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.

Development of a clinical decision support system using genetic algorithms and Bayesian classification for improving the personalised management of women attending a colposcopy room.

Cervical cancer (CxCa) is often the result of underestimated abnormalities in the test Papanicolaou (Pap test). The recent advances in the study of the human papillomavirus (HPV) infection (the necessary cause for CxCa development) have guided clinical practice to add HPV related tests alongside the Pap test. In this way, today, HPV DNA testing is well accepted as an ancillary test and it is used for the triage of women with abnormal findings in cytology. However, these tests are either highly sensitive or highly specific, and therefore none of them provides an optimal solution. In this Letter, a clinical decision support system based on a hybrid genetic algorithm - Bayesian classification framework is presented, which combines the results of the Pap test with those of the HPV DNA test in order to exploit the benefits of each method and produce more accurate outcomes. Compared with the medical tests and their combinations (co-testing), the proposed system produced the best receiver operating characteristic curve and the most balanced combination among sensitivity and specificity in detecting high-grade cervical intraepithelial neoplasia and CxCa (CIN2+). This system may support decision-making for the improved management of women who attend a colposcopy room following a positive test result.

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.

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.

A decision support system based on an ensemble of random forests for improving the management of women with abnormal findings at cervical cancer screening.

In most cases, cervical cancer (CxCa) develops due to underestimated abnormalities in the Pap test. Today, there are ancillary molecular biology techniques available that provide important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA tests, HPV mRNA tests and immunocytochemistry techniques such as overexpression of p16. These techniques are either highly sensitive or highly specific, however not both at the same time, thus no perfect method is available today. In this paper we present a decision support system (DSS) based on an ensemble of Random Forests (RFs) for the intelligent combination of the results of classic and ancillary techniques that are available for CxCa detection, in order to exploit the benefits of each technique and produce more accurate results. The proposed system achieved both, high sensitivity (86.1%) and high specificity (93.3%), as well as high overall accuracy (91.8%), in detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). The system's performance was better than any other single test involved in this study. Moreover, the proposed architecture of employing an ensemble of RFs proved to be better than the single classifier approach. The presented system can handle cases with missing tests and more importantly cases with inadequate cytological outcome, thus it can also produce accurate results in the case of stand-alone HPV-based screening, where Pap test is not applied. The proposed system may identify women at true risk of developing CxCa and guide personalised management and therapeutic interventions.

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.

A randomized controlled pilot study to evaluate a technology platform for the assisted living of people with dementia and their carers.

The use of telemedicine is becoming increasingly popular in assisting with the home management of People with Dementia (PwD) by offering services to the carers that may enhance their ability to care for their relative for longer. A computerized platform, ALADDIN, was evaluated in its usefulness to reduce carer burden and distress and to improve their quality of life, in an attempt to delay institutionalization of PwD. ALADDIN offers educational material about dementia to carers and provides the opportunity to contact other carers and clinicians. ALADDIN also facilitates remote monitoring of the PwD and their carers by the clinicians to enable speedy delivery of appropriate intervention. The ALADDIN platform was piloted at three European sites, and used by thirty carers of PwD living in the community (platform group). The platform group and a control group of thirty PwD and their carers were assessed at baseline, 3 months, and 6 months. The results showed a significant improvement in the quality of life of the carers in the platform group, with some reduction in carer burden and distress. The platform was useful in monitoring the patients and facilitating contact with other professionals. Access to and use of the ALADDIN platform was rated positively by carers and clinicians. The ALADDIN platform's usefulness and applicability for prolonging the home management of PwD are discussed.

Multiplex assessment of a panel of 16 serum molecules for the differential diagnosis of Alzheimer's disease.

One of the current challenge in Alzheimer's disease (AD) is the identification of reliable biomarkers that might improve diagnostic accuracy, possibly correlating with the disease progression and patient's response to therapy. As the clinically validated AD biomarkers evaluate cerebrospinal fluid (CSF) parameters, the need for less invasive diagnostic markers is well evident. To this respect, blood circulating cytokines or growth factors have provided some encouraging results, even though no clinically validated to date. In 2007 Ray et al suggested a panel of 18 circulating molecules that might increase AD diagnostic accuracy. In an attempt of replicating their data, we designed a multiplex fluorimetric assay comprising 16 independent analytes and covering 15 out of the 18 described proteins. We collected serum samples from three diagnostic groups: probable AD (n=33), matched healthy controls (CNT, n=23) and non AD demented (NAD, n=14). After correction for age, we found an increased level of EGF-1 in AD in comparison to CNT and NAD, while an increase of TRAIL-R4 was found in NAD. However, evaluation of specificity/sensitivity by ROC curve analysis gave weak evidence of diagnostic accuracy (area under the curve = 0.63 and 0.66 for EGF and TRAIL-R4, respectively). Finally, we tried to find a diagnostic classifier by a multivariate algorithm. We found indication of diagnostic evidence for AD only, while NAD samples did not show a diagnostic pattern.

Emotional impairment in a patient with amyotrophic lateral sclerosis: a (99m)Tc-HMPAO SPET brain study.

Behavioral abnormalities have been reported in patients with amyotrophic lateral sclerosis (ALS) but their aetiology is not yet clearly defined. We report on the case of a 48 years old woman with long standing bulbar onset ALS referred to our department for brain perfusion scan for the evaluation of behavioral and emotional disorders. The patient's scores on the neuropsychological tests were satisfactory while magnetic resonance imaging showed no structural brain abnormalities. However, cerebral blood flow imaging with single photon emission tomography with technetium-99m-hexamethyl propylamine oxime demonstrated bilateral frontal cortex hypoperfusion, as well as perfusion defects in the left parietal, temporal and occipital lobes. In conclusion, the reduced regional cerebral blood flow in the frontal lobes might be suggestive of underlying cortical disturbance and potentially explain the patient's symptoms.

Regulation of α-synuclein expression in cultured cortical neurons.

Alpha-synuclein (SNCA) is a predominantly neuronal protein involved in the control of neurotransmitter release. The levels of SNCA expression are closely linked to the pathogenesis of Parkinson's disease; however, the biochemical pathways and transcriptional elements that control SNCA expression are not well understood. We previously used the model system of neurotrophin-mediated PC12 cell neuronal differentiation to examine these phenomena. Although these studies were informative, they were limited to the use of a cell line; therefore, in the current work, we have turned our attention to cultured primary rat cortical neurons. In these cultures, SNCA expression increased with time in culture, as the neurons mature. Luciferase assays based on transient transfections of fusion constructs encoding components of the transcriptional control region of SNCA identified various promoter areas that have a positive or negative effect on SNCA transcription. Intron 1, previously identified by us as an important regulatory region in the PC12 cell model, cooperates with regions 5' to exon 1 to mediate gene transcription. Using selective pharmacological tools, we find that tyrosine kinase receptors and the phosphatidyl-inositol 3 kinase signaling pathway are involved in mediating these effects. The exogenous application of the neurotrophin brain-derived neurotrophic factor (BDNF) is sufficient on its own to promote the transcriptional activation of SNCA through this pathway, but a neutralizing antibody against BDNF failed to affect SNCA transcription in maturing cultures, suggesting that BDNF is not the main factor involved in maturation-induced SNCA transcription in this model. Further in vivo studies are needed to establish the role of neurotrophin signaling in the control of SNCA transcription.