthcare services: i) Telecollaboration and teleconsultation services between remotely located healthcare providers, ii) telemedicine services in emergencies, iii) home telecare services for "at risk" citizens such as the elderly and patients with chronic diseases, and iv) eLearning services for the continuous training through seminars of both healthcare personnel (physicians, nurses etc) and persons supporting "at risk" citizens. These systems support data transmission over simple phone lines, internet connections, integrated services digital network / digital subscriber lines, satellite links, mobile networks (GPRS / 3G), and wireless local area networks. The data corresponds, among others, to voice, vital biosignals, still medical images, video, and data used by eLearning applications. The proposed platform comprises several systems, each supporting different services. These were integrated using a common data storage and exchange scheme in order to achieve system interoperability in terms of software, language and national characteristics.
RESULTS: The platform has been installed and evaluated in different rural and urban sites in Greece, Cyprus and Italy. The evaluation was mainly related to technical issues and user satisfaction. The selected sites are, among others, rural health centers, ambulances, homes of "at-risk" citizens, and a ferry.
CONCLUSIONS: The results proved the functionality and utilization of the platform in various rural places in Greece, Cyprus and Italy. However, further actions are needed to enable the local healthcare systems and the different population groups to be familiarized with, and use in their everyday lives, mature technological solutions for the provision of healthcare services.
Abstract: Background: Acute non-specific abdominal pain (NSAP) is prevalent in 6 to 25% of the general population and is a common cause of admission to the emergency department (ED). Despite involvement of substantial financial and human resources, there is little data on long-term outcome after initial diagnosis.
Aim: To evaluate long-term outcome of patients initially admitted with NSAP to an ED
Methods: The study involves a five-year follow-up analysis of prospectively collected data on 104 patients admitted to our ED in 2003 with NSAP. Primary endpoint was clinical outcome five years after initial ED admission. Predictive risk factors were assessed using a multivariate regression model.
Results: 29 patients (28%) had recurring NSAP 5 years after initial ED admission, 76% of these patients received (multiple) diagnostic examinations and 13% eventually required diagnostic (or therapeutic) surgery. Although approximately half of patients with recurring NSAP eventually received a definite diagnosis, 30% still suffered from recurrent abdominal pain. Using regression analysis, no single factor in our dataset could be identified as a predictor for NSAP persistence.
Conclusion: The long-term impact for patients initially admitted to our ED with acute NSAP is significant - 28% of patients continue to suffer from recurring NSAP after 5 years. NSAP therefore remains, despite more advanced diagnostic tools, a true and, as yet, unsolved problem.
Abstract: SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.
Abstract: Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food;drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.
Abstract: Background: Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI < 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. Results: PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. Conclusions: The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.
Abstract: In this paper, a modular system for medical image archiving, management, diagnosis support, and telematic cooperation is presented. The system provides digital imaging and communications in medicine (DICOM)-compatible tools for digital image processing and database management of medical images. The software features algorithms for preprocessing, manual or semi-automatic segmentation, automatic calculation of geometrical/size characteristics, and 3-D visualization of organs or selected regions of interest. Additionally, the system incorporates a database where patient data and information can be stored and retrieved. Access to the database is only permitted to authorized users. The user-friendly interface makes the software handy and accessible to clinicians, whereas the telematic components allow collaboration with remote experts. The pilot system incorporates a computer-aided diagnosis module aiming at providing support in the diagnosis of focal liver lesions from computed tomography images.
Abstract: Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke. (E-mail: knikita@cc.ece.ntua.gr) (c) 2006 World Federation for Ultrasound in Medicine & Biology.
Abstract: Objectives: The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Materials and methods: Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. Results: The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. Conclusions: The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images. (c) 2007 Elsevier B.V. All rights reserved.
Abstract: Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atherotnatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84%, were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis. (c) 2006 Elsevier B.V. All rights reserved.
Abstract: This paper presents a telematic system for providing advice and assisting in the management and reporting of patients with Type 1 diabetes. The system is comprised of four distinct entities: devices for measuring blood glucose levels; an insulin advisory system; a mobile phone application and a diabetes patient management and reporting system. The system integrates wireless personal area networks with the use of mobile and Internet technologies and commercially available measurement devices, along with online analytical processing techniques and reporting tools. In addition to its clinical applications, the proposed system can be used by healthcare managers to evaluate healthcare provision, and for demographic analysis to estimate diabetes management costs.
Abstract: In this paper, the implementation of a pilot computerized system for the classification of landscape images (SCAPEVIEWER) is presented. A total of 108 landscape photographs have been organized, according to the mean estimation of scenic beauty from seven experts, into three classes: indistinctive (C1), typical or common (C2), and distinctive (C3). For each of the landscape photographs, 10 indices are estimated. These indices are then fed to a classifier based on neural network (NN) technology. In order to examine whether NNs are suitable for this specific application, two different approaches have been tested and compared against a linear discrimination method (LDM) classifier. The first approach is a feed forward NN (Classic-NN), while the second approach (Hybrid-NN) is based on the Classic-NN modified by using genetic algorithms (GAs). The correct classification performances achieved by the Classic-NN and the Hybrid-NN were 87% and 84%, respectively, while the classification performance of the LDM classifier was only 68%. Although the Classic-NN achieved slightly better results than the Hybrid-NN, the latter is preferred due to its ability of index selection and automatical adjustment of internal NN parameters. The pilot system has shown the feasibility for classifying landscape photographs according to scenic beauty by means of a computerized system combining the knowledge of an expert with a NN classifier.
Abstract: This paper presents an alternative dynamic channel assignment technique for use in satellite-aided cellular systems. It is based on a three-layer cellular architecture. The use of the proposed technique optimizes the QoS of ultra high-speed (UHSMT) and high-speed moving terminals (HSMT). The lower layer of the proposed architecture absorbs the traffic loads of low speed moving terminals (LSMT). The second layer absorbs the traffic load of the HSMT and the higher layer based on a satellite cell absorbs the traffic load of UHSMT. The results of simulating the proposed technique show that assigning the optimum number of channels in every layer, the QoS of UHSMT and HSMT are optimized especially in high traffic conditions, having a small negative effect on the QoS of LSMT
Abstract: In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system, consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.
Abstract: Background: A decision support system based on a neural network approach is proposed to advise on insulin regime and dose adjustment for type 1 diabetes patients. Method: The system consists of two feed-forward neural networks, trained with the back-propagation algorithm with momentum and adaptive learning rate. The input to the system consists of patient’s glucose levels, insulin intake, and observed hypoglycemia symptoms during a short time period. The output of the first neural network provides the insulin regime, which is applied as input to the second neural network to estimate the appropriate insulin doses for a short time period. Results: The system’s ability in order to recommend on insulin regime is excellent, while its performance in adjusting the insulin dosages for a specific patient is highly dependent on the data set used during the training procedure. Conclusions: Despite the limitations of computer-based approaches, this study shows that artificial neural networks can assist diabetes patients in insulin adjustment.
Abstract: Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that promise to increase the efficiency of physicians during their clinical practice. This chapter provides a brief overview of the use of computational intelligence methods in the design and development of DSSs aimed at the differential diagnosis of hepatic lesions from Computed Tomography (CT) images. Furthermore, examples of DSSs developed by our research team for supporting the diagnosis of focal liver lesions from non-enhanced CT images are presented.
Abstract: The aim of this article is to describe how NN have been applied for the simulation of glucose—insulin metabolism, and to present two NN based personalized models for children with T1DM. The models, which are able to make short-term glucose predictions, are based on the combined use of MMs and NNs. The models are comparatively assessed using data about glucose levels, insulin intake, and diet during previous time periods, from four children with T1DM.
Abstract: A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its reduced version after feature selection. The second ensemble of classifiers was generated by combining five different type of primary classifiers, two NNs, and three k-nearest neighbor classifiers. The primary classifiers of the second ensemble used identical input vectors, which resulted from the combination of the five texture feature sets, either directly or after proper feature selection. The decision of each ensemble of classifiers was extracted by applying voting schemes.
Abstract: This chapter introduces the basic principles of Clinical Decision Support (CDS) systems. CDS systems aim to codify and strategically manage biomedical knowledge to handle challenges in clinical practice using mathematical modelling tools, medical data processing techniques and Artificial Intelligence (AI) methods. CDS systems cover a wide range of applications, from diagnosis support to modelling the possibility of occurrence of various diseases or the efficiency of alternative therapeutic schemes, using not only individual patient data but also data on risk factors and efficiency of available therapeutic schemes stored in databases. Computer-Aided Diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. Modern Therapeutic Decision Support (TDS) systemsmake use of advanced modelling techniques and available patient data to optimise and individualise patient treatment. CDS systems aim to improve the overall health of the population by improving the quality of healthcare services, as well as by controlling the cost-effectiveness of medical examinations and treatment.
Abstract: In this paper, a novel approach for glucose regulation in individuals with type 1 diabetes mellitus is considered using the principles of adaptive reinforcement learning techniques. More specifically a controller based on Actor-Critic (AC) learning algorithm is used for the estimation of insulin infusion rate in adult patients using continuous glucose monitoring devices and insulin infusion pumps. Generally, AC algorithms are able to solve control problems of nonlinear, dynamic systems. Aim of the paper is to investigate the applicability of the AC learning method to the problem of glucose regulation and the feasibility to be used for an artificial pancreas. The implemented controller has been tested in an in silico environment. Preliminary results have shown that although the algorithm prevents hypoglycemic events, further research is needed in order to reduce the percentage of glucose
concentration values over the acceptable normoglycaemia bound. Furthermore, open issues with the proposed algorithm have been identified, while the next research steps have been defined.
Abstract: The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.
Abstract: Aim of the manuscript is to present an integrated web-based platform which is able to assess a person's risk to develop Cardiovascular Disease (CVD) using the Body Mass Index (BMI) as independent risk factor based on genetic and lifestyle information and in parallel to provide personalized advice in order to reduce this risk. A subject fills out a web available questionnaire regarding his/her lifestyle in terms of nutrition and food habits, while his/her biological material is send for DNA analysis. Data regarding lifestyle and genetic information are sent to a web server, in order to be used for the assessment of the subject to develop high BMI. The assessment is based on an artificial intelligence based system. The result of risk assessment is fed to a remote server where it is integrated with all values corresponding to the answers of the subject to the questionnaire. All values are transferred through the platform in an .xml file. Then, through appropriate mechanism, a report is generated as a document file (a pdf Acrobat file) which includes the result of risk assessment and the corresponding advice on lifestyle habits. Appropriate quality control actions are taken into consideration during the various processes, while access to the platform is permitted only to authenticated personnel. The latter is ensured by an authentication procedure in the user interface of the software and appropriate usernames/passwords.
Abstract: Recent advances in information and communication technologies permitted the design and development of new patient-centric models for the provision of better health care services and enhancement of patient's self-management. This paper presents a prototype mobile phone application which is being designed to improve the self-management of individuals with Type 1 Diabetes Mellitus (T1DM). The developed application using the Microsoft .Net framework runs on 3G mobile phones. The application consists of five major interfaces for the management of: blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity. Furthermore, the user has the following capabilities i) to keep notes, and ii) in case of an emergency to press a button, in order to transmit immediately his/her position to both an emergency call center, and the attendant physician. It has to be noted, that the above mentioned data are stored locally to the mobile phone, and regularly transmitted via the mobile network to a dedicated hospital web-server. Technical evaluation of the prototype indicates that the use of the mobile network makes feasible the self-management of T1DM.
Abstract: Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.
Abstract: Identification of gene-gene and gene-environment interactions that contribute in the onset of a multi-factorial disease supports the prevention of diseases like the Cardiovascular Disease (CVD). Body Mass Index (BMI), a measure of human obesity, is an independent risk factor of CVD. Furthermore, it is known that a subject's BMI is affected both by his/her lifestyle, e.g. nutrition, and genetic profile. Aim of the paper is to predict a subject's onset of obesity using lifestyle and genetic information. The prediction is performed by a computational intelligence based system using a Parameter Decreasing Method (PDM) combined with an Artificial Neural Network (ANN). The system uses an initial set of 63 input variables corresponding to sex, average nutrition intake measurements, and genetic variations to identify the 32 most important ones that affect BMI. The selected variables are the ones to interact with each other towards the complex trait of BMI, which is used as a 2-class output variable (BMI <= 25 vs. BMI>25) in the ANN. The system achieved a mean accuracy of the system evaluated by a 3-cross validation resampling technique equal to 77.89%.
Abstract: Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (A(z)) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total A(z) for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total A(z): 0.802 +/- 0.083) in the discrimination of hepatic tissue.
Abstract: Rapid advances in wireless communications and networking technologies, linked with advances in computing and medical technologies facilitate the development and offering of emerging mobile systems and services in the healthcare sector. The objective of this paper is to provide an overview of the current status and challenges of mobile health systems (m-health) in emergency healthcare systems and services (e-emergency).
Abstract: In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPQ which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the HAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented HAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.
Abstract: This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully - automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visuallizing of patients' data has been implemented to facilitate treatment design by health care experts.
Abstract: In this paper, an integrated system for medical image archiving, management, diagnosis support, and telematic cooperation is presented The system provides DICOM compatible tools for digital image processing and database management of medical images. The software features algorithms for preprocessing, manual or semi-automatic segmentation, automatic calculation of geometrical/size characteristics, and three-dimensional visualization of organs or selected regions of interest. Additionally, the system incorporates a database where patient data and information can be stored and retrieved The access in the database is permitted only to authorized users. The userfriendly interface makes the software handy and accessible to clinicians, while telematic components allow tele-collaboration with other clinics. The pilot application aims at providing support in the diagnosis of focal liver lesions from Computed Tomography images.
Abstract: A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared, The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its reduced version after feature selection. The second ensemble of classifiers was generated by combining five different type of primary classifiers, two NNs, and three k-nearest neighbor classifiers. The primary classifiers of the second ensemble used identical input vectors, which resulted from the combination of the five texture feature sets, either directly or after proper feature selection. The decision of each ensemble of classifiers was extracted by applying voting schemes.
Abstract: In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN
Abstract: Type 1 diabetes mellitus is a chronic disease characterized by blood glucose levels out of normal range due to inability of insulin production. This dysfunction leads to many short- and long-term complications. In this paper, a system for tele-monitoring and tele-management of type 1 diabetes patients is proposed, aiming at reducing the risk of diabetes complications and improving quality of life. The system integrates wireless personal area networks (WPAN), mobile infrastructure, and Internet technology along with commercially available and novel glucose measurement devices, advanced modeling techniques, and tools for the intelligent processing of the available diabetes patients information. The integration of the above technologies enables intensive monitoring of blood glucose levels, treatment optimisation, continuous medical care, and improvement of quality of life for type 1 diabetes patients, without restrictions in everyday life activities
Abstract: In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model alms at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.
Abstract: A computer aided diagnosis (CAD) system for the characterization of hepatic tissue from computed tomography (CT) images is presented. Regions of interest (ROI's) corresponding to four types of hepatic tissue are drawn by an experienced radiologist on abdominal non-enhanced CT images. For each ROI, five sets of texture features are extracted and combined to provide input to the CAD system. If the dimensionality of a feature set is greater than a predefined threshold, appropriate feature selection based on a genetic algorithm (GA) is applied. Classification of the ROI is then carried out using an ensemble of classifiers consisting of two neural network (NN) and three statistical classifiers. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the primary classifiers of the ensemble. A classification performance of the order of 90.63% was finally achieved
Abstract: Objective diagnosis of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The Computer-Aided Diagnostic (CAD) system described in this paper can analyze B-mode ultrasound images of the carotid artery and classify them into Symptomatic . (S) or Asymptomatic (A). Images from 54 S and 54 A plaques were fed to the CAD system, which consists of three modules: the feature extraction module, where texture features are estimated based on Laws' texture energy, the dimensionality reduction module, where the number of features is reduced using ANOVA statistics, and the classifier module with a Neural Network (NN) trained via a novel hybrid method in order to recognize the type of atheromatous plaques. The hybrid training method uses Genetic Algorithms (GA's) to locate a starting point close to the optimal solution, and then the back-propagation (BP) algorithm with adaptive learning rate and momentum to refine the NN configuration with local search. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture, and to optimize the classification performance. The proposed CAD system has achieved a total classification performance of 99%
Abstract: In this paper, a Computer Aided Diagnosis (CAD) system for the characterization of hepatic tissue from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) corresponding to normal liver, cyst, hemangioma, and hepatocellular carcinoma, are drawn by an experienced radiologist on abdominal nonenhanced CT images. For each ROI, five distinct sets of texture features are extracted using the following methods: first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. If the dimensionality of a feature set is greater than a predefined threshold, feature selection based on a Genetic Algorithm (GA) is applied. Classification of the ROI is then carried out by a system of five neural networks (NNs), each using as input one of the above feature sets. The members of the NN system (primary classifiers) are 4-class NNs trained by the backpropagation algorithm with adaptive learning rate and momentum. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the individual NNs. The multiple classification scheme using the five sets of texture features results in significantly enhanced performance, as compared to the classification performance of the individual primary classifiers
Abstract: In this paper a computer-aided diagnostic system for the classification of hepatic lesions from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) taken from non-enhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas (a total of 147 samples), have been used as input to the system. The system consists of two levels: the feature extraction and the classification levels. The feature extraction level calculates the average grey scale and 48 texture characteristics, which are derived from the spatial grey-level co-occurrence matrices, obtained from the ROI's. The classifier level consists of three sequentially placed feed-forward Neural Networks (NN's), which are activated sequentially. The first NN classifies into normal or pathological liver regions. The pathological liver regions are classified by the second NN into cysts or "other disease". The third NN classifies "other disease" into hemangiomas and hepatocellular carcinomas. In order to enhance the performance of the classifier and improve the execution time, the dimensionality of the initial feature vector has been reduced using the sequential forward floating selection method for each individual NN input vector. A total classification rate of 98% has been achieved
Abstract: Describes a decision support system, which is able to predict the insulin regime and dose to be taken by insulin-dependent diabetes mellitus (IDDM) patients for a short time period. The system is implemented using two feedforward neural networks trained with a back-propagation algorithm. The output of the first neural network provides information about the insulin regime that has to be followed by the patient, while the output of the second neural network suggests the type and the dose of insulin to be taken
