Enhancing Healthcare Through Telehealth Ecosystems: Impacts and Prospects.

This poster presents a comprehensive assessment of the transformative potential of telehealth ecosystems, integrating Internet of Things (IoT), Internet of Medical Things (IoMT), and Artificial Intelligence (AI) technologies. The study explores their impact on healthcare delivery and markets, emphasising the need for robust cybersecurity measures and technological integration. By facilitating continuous monitoring, personalised interventions, and improved patient outcomes, the integration of advanced technologies in telehealth ecosystems has the potential to revolutionise healthcare delivery and reduce healthcare costs. However, successful implementation and maximisation of their benefits require collaborative research and adherence to ethical and regulatory standards.

An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient's age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.

Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data.

Tinnitus is a highly prevalent condition, affecting more than 1 in 7 adults in the EU and causing negative effects on sufferers' quality of life. In this study, we utilised data collected within the "UNITI" project, the largest EU tinnitus-related research programme. Initially, we extracted characteristics from both auditory brainstem response (ABR) and auditory middle latency response (AMLR) signals, which were derived from tinnitus patients. We then combined these features with the patients' clinical data, and integrated them to build machine learning models for the classification of individuals and their ears according to their level of tinnitus-related distress. Several models were developed and tested on different datasets to determine the most relevant features and achieve high performances. Specifically, seven widely used classifiers were utilised on all generated datasets: random forest (RF), linear, radial, and polynomial support vector machines (SVM), naive bayes (NB), neural networks (NN), and linear discriminant analysis (LDA). Results showed that features extracted from the wavelet-scattering transformed AMLR signals were the most informative data. In combination with the 15 LASSO-selected clinical features, the SVM classifier achieved optimal performance with an AUC value, sensitivity, and specificity of 92.53%, 84.84%, and 83.04%, respectively, indicating high discrimination performance between the two groups.

Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation.

Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools' detection and annotation results, regarding the waves of interest, were then compared to the clinicians' manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals.

A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants' reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

Automatic Absence Seizures Detection in EEG signals: An Unsupervised Module.

Absence seizures are expressed with distinctive spike-and-wave complexes in the electroencephalogram (EEG), which can be used to automatically distinguish them from other types of seizures and interictal activity. Considering the chaotic nature of the EEG signal, it is very unlikely that such continuous, repetitive patterns with strict periodic behavior would occur naturally under normal conditions. Searching for spectral activity in the range of 2.5-4.5 Hz and assessing the presence of synchronous, repeated patterns across multiple EEG channels in an unsupervised manner, the proposed methodology provides high absence seizure detection sensitivity of 93.94% with a low false detection rate of 0.168 FD/h using the open TUSZ dataset.

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.

Simulation of a Cellular Automaton with Markov Chains: Applications in Self-Organized Dynamical Systems.

over the past years, technology has allowed information technology to contemplate complex events as well as complex semantic features to predict what types of "thoughts" are being conceptualized. The introduction of the neuro-robotics field allows a mix of different disciplines to inter-collate and produce actual results that could be considered outputs of a science-fiction novel 20 twenty years ago. In the present work, we attempted to present an example of how an automaton can move in an environment with obstacles, by regulating its behavior so as to allow a decision based on rewards and penalties. Examples of the robotic behavior, running on a virtual environment are presented, along with a discussion of its different possibilities expressed as a penalty function for the behavior of the robot.

The "Prisoner's Dilemma" in the Tumor Biology Context: The Warburg Effect, Proliferation Dynamics and Equilibria.

There are numerous theories concerning carcinogenesis. Starting from the Warburg effect, which was one of the first theories concerning the mitochondrial dysfunction in tumor cells. Further on, the "two-hit" theory, where tumors were considered to be the outcome of genetic aberrations or mutations and more specifically of a certain number of "hits" each one resulting in a mutation. One of the main physical problems of biological systems is proliferation. Proliferation brings forwards two main questions: First, under a given population of cells, at time t what will be the precise population at time t+24h (or any other time point)? Second, what are the metabolic strategies followed by tumor cells in order to facilitate for their growth? In the present work we have used experimental data obtained from proliferation experiments of leukemic cells, where cell population and glucose consumption were evaluated. These data were further used to examine whether cells progress through competitive behavior or synergistically. Our results have shown that cells probably progress through a cooperative strategy.

Bioinformatics Analysis Reveals Ki-67 Specific microRNA Functions in Pediatric Embryonal Tumors.

Pediatric Central Nervous System (CNS) neoplasms are the second most prevalent tumors of childhood. CNS malignancies are considered as the most notorious type of tumors, due to their anatomic position manifesting an imminent threat to the patients' life. miRNAs are molecules that play a significant role in CNS tumor biology. At the same time diagnostic markers such as Ki-67 have played an important role in CNS tumor diagnosis. In a previous study we have identified several miRNAs, common to different subtypes of pediatric embryonal CNS malignancies as well as, we have identified miRNAs that manifest significant dynamics with respect to their expression and the neoplasmatic subtype. Among the previously reported miRNAs, several have manifested significant differences with respect to Ki-67 expression. Those miRNAs, were further analyzed bioinformatically and related functions were revealed, where some of them confirmed Ki-67 role as a proliferation marker but also predicted novel miRNAs functions in pediatric embryonal tumors.

An Unsupervised Methodology for the Detection of Epileptic Seizures Using EEG Signals: A Multi-Dataset Evaluation.

Although the electroencephalogram (EEG) is the most commonly used means to monitor epileptic patients, public EEG datasets are very scarce making it difficult to develop and validate seizure detection algorithms. In this work an unsupervised seizure detection methodology is used to isolate ictal EEG segments without requiring any apriori information or human intervention. Seizures are detected using four simple seizure detection conditions that are activated when rhythmical activity from different brain areas is simultaneously concentrated in the alpha (8-13 Hz), theta (4-7 Hz) or delta (1-3 Hz) frequency range. Then, only a small proportion of the EEG segments that are most likely to contain ictal activity is selected and presented to the physician for the final evaluation. In this way, large volumes of EEG signals can be annotated in a fraction of the time and effort that would be otherwise required. Using EEG data from 33 sessions from the Temple University Hospital (TUH) EEG Corpus, our unsupervised methodology reached, on average, 84.92% seizure detection sensitivity with 3.46 false detections per hour of EEG signals.

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature.

Predicting rapid progression of Parkinson's Disease at baseline patients evaluation.

The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

PD_Manager: an mHealth platform for Parkinson's disease patient management.

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

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 individual's 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.

3-D registration on carotid artery imaging data: MRI for different timesteps.

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance (ANOVA) showed that the p-values were <;0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied.

Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry.

Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.

An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition.

Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35 dB on average.