Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Multi-resolution dental image registration based on genetic algorithm

The Automated Dental Identification System (ADIS) is a Post Mortem Dental Identification System. This thesis presents dental image registration, required for the preprocessing steps of the image comparison component of ADIS. We proposed a multi resolution dental image registration based on genetic algorithms. The main objective of this research is to develop techniques for registration of extracted subject regions of interest with corresponding reference regions of interest.;We investigated and implemented registration using two multi resolution techniques namely image sub sampling and wavelet decomposition. Multi resolution techniques help in the reduction of search data since initial registration is carried at lower levels and results are updated as the levels of resolutions increase. We adopted edges as image features that needed to be aligned. Affine transformations were selected to transform the subject dental region of interest to achieve better alignment with the reference region of interest. These transformations are known to capture complex image distortions. The similarity between subject and reference image has been computed using Oriented Hausdorff Similarity measure that is robust to severe noise and image degradations. A genetic algorithm was adopted to search for the best transformation parameters that give maximum similarity score.;Testing results show that the developed registration algorithm yielded reasonable results in accuracy for dental test cases that contained slight misalignments. The relative percentage errors between the known and estimated transformation parameters were less than 20% with a termination criterion of a ten minute time limit. Further research is needed for dental cases that contain high degree of misalignment, noise and distortions

Feasibility of using Lodox to perform digital subtraction angiography

Bibliography: leaves 150-157.Many cases in trauma involve vessel imaging to determine integrity and the origin of lesions or blockages. Digital subtraction angiography (DSA) is a tool used to improve the clarity of the vessels being imaged for better and easier decision making in diagnostics and planning. Lodox, a low dose x-ray system developed by Debex (Pty) Ltd, a subsidiary of de Beers, was designed specifically for the trauma environment. It therefore follows that, if possible, a function so readily used in trauma, such as DSA, should be added to the imaging repertoire of an x-ray system designed for use in this environment. In this dissertation the feasibility of using Lodox to perform DSA is therefore explored. In doing so, the requirements of a trauma unit and the theory behind DSA were researched so as to obtain a better understanding into what would be required

Design and Characterization of a High-resolution Cardiovascular Imager

Fluoroscopic imaging devices for interventional radiology and cardiovascular applications have traditionally used image-intensifiers optically coupled to either charge-coupled devices (CCDs) or video pick-up tubes. While such devices provide image quality sufficient for most clinical applications, there are several limitations, such as loss of resolution in the fringes of the image-intensifier, veiling glare and associated contrast loss, distortion, size, and degradation with time. This work is aimed at overcoming these limitations posed by image-intensifiers, while improving on the image quality. System design parameters related to the development of a high-resolution CCD-based imager are presented. The proposed system uses four 8 x 8-cm three-side buttable CCDs tiled in a seamless fashion to achieve a field of view (FOV) of 16 x 16-cm. Larger FOVs can be achieved by tiling more CCDs in a similar manner. The system employs a thallium-doped cesium iodide (CsI:Tl) scintillator coupled to the CCDs by straight (non-tapering) fiberoptics and can be operated in 78, 156 or 234-microns pixel pitch modes. Design parameters such as quantum efficiency and scintillation yield of CsI:Tl, optical coupling efficiency and estimation of the thickness of fiberoptics to provide reasonable protection to the CCD, linearity, sensitivity, dynamic range, noise characteristics of the CCD, techniques for tiling the CCDs in a seamless fashion, and extending the field of view are addressed. The signal and noise propagation in the imager was modeled as a cascade of linear-systems and used to predict objective image quality parameters such as the spatial frequency-dependent modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE). The theoretical predictions were compared with experimental measurements of the MTF, NPS and DQE of a single 8 x 8-cm module coupled to a 450-microns thick CsI:Tl at x-ray beam quality appropriate for cardiovascular fluoroscopy. The measured limiting spatial resolution (10% MTF) was 3.9 cy/mm and 3.6 cy/mm along the two orthogonal axes. The measured DQE(0) was ~0.62 and showed no dependence with incident exposure rate over the range of measurement. The experimental DQE measurements demonstrated good agreement with the theoretical estimate obtained using the parallel-cascaded linear-systems model. The temporal imaging properties were characterized in terms of image lag and showed a first frame image lag of 0.9%. The imager demonstrated the ability to provide images of high and uniform spatial resolution, while preserving and potentially improving on DQE performance at dose levels lower than that currently used in clinical practice. These results provide strong support for potential adaptation of this type of imager for cardiovascular and pediatric angiography

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Delayed Stroke after Aneurysm Treatment with Flow Diverters in Small Cerebral Vessels: A Potentially Critical Complication Caused by Subacute Vasospasm

Flow diversion (FD) is a novel endovascular technique based on the profound alteration of cerebrovascular hemodynamics, which emerged as a promising minimally invasive therapy for intracranial aneurysms. However, delayed post-procedural stroke remains an unexplained concern. A consistent follow-up-regimen has not yet been defined, but is required urgently to clarify the underlying cause of delayed ischemia. In the last two years, 223 patients were treated with six different FD devices in our center. We identified subacute, FD-induced segmental vasospasm (SV) in 36 patients as a yet unknown, delayed-type reaction potentially compromising brain perfusion to a critical level. Furthermore, 86% of all patients revealed significant SV approximately four weeks after treatment. In addition, 56% had SV with 25% stenosis, and 80% had additional neointimal hyperplasia. Only 13% exhibited SV-related high-grade stenosis. One of those suffered stroke due to prolonged SV, requiring neurocritical care and repeated intra-arterial (i.a.) biochemical angioplasty for seven days to prevent territorial infarction. Five patients suffered newly manifested, transient hemicrania accompanying a compensatorily increased ipsilateral leptomeningeal perfusion. One treated vessel obliterated permanently. Hence, FD-induced SV is a frequent vascular reaction after FD treatment, potentially causing symptomatic ischemia or even stroke, approximately one month post procedure. A specifically early follow-up-strategy must be applied to identify patients at risk for ischemia, requiring intensified monitoring and potentially anti-vasospastic treatment

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Advancing the Clinical Potential of Carbon Nanotube-enabled stationary 3D Mammography

Scope and purpose. 3D imaging has revolutionized medicine. Digital breast tomosynthesis (DBT), also recognized as 3D mammography, is a relatively recent example. stationary DBT (sDBT) is an experimental technology in which the single moving x-ray source of conventional DBT has been replaced by a fixed array of carbon nanotube (CNT)-enabled sources. Given the potential for a higher spatial and temporal resolution compared to commercially-available, moving-source DBT devices, it was hypothesized that sDBT would provide a valuable tool for breast imaging. As such, the purpose of this work was to explore the clinical potential of sDBT. To accomplish this purpose, three broad Aims were set forth: (1) study the challenges of scatter and artifact with sDBT, (2) assess the performance of sDBT relative to standard mammographic screening approaches, and (3) develop a synthetic mammography capability for sDBT. Throughout the work, developing image processing approaches to maximize the diagnostic value of the information presented to readers remained a specific goal. Data sources and methodology. Sitting at the intersection of development and clinical application, this work involved both basic experimentation and human study. Quantitative measures of image quality as well as reader preference and accuracy were used to assess the performance of sDBT. These studies imaged breast-mimicking phantoms, lumpectomy specimens, and human subjects on IRB-approved study protocols, often using standard 2D and conventional 3D mammography for reference. Key findings. Characterizing scatter and artifact allowed the development of new processing approaches to improve image quality. Additionally, comparing the performance of sDBT to standard breast imaging technologies helped identify opportunities for improvement through processing. This line of research culminated in the incorporation of a synthetic mammography capability into sDBT, yielding images that have the potential to improve the diagnostic value of sDBT. Implications. This work advanced the evolution of CNT-enabled sDBT toward a viable clinical tool by incorporating key image processing functionality and characterizing the performance of sDBT relative to standard breast imaging techniques. The findings confirmed the clinical utility of sDBT while also suggesting promising paths for future research and development with this unique approach to breast imaging.Doctor of Philosoph

Contribution of noise to the variance of integrating detectors

X-ray medical imaging provides invaluable medical information, while subjecting patients to hazardous ionizing radiation. The dosage that the patient is exposed to may be reduced, at the cost of image resolution. A technology that promises lower dosage for a given resolution is direct conversion digital imaging, typically based on amorphous Selenium semiconductor. Sufficient exposure should be used for the first exposure to avoid subsequent exposures; a challenge is then to reduce the necessary exposure for a suitable image. To quantify how little radiation the detector can reliably discriminate, one needs an analysis of the variance that 1/f and white noise contribute to the signal of such detectors. An important consideration is that the dark current, which varies with time, is subtracted from the photo-current, to reduce the spurious spatial variance in the image. In this thesis, the variance that 1/f noise contributes to integrating detectors is analysed, for a very general integrating detector. Experiments were performed to verify the theoretical results obtained for the 1/f noise variance contribution