Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis

The key experimental results from this research are the viable and cost effective methods of diagnosing oral and pancreatic cancer with accuracies over 90%. Furthermore, development of the molecular windowing method to further narrow down the origins of those cancer biomarkers and further improve accuracy.Many papers are being published demonstrating how vibrational spectral biomarkers can be used to diagnose a whole variety of diseases, from cancers to colitis. However, much of the research, proposed as discovering a useful tool for clinical diagnosis, has not yet been widely utilised in clinical practice. This is due mainly to the lack or reproducibility of the findings and current lack of relating the spectral observation to a root biological cause. This thesis aims to highlight the inconsistencies between studies and propose an improved process for spectral biomarker identification, including suggestions for follow up studies to discover the foundation of the spectral change. This thesis reassesses, and adds to, ground covered by previous reviews regarding sample preparation, patient selection and multivariate analysis.Resultantly, this thesis brings light to the need, and suggests solutions, for:• a method to standardise results between detection devices,• knowledge of the additional requirements for using biomarkers for disease monitoring/prognosis,• understanding the biological root cause for the spectral shift.These promising results and suggestions for combined methodology improvements will provide guidance to enable this burgeoning research field to improve patient outcome in the clinical sphere

Toward a novel predictive analysis framework for new-generation clinical decision support systems

The idea of developing automated tools able to deal with the complexity of clinical information processing dates back to the late 60s: since then, there has been scope for improving medical care due to the rapid growth of medical knowledge, and the need to explore new ways of delivering this due to the shortage of physicians. Clinical decision support systems (CDSS) are able to aid in the acquisition of patient data and to suggest appropriate decisions on the basis of the data thus acquired. Many improvements are envisaged due to the adoption of such systems including: reduction of costs by faster diagnosis, reduction of unnecessary examinations, reduction of risk of adverse events and medication errors, increase in the available time for direct patient care, improved medications and examination prescriptions, improved patient satisfaction, and better compliance to gold-standard up-to-date clinical pathways and guidelines. Logistic regression is a widely used algorithm which frequently appears in medical literature for building clinical decision support systems: however, published studies frequently have not followed commonly recommended procedures for using logistic regression and substantial shortcomings in the reporting of logistic regression results have been noted. Published literature has often accepted conclusions from studies which have not addressed the appropriateness and accuracy of the statistical analyses and other methodological issues, leading to design flaws in those models and to possible inconsistencies in the novel clinical knowledge based on such results. The main objective of this interdisciplinary work is to design a sound framework for the development of clinical decision support systems. We propose a framework that supports the proper development of such systems, and in particular the underlying predictive models, identifying best practices for each stage of the model’s development. This framework is composed of a number of subsequent stages: 1) dataset preparation insures that appropriate variables are presented to the model in a consistent format, 2) the model construction stage builds the actual regression (or logistic regression) model determining its coefficients and selecting statistically significant variables; this phase is generally preceded by a pre-modelling stage during which model functional forms are hypothesized based on a priori knowledge 3) the further model validation stage investigates whether the model could suffer from overfitting, i.e., the model has a good accuracy on training data but significantly lower accuracy on unseen data, 4) the evaluation stage gives a measure of the predictive power of the model (making use of the ROC curve, which allows to evaluate the predictive power of the model without any assumptions on error costs, and possibly R2 from regressions), 5) misclassification analysis could suggest useful insights into determining where the model could be unreliable, 6) implementation stage. The proposed framework has been applied to three applications on different domains, with a view to improve previous research studies. The first developed model predicts mortality within 28 days of patients suffering from acute alcoholic hepatitis. The aim of this application is to build a new predictive model that can be used in clinical practice to identify patients at greatest risk of mortality in 28 days as they may benefit from aggressive intervention, and to monitor their progress while in hospital. A comparison generated by state of the art tools shows an improved predictive power, demonstrating how an appropriate variables inclusion may result in an overall better accuracy of the model, which increased by 25% following an appropriate variables selection process. The second proposed predictive model is designed to aid the diagnosis of dementia, as clinicians often experience difficulties in the diagnosis of dementia due to the intrinsic complexity of the process and lack of comprehensive diagnostic tools. The aim of this application is to improve on the performance of a recent application of Bayesian belief networks using an alternative approach based on logistic regression. The approach based on statistical variables selection outperformed the model which used variables selected by domain experts in previous studies. Obtained results outperform considered benchmarks by 15%. The third built model predicts the probability of experiencing a certain symptom among common side-effects in patients receiving chemotherapy. The newly developed model includes a pre-modelling stage (which was based on previous research studies) and a subsequent regression. The computed accuracy of results (computed on a daily basis for each cycle of therapy) shows that the newly proposed approach has increased its predictive power by 19% when compared to the previously developed model: this has been obtained by an appropriate usage of available a priori knowledge to pre-model the functional forms. As shown by the proposed applications, different aspects of CDSS development are subject to substantial improvements: the application of the proposed framework to different domains leads to more accurate models than the existing state-of-the-art proposals. The developed framework is capable of helping researchers to identify and overcome possible pitfalls in their ongoing research works, by providing them with best practices for each step of the development process. An impact on the development of future clinical decision support systems is envisaged: the usage of an appropriate procedure in model development will produce more reliable and accurate systems, and will have a positive impact on the newly produced medical knowledge which may eventually be included in standard clinical practice

Diagnosis of liver disease by computer- assisted imaging techniques: A literature review

Copyright © 2022 The authors. Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician's or doctor's experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms

Noninvasive methods for the detection and diagnosis of hepatic diseases compared to the previous standard of care

As the global burden of liver disease evolves, a need for noninvasive detection and diagnosis has emerged. For over fifty years biopsy has been the standard to which all other disease detection and confirmation methods have been compared. With the development of several noninvasive methods in the detection of liver disease, biopsy has come under scrutiny for its cost-effectiveness, reliability and safety. Serologic testing has proven useful but not specific overall for the determination of disease stages. Liver stiffness is a relatively new parameter used in the diagnosis and monitoring of hepatic disease. It is a quantifiable through the use of an ultrasound-based method of transient elastography using a tool Fibroscan. The implementation of transient elastography has changed the paradigm of liver disease diagnostics with a more cost effective, reproducible, reliable, and well-tolerated option. While the hepatitis C virus is the primary cause of fibrosis and cirrhosis, numerous studies of the application of liver stiffness measurement to varying disease etiologies have broadened the scope of the method. Optimizing reliability criteria has been the focus of many studies to find a standard by which cirrhosis may be ruled out and fibrosis staging may be accomplished. Novel non-interferon based HCV therapies are altering the course of disease progression and may affect the need for continued development of noninvasive monitoring procedures. However, globally the impact of advanced liver disease is rising with an increase in mortality by fifty million cases per year from 1990-2010 indicating the continued relevance and need for widely applicable noninvasive procedures for both diagnosing hepatic disease and informing treatment options

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Introduction: Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods: This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results: The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion: The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration

The VPS ReplaySuite: development and evaluation of a novel, Internet based telepathology tool

The ReplaySuite is a web-based telepathology tool that replicates the doubleheaded microscope environment online, enabling a reviewing pathologist to ‘replay’ an archived virtual slide examination. Examination-tracking data obtained by the Virtual Pathology Slide (VPS) virtual slide viewer is exploited, allowing a remote pathologist to review an examination conducted at a different time and location. This removes temporal and spatial issues associated with double-headed microscopy. In order to conduct a preliminary evaluation of the technology, 9 pathologists used the ReplaySuite to review examination replays and diagnostic data from archived examinations of 10 needlecore breast biopsies. Diagnostically difficult cases were most frequently evaluated, either via diagnostic concordance graphs or examination replays, and all 3 participants who replayed more than 10 examinations stated the ReplaySuite to be of some or great benefit in pathology training and quality assurance. Of those who replayed an examination by another pathologist, 83% (5/6) agreed that replays provided an insight into the examining pathologists diagnosis, and 33% (2/6) reconsidered their own diagnosis for at least one case. Of those who reconsidered their original diagnosis, all reclassified either concordant with group consensus or original glass slide diagnosis. This study demonstrated that the ReplaySuite was of potential benefit in pathology education, however the technology required evaluation in a setting that would facilitate its impact on diagnostic performance. Accordingly, a redeveloped VPS and ReplaySuite were incorporated into the EQUALIS External Quality Assurance (EQA) study in chronic hepatitis staging and grading. During the study, 9 Swedish pathology departments examined and scored digital representations of liver needlecore biopsies during two sessions, with 10 cases per session and two digital slides per case. Between scoring sessions, participants were provided with access to two supplementary electronic resources: the ReplaySuite, and a library of pre-selected reference images. Comparison of concordance with gold standard (KVAST group) scoring before and after electronic resource use facilitated the elucidation of impact on diagnostic performance. Between scoring sessions, participant concordance with KVAST staging increased by 18% (49%-67%), while concordance with KVAST grading increased by 20% (34%-54%). Mean staging un-weighted kappa improved from 0.347 to 0.554 (+0.207), or from ‘fair’ to ‘moderate’ exact agreement with KVAST staging. Linear weighted staging kappa improved from 0.603 to 0.688 (+0.085), indicating close agreement in both sessions. Mean grading unweighted kappa increased from 0.132 to 0.412 (+0.280), or from a ‘poor’ to ‘moderate’ level o f exact agreement with KVAST, while linear weighted kappa improved from 0.328 to 0.624 (+0.295), or from ‘fair’ to ‘good’ level of approximate agreement with KVAST. Subsequent to the EQA scheme, an expert liver pathologist used the ReplaySuite to evaluate study examinations, assessing examination technique and identifying sources of error. Examinations scoring concordant with KVAST were observed to exhibit acceptable examination technique more frequently than discordantly scoring examinations. When grading, 28% (46% - 18%) more concordant than discordant examinations were considered to have viewed sufficient tissue, and at the appropriate magnification. A similar disparity of 24% (59% - 35%) was observed in staging, suggesting that examination technique was important both when determining the degree of necroinflammation within a biopsy, and when ascertaining the extent of fibrosis. In assessing sources of error, the expert pathologist identified a potential source in 50% of grading examinations, with misinterpretation of observed pathology cited in 19%, and missed pathology (oversight) cited in 31% of grading examinations. Of the 41% of staging examinations in which a source was identified, misinterpretation of observed pathology was cited in 20% of examinations, and missed pathology (oversight) in 21% of examinations. This study demonstrated that the use of supplementary electronic resources could result in improvements in diagnostic performance. It also illustrated the significant ‘add on’ value that could be provided by the ReplaySuite in EQA, by providing means to assess not only diagnostic concordance, but also diagnostic technique and identify sources of error. In order to assess Irish trainee pathologist’s perceptions of computer-assisted learning (CAL), a number of commercial systems were utilised to incorporate digital slides into a postgraduate seminar series, and provide subsequent access to seminar digital slides, diagnoses and expert annotations online. All surveyed trainees considered the use of digital slides and expert annotations of benefit in pathology training, and considered the potential implementation of expert examination replays, online self-assessment and the capability to search online for material by organ, diagnosis or pathological feature of benefit. The work described herein illustrates that both expert and trainee pathologists alike consider the use of supplementary electronic resources of benefit in pathology education, and demonstrates that their use can improve diagnostic performance. The ability to evaluate participation in EQA studies via the ReplaySuite provides significant additional value to education schemes, providing a depth of assessment not possible with conventional microscopy

Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

Computational approaches for improving treatment and prevention of viral infections

The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptor-hiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.Die Behandlung von HIV- oder HCV-Infektionen ist herausfordernd. Daher werden neue Wirkstoffe, sowie neue computerbasierte Verfahren benötigt, welche die Therapie verbessern. In dieser Arbeit wurden Methoden zur Unterstützung der Therapieauswahl entwickelt, aber auch solche, welche neuartige Therapien vorantreiben. geno2pheno[ngs-freq] bestimmt, ob Resistenzen gegen Medikamente vorliegen, indem es Hochdurchsatzsequenzierungsdaten von HIV-1 oder HCV Proben mittels Support Vector Machines oder einem regelbasierten Ansatz interpretiert. geno2pheno[coreceptor-hiv2] bestimmt den HIV-2 Korezeptorgebrauch dadurch, dass es einen Abschnitt des viralen Oberflächenproteins mit einer Support Vector Machine analysiert. openPrimeR kann optimale Kombinationen von Primern für die Multiplex-Polymerasekettenreaktion finden, indem es ein Mengenüberdeckungsproblem löst und auf ein neues logistisches Regressionsmodell für die Vorhersage von Amplifizierungsereignissen zurückgreift. geno2pheno[ngs-freq] und geno2pheno[coreceptor-hiv2] ermöglichen die Personalisierung antiviraler Therapien und unterstützen die klinische Entscheidungsfindung. Durch den Einsatz von openPrimeR auf humanen Immunoglobulinsequenzen konnten Primersätze generiert werden, welche die Isolierung von breit neutralisierenden Antikörpern gegen HIV-1 verbessern. Die in dieser Arbeit entwickelten Methoden leisten somit einen wichtigen Beitrag zur Verbesserung der Prävention und Therapie viraler Infektionskrankheiten