Maria Haritou

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

Objective This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. Study Design A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted. Results The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material. Conclusions Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

Wireless patient monitoring for the e-inclusion of chronic patients and elderly people

e-Health has clearly started to become an important issue for implementation, operational deployment of services and a promising market for industry. The need for concentration of the information society technologies on the future so-called convergence generation has been specifically noted since FP6. The scope of this paper is to present an ambient, home based health and wellness measurement and monitoring architecture, especially targeting the elderly and chronic patients, aiming to facilitate their social inclusion (e-inclusion) by providing the means of easy follow-up from their home environment. The proposed paper presents a one-button functional, wireless monitoring system capable of acquiring 3 leads of ECG, pulse oxymetry and temperature measurements, and transmitting them over ZigBee to a computing device, which in turn is responsible for the transmission of the signal to a consultation unit. The authors envisage the development of a lightweight unobtrusive, belt-like wearable device that would enable patients to be monitored daily and at the same time allow them to perform their regular daily activities.

Personalised management of women with cervical abnormalities using a clinical decision support scoring system

OBJECTIVES To develop a clinical decision support scoring system (DSSS) based on artificial neural networks (ANN) for personalised management of women with cervical abnormalities. METHODS We recruited women with cervical abnormalities and healthy controls that attended for opportunistic screening between 2006 and 2014 in 3 University Hospitals. We prospectively collected detailed patient characteristics, the colposcopic impression and performed a series of biomarkers using a liquid-based cytology sample. These included HPV DNA typing, E6&E7 mRNA by NASBA or flow cytometry and p16INK4a immunostaining. We used ANNs to combine the cytology and biomarker results and develop a clinical DSSS with the aim to improve the diagnostic accuracy of tests and quantify the individuals risk for different histological diagnoses. We used histology as the gold standard. RESULTS We analysed data from 2267 women that had complete or partial dataset of clinical and molecular data during their initial or followup visits (N=3565). Accuracy parameters (sensitivity, specificity, positive and negative predictive values) were assessed for the cytological result and/or HPV status and for the DSSS. The ANN predicted with higher accuracy the chances of high-grade (CIN2+), low grade (HPV/CIN1) and normal histology than cytology with or without HPV test. The sensitivity for prediction of CIN2 or worse was 93.0%, specificity 99.2% with high positive (93.3%) and negative (99.2%) predictive values. CONCLUSIONS The DSSS based on an ANN of multilayer perceptron (MLP) type, can predict with the highest accuracy the histological diagnosis in women with abnormalities at cytology when compared with the use of tests alone. A user-friendly software based on this technology could be used to guide clinician decision making towards a more personalised care.

ALADDIN, A Technology pLatform for the Assisted Living of Dementia elDerly INdividuals and Their Carers

Alzheimers disease, the most common form of cortical dementia, is a degenerative brain disease for which there is no known cure but only a symptomatic therapy. Experts estimate that 26.6 million people worldwide had Alzheimer in 2006, which would multiply by four by 2050. The scope of the present paper is to present ALADDIN, *** technology pLatform for the Assisted living of Dementia elDerly INdividuals and their carers, which aims at supporting maintaining health and functional capability, providing the means for the self-care and the self-management of chronic conditions, providing added value to the individual, leveraging his/her quality of life, while at the same time supporting the moral and mental upgrade of both the patients and their carers, as well as enhancing the home-as-care environment through the provision of tools for frequent, unobtrusive monitoring, via the development of user-friendly ICT tools.

CxCaDSS: A Web-Based Clinical Decision Support System for Cervical Cancer

Data from countries with well-organized screening programs and cancer registries indicate that the vast majority of participants who developed cervical cancer could be explained as underestimation of cases that had at least one abnormal Pap test. Nowadays, there are ancillary molecular biology techniques available that provide important information related to cervical cancer and the HPV natural history, including DNA micro-arrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. However, each one of these techniques has its own performance, advantages and limitations, thus a combinatorial approach via artificial intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a novel web-based clinical decision support system and its integration with underlying artificial neural networks, for the combination of the results of classic and ancillary techniques in order to increase the accuracy of diagnosis and thus identify women at true risk of developing cervical cancer. The presented system follows the MVC approach enabling it to easily adapt to any underlying data and structure to support clinical decisions for other domains as well.

Enabling risk assessment and analysis by event detection in dementia patients using a reconfigurable rule set

Chronic mental illnesses pose a great burden on the lives of citizens worldwide. In modern health-care, decentralization and enabling the self management of patients at home are crucial factors in improving the every-day lives of patients and the people close to them. People in general tend to dislike obtrusive monitoring on their daily activities, so how can we implement a platform that can provide clinicians with adequate and concise information on their patients health status and at the same time be unobtrusive and easy to use. Moreover, how can we make such an unobtrusive system capable of providing the doctor with high-impact warnings on the patients health status only when it is needed, thus relieving him of unnecessary workload? In this paper, the authors present a reconfigurable Event Detection mechanism used in the ALADDIN platform for Risk Assessment and Analysis.

HPVGuard: A software platform to support management and prognosis of cervical cancer

Cervical cancer (CxCa) is one of the commonest reasons of womens mortality, although it can be prevented and treated if diagnosed early. Key to this is the regular examination with the test Papanikolaou but nowadays also with ancillary molecular biology tests. In this paper the authors present aspects of the architecture, design and implementation of the HPVGuard information system, a software platform capable to store and handle a multitude of medical examination data along with non-medical information. HPVGuard integrates artificial intelligence models that combine data from different medical examinations and produce an estimation of womens risk to develop CxCa. The application of HPVGuard proved that computerized systems supporting women control can be of extreme value. This is nowadays feasible via the use of inexpensive tools and can be made available to the end users as a web service on standard computers as well as on a variety of mobile terminals.

A Context-Aware Social Networking Platform Built Around the Needs of Elderly Users: The Go-myLife Experience

In our increasingly dislocated and mobile society, online social network sites are proving valuable in bridging distances and facilitating interaction and communication. People are spending a significant amount of time at the top social networking websites in order to manage existing relationships with friends, reconnect with old friends, share media and find new contacts that have similar interests. Fulfilling these needs are just as important for elderly people as it is for everyone else, but can become more difficult. In spite of the need for social contact, elderly people, even those who use the internet, tend to miss out on the benefits of online social networking platforms. Many are no longer at work so they do not have a daily set of activities with the same group of people. This, combined with increasing frailty, can lead to a habit of staying at home, which adds to the feeling of loneliness and isolation. In the same time, elderly people are keen to maintain contact with the different generations of their family and many of them have already invested significant time in building contact lists and relationships within the major social networks. The challenge, therefore, is not setting up a new social network dedicated to older people but rather a platform from where they can post messages, receive updates and take part in discussions across a variety of platforms, thus bringing existing communities together. In this Chapter, the authors present such a context-aware social networking platform, the “Going online: my social life” platform, which is adapted to the needs of elderly users.

Combined texture features for improved classification of suspicious areas in autofluorescence bronchoscopy

Autofluorescence bronchoscopy (AFB) has been utilized over the past decade, proving to be a powerful tool for the detection and localization of premalignant and malignant lesions of the airways. AFB is, however, characterized by low specificity and a high rate of false positive findings (FPFs). The majority of FPFs are due to inflammations, as they often fluoresce at the same wavelengths with cancer. According to several clinical trials, the percentage of the FPFs is about 30%. In this paper we present an intelligent computing system based on combined texture features, feature selection methods and classification models, for improved classification of suspicious areas of the bronchial mucosa, in order to decrease the rate of FPFs, to increase the specificity and sensitivity of AFB and enhance the overall diagnostic value of the AFB method.