451 research outputs found
Object detection, recognition and classification using computer vision and artificial intelligence approaches
Object detection and recognition has been used extensively in recent years to solve numerus challenges in different fields. Due to the vital roles they play, object detection and recognition has enabled quantum leaps in many industry fields by helping to overcome some serious challenges and obstacles. For example, worldwide security concerns have drawn the attention and stimulated the use of highly intelligent computer vision technology to provide security in different environments and in diverse terrains. In addition, some wildlife is at present exposed to danger and extinction worldwide. Therefore, early detection and recognition of potential threats to wildlife have become essential and timely. The extent of using computer vision and artificial intelligence to convert the seemingly insecure world to a more secure one has been widely accepted. Such technologies are used in monitoring, tracking, organising, analysing objects in a scene and for a number of other countless purposes. [Continues.
Label Uncertainty and Learning Using Partially Available Privileged Information for Clinical Decision Support: Applications in Detection of Acute Respiratory Distress Syndrome
Artificial intelligence and machine learning have the potential to transform health care by deriving new and important insights from the vast amount of data generated during routine delivery of healthcare. The digitization of health data provides an important opportunity for new knowledge discovery and improved care delivery through the development of clinical decision support that can leverage this data to support various aspects of healthcare - from early diagnosis to epidemiology, drug development, and robotic-assisted surgery. These diverse efforts share the ultimate goal of improving quality of care and outcome for patients. This thesis aims to tackle long-standing problems in machine learning and healthcare, such as modeling label uncertainty (e.g., from ambiguity in diagnosis or poorly labeled examples) and representation of data that may not be reliably accessible in a live environment.
Label uncertainty hinges on the fact that even clinical experts may have low confidence when assigning a medical diagnosis to some patients due to ambiguity in the case or imperfect reliability of the diagnostic criteria. As a result, some data used for machine training may be mislabeled, hindering the model’s ability to learn the complexity of the underlying task and adversely affecting the algorithm’s overall performance. In this work, I describe a heuristic approach for physicians to quantify their diagnostic uncertainty. I also propose an implementation of instance-weighted support vector machines to incorporate this information during model training.
To address the issue of unreliable data, this thesis examines the idea of learning using “partially available” privileged information. This paradigm, based on knowledge transfer, allows for models to use additional data available during training but may not be accessible during testing/deployment. This type of data is abundant in healthcare, where much more information about a patient’s health status is available in retrospective analysis (e.g., in the training data) but not available in real-time environments (e.g., in the test set). In this thesis, “privileged information” are features extracted from chest x-rays (CXRs) using novel feature engineering algorithms and transfer learning with deep residual networks. This example works well for numerous clinical applications, since CXRs are retrospectively accessible during model training but may not be available in a live environment due to delay from ordering, developing, and processing the request.
This thesis is motivated by improving diagnosis of acute respiratory distress syndrome (ARDS), a life-threatening lung injury associated with high mortality. The diagnosis of ARDS serves as a model for many medical conditions where standard tests are not routinely available and diagnostic uncertainty is common. While this thesis focuses on improving diagnosis of ARDS, the proposed learning methods will generalize across various healthcare settings, allowing for better characterization of patient health status and improving the overall quality of patient care. This thesis also includes development of methods for time-series analysis of longitudinal health data, signal processing techniques for quality assessment, lung segmentation from complex CXRs, and novel feature extraction algorithm for quantification of pulmonary opacification. These algorithms were tested and validated on data obtained from patients at Michigan Medicine and additional external sources. These studies demonstrate that careful, principled use of methodologies in machine learning and artificial intelligence can potentially assist healthcare providers with early detection of ARDS and help make a timely, accurate medical diagnosis to improve outcomes for patients.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167930/1/nreamaro_1.pd
Image similarity in medical images
Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al
Application of Machine Learning in Healthcare and Medicine: A Review
This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration
Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework
Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them
The effect of pulsatile flow on co-cultured retinal endothelial & pericyte cells
Microvascular cell fate decisions are hallmarks of the microvascular cell response to injury and play a crucial role in the pathogenesis of retinal disease. Abnormalities in retinal blood flow play a critical role in remodeling of the retinal vasculature by altering microvascular endothelial and pericyte cell fate (proliferation, apoptosis and migration). Retinal blood flow is controlled locally by vasodilators such as nitric oxide, prostacyclin and the vasoconstrictor endothelin-1 , with considerable evidence linking retinal pathologies such as Normal Tension Glaucoma and Diabetic Retinopathy to altered retinal blood flow Shear stress has previously been shown to modulate EC production of these vasoactive agents in macrovascular cells. Therefore, using a perfused transcapillary coculture of bovine microvascular retinal endothelial cells (BRECs) and bovine retinal pericytes (BRPs), we examined the acute and chronic effect of pulsatile flow on the release of these vasoactive mediators and their subsequent role in modulating retinal vascular cell fate.
Acute exposure to pulsatile flow increased BREC NO, PGI2 & ET-1 formation and release Similarly, chronic exposure to pulsatile flow enhanced NO and PGI2 release while concomitantly inhibiting ET-1 in these cells In parallel studies, there was an increase in BRP apoptosis following exposure to high pulsatile flow, whereas BREC apoptosis decreased. Furthermore, the pulsatile flow-induced increases in BRP apoptosis is dependent on increased PGI2 , whereas both ET-1 and NO mediate the protective effect of increased flow on BRECs survival.
Notch receptor-hgand interactions and the Hedgehog signalling pathway have been strongly implicated in vascular morphogenesis and remodelling of the embryonic vasculature, with Hedgehog acting upstream of Notch signalling during development. We therefore tested the hypothesis that Hedgehog (Hh) and Notch pathway interact to promote changes in vascular cell fate in BRECs and BRPs in vitro in response to changes in pulsatile flow
The potential role of the hedgehog signalling pathway in the regulation of epithelial-mesenchymal transition in breast cancer
Breast cancer is a deadly disease that accounts for a third of all female cancer- related deaths globally. Although recent advances in early diagnosis and targeted therapy using prognostic markers have reduced deaths, more than 50% of newly diagnosed cases have already developed metastatic disease at diagnosis. Furthermore, the remaining cases still have a high risk of secondary disease and relapse. Breast cancer metastasis is the leading cause of breast cancer related mortality, it is incurable and current treatments aims to prolong life alongside pain management. Thus, there is a need for treatment for this disease.The epithelial-mesenchymal transition (EMT) is the process by which cancer cells acquire the ability to invade and eventually metastasise. Therefore, understanding the regulation of breast cancer cell metastasis is essential for identifying methods for management of metastatic disease either by prevention or treatment. The first aim of this study was to confirm the ability of breast cancer cell lines, that belong to several molecular subtypes to undergo EMT using an in vitro seeding density model. Results showed that there was active EMT in breast cancer cell lines and that the activation of these pathways is not restricted to, nor governed by, molecular subtypes of breast cancer. Then to identify pathways involved in regulating EMT and utilising these pathways as prognostic marker as well as for developing treatments.The Hedgehog (Hh) signalling pathway is involved in the regulation of EMT during mammary gland development stages. This pathway was involved in the progression and metastasis of many human cancers. So, the expression of a number of proteins involved in the Hh pathway were assessed in a cohort of breast cancer patient samples. The expression of these proteins in the tumour centre and also the invasive front was assessed and correlated with the clinicopathological criteria. Data from breast cancer cohort showed that there was increased expression of Hh proteins (Gli1, Gli2 and Gli3) at the invasive front. Also, the data suggested that breast cancer cells gain the ability to activate Hh signalling by autocrine rather than by paracrine signalling. These findings encouraged further investigation to understand the effect of inhibiting the Hh signalling using cyclopamine or LDE225 in vitro.Inhibition with cyclopamine or LDE225 resulted in a reduction of cell yield and viability that correlated with increased cellular apoptosis. The reduction in viability and increased cell death was associated with alteration of Hh proteins expression and subcellular localisation. Also, assessment of the catenin-related transcription, that measured the outputs of canonical Wnt signalling, showed that inhibiting Hh signalling using cyclopamine and LDE225 resulted in reduction of Wnt signalling activity in both cell lines. Assessment of E-cadherin expression showed that Hh inhibition caused increased of expression in both cell lines, that was associated with a reduction of cells invasion.Findings showed that Hh signalling was involved in the regulation of EMT and that there was crosstalk between Hh and Wnt signalling in breast cancer cells. It can be concluded that the combined activation of Hh and Wnt signalling in breast cancer was associated with increased metastasis as a result of EMT activation. Assessment of the co-expression of Hh and Wnt signalling proteins in breast cancer samples provides potential prognostic markers for identifying breast cancer patients who that could benefit from Hh-targeted therapy
Implementing decision tree-based algorithms in medical diagnostic decision support systems
As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems.
Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks.
We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models
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