AUTOMATED RETINAL IMAGE ANALYSIS TO DETECT WHITE MATTER HYPERINTENSITIES IN STROKE- AND DEMENTIA-FREE HEALTHY SUBJECTS - A CROSS-VALIDATION STUDY PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download AUTOMATED RETINAL IMAGE ANALYSIS TO DETECT WHITE MATTER HYPERINTENSITIES IN STROKE- AND DEMENTIA-FREE HEALTHY SUBJECTS - A CROSS-VALIDATION STUDY PDF full book. Access full book title AUTOMATED RETINAL IMAGE ANALYSIS TO DETECT WHITE MATTER HYPERINTENSITIES IN STROKE- AND DEMENTIA-FREE HEALTHY SUBJECTS - A CROSS-VALIDATION STUDY by Alexander Y. Lau. Download full books in PDF and EPUB format.
Author: Alexander Y. Lau Publisher: ISBN: Category : Languages : en Pages :
Book Description
Background Retinal imaging with artificial-intelligence assisted analysis has the potential to become a simple and reliable tool for screening population-at-risk of cerebrovascular disease and dementia. ObjectiveTo develop an algorithm with automatic retinal imaging in identifying asymptomatic subjects with high burden of white matter hyperintensities (WMH).MethodsWe performed automated retinal image analysis (ARIA) in 180 community dwelling, stroke and dementia-free healthy subjects. ARIA is fully automatic and validated in separate disease cohorts. WMH on MRI brain was graded using ARWMC scale by an independent accessor. 126(70%) subjects were randomly selected for model building, 27(15%) for model cross-validation, and remaining 27(15%) for testing; all 180 subjects were used for evaluation of model accuracy to predict WMH burden. ResultsAll 180 subjects completed ARIA with successful analysis. The mean age was 70.3 +/- 4.5 years, 70(39%) were male. Risk factor profiles were: 106(59%) hypertension, 31(17%) diabetes, and 47(26%) hyperlipidemia. Severe WMH (defined as global ARWMC grading >=2) was found in 56(31%) subjects. The performance (sensitivity, SN; and specificity, SP) for model building (SN 96.7%, SP 80.6%), model validation (SN 100%, SP 87.5%), and testing (SN 100%, SP 83.3%) was excellent. The overall performance was SN 97.6% and SP 82.1%, with PPV 94% and NPV 92%. There was good correlation with WMH volume (log-transformed) in the building (R=0.92), validation (R=0.81), testing (R=0.88) and overall (R=0.90) models, respectively. DiscussionWe developed a robust algorithm to automatically evaluate retinal fundus image that can identify community subjects with high WMH burden.
Author: Alexander Y. Lau Publisher: ISBN: Category : Languages : en Pages :
Book Description
Background Retinal imaging with artificial-intelligence assisted analysis has the potential to become a simple and reliable tool for screening population-at-risk of cerebrovascular disease and dementia. ObjectiveTo develop an algorithm with automatic retinal imaging in identifying asymptomatic subjects with high burden of white matter hyperintensities (WMH).MethodsWe performed automated retinal image analysis (ARIA) in 180 community dwelling, stroke and dementia-free healthy subjects. ARIA is fully automatic and validated in separate disease cohorts. WMH on MRI brain was graded using ARWMC scale by an independent accessor. 126(70%) subjects were randomly selected for model building, 27(15%) for model cross-validation, and remaining 27(15%) for testing; all 180 subjects were used for evaluation of model accuracy to predict WMH burden. ResultsAll 180 subjects completed ARIA with successful analysis. The mean age was 70.3 +/- 4.5 years, 70(39%) were male. Risk factor profiles were: 106(59%) hypertension, 31(17%) diabetes, and 47(26%) hyperlipidemia. Severe WMH (defined as global ARWMC grading >=2) was found in 56(31%) subjects. The performance (sensitivity, SN; and specificity, SP) for model building (SN 96.7%, SP 80.6%), model validation (SN 100%, SP 87.5%), and testing (SN 100%, SP 83.3%) was excellent. The overall performance was SN 97.6% and SP 82.1%, with PPV 94% and NPV 92%. There was good correlation with WMH volume (log-transformed) in the building (R=0.92), validation (R=0.81), testing (R=0.88) and overall (R=0.90) models, respectively. DiscussionWe developed a robust algorithm to automatically evaluate retinal fundus image that can identify community subjects with high WMH burden.
Author: Emanuele Trucco Publisher: Academic Press ISBN: 0081028172 Category : Computers Languages : en Pages : 506
Book Description
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies
Author: Mahsa Dadar Publisher: ISBN: Category : Languages : en Pages :
Book Description
"Automatic methods for segmentation of various tissues and pathologies are critical for systematic studies of the brain to investigate changes that occur in different components of the phenomenon under study. White matter hyperintensities (WMHs) are one of the major components of small-vessel disease in aging and Alzheimer's disease (AD) populations that need to be assessed and monitored to estimate the vascular disease burden. In this thesis, a new fully automatic technique is proposed for segmenting WMHs from multiple contrasts of magnetic resonance (MR) brain images. The proposed segmentation technique uses a machine learning classification scheme by combining a set of intensity and location features obtained from multi-contrast MR sequences, namely T1w, T2w, proton density (PD) and fluid attenuated inversion recovery (FLAIR) images and a linear or nonlinear classifier to detect WMHs. The segmentations are performed in the native space of the optimal contrast (e.g. FLAIR or T2w) to avoid the blurring caused by resampling the images, especially since these images generally have relatively thick slices (3-5 mm). The classifiers are then trained on the training dataset with manually segmented labels. The performance of the classifiers is assessed using Dice Kappa values as the primary outcome measure and through a 10-fold cross validation scheme.Using the developed tool, the WMHs were segmented using different combinations of input image contrasts (i.e. T1w+T2w+PD, T1w+FLAIR, T1w) to assess the performance of the classifiers and the contribution of each of the contrasts in detecting WMHs. The question of interest was whether the WMHs loads obtained from segmentations based only on T1w images can be used as accurate estimates of the actual WMH loads. To assess this, the volumetric correlation of WMH loads in different brain lobes as well as correlation with age and cognitive measures were compared to investigate the effectiveness of each contrast in providing WMH load estimates that are highly correlated with aging and cognitive scores. The assessments revealed that the best Dice Kappa values are obtained while using the optimal FLAIR and T2w/PD contrasts. Classifications based solely on T1w images tend to undersegment the WMHs, only detecting the brightest of these lesions on FLAIR and T2w/PD images. However, the WMH loads obtained from T1w segmentations were still able to provide high correlations with age and cognitive scores. Finally, using the developed tool, baseline WMHs were segmented in an early stage Parkinson's disease (PD) database as well as age matched healthy controls. Using longitudinal clinical assessments and cortical thickness measures, we studied the relationship between baseline WMHs and future cognitive decline and cortical thinning. PD subjects with high WMH loads were found to present with more future cognitive decline and cortical thinning in comparison with (i) PD subjects with low WMH loads and (ii) age matched control subjects with high WMH loads. These findings show that the existence of WMHs affects PD patients differently from controls. " --
Author: David J. Werring Publisher: Cambridge University Press ISBN: 1139496212 Category : Medical Languages : en Pages : 207
Book Description
Stroke is a leading cause of death and disability throughout the world. About one in three symptomatic strokes are due to disease of small perforating arteries; however, most effective interventions are targeted at disease of large arteries. The underlying mechanisms and treatment of small vessel disease remain poorly understood. Microbleeds have emerged as a critical imaging marker of small vessel disease, being found in all types of stroke. With increasing evidence that microbleeds are caused by hypertensive arteriopathy and cerebral amyloid angiopathy, they are likely to play a strong future role in increasing our understanding of the causes of small vessel disease and the potential link between cerebrovascular disease and neurodegeneration. Cerebral Microbleeds summarizes our current knowledge, bringing together expert research from global authorities in the field. This authoritative and systematic text will be of interest to all clinical researchers and physicians in the fields of stroke and cognitive impairment.
Author: Sumeet Dua Publisher: Springer Science & Business Media ISBN: 3642400175 Category : Technology & Engineering Languages : en Pages : 334
Book Description
The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.
Author: Olivier Godefroy Publisher: Cambridge University Press ISBN: 1139461893 Category : Medical Languages : en Pages : 637
Book Description
The care of stroke patients has changed dramatically. As well as improvements in the emergency care of the condition, there have been marked advances in our understanding, management and rehabilitation of residual deficits. This book is about the care of stroke patients, focusing on behavioural and cognitive problems. It provides a comprehensive review of the field covering the diagnostic value of these conditions, in the acute and later phases, their requirements in terms of treatment and management and the likelihood and significance of long-term disability. This book will appeal to all clinicians involved in the care of stroke patients, as well as to neuropsychologists, other rehabilitation therapists and research scientists investigating the underlying neuroscience.
Author: Massimo Filippi Publisher: Springer Nature ISBN: 303038621X Category : Medical Languages : en Pages : 215
Book Description
This book provides cutting-edge information on the epidemiology, etiopathogenesis, clinical manifestations, diagnostic procedures and treatment approaches for the main white matter (WM) disorders of the central nervous system (CNS). WM lesions are associated with many neurological conditions, and with aging. The diagnostic work-up of neurological diseases characterized by the presence of these lesions has changed dramatically over the past few years. This is mainly due on the one hand to the discovery of specific pathogenetic factors in some of these conditions, and on the order to the optimized use of diagnostic tools. All of this has resulted in new diagnostic algorithms, and in the identification of new neurological conditions. The book offers neurologists essential guidance in the diagnosis and treatment of the most frequent WM conditions, promoting their correct and cost-saving diagnosis and management. By integrating neurological, laboratory and imaging concepts with the demands of accurate diagnosis, this reference guide provides a state-of-the-art overview of the current state of knowledge on these conditions, as well as practical guidelines for their diagnosis and treatment.
Author: Steven C. Cramer Publisher: Cambridge University Press ISBN: 1139490656 Category : Medical Languages : en Pages : 307
Book Description
Increasing evidence identifies the possibility of restoring function to the damaged brain via exogenous therapies. One major target for these advances is stroke, where most patients can be left with significant disability. Treatments have the potential to improve the victim's quality of life significantly and reduce the time and expense of rehabilitation. Brain Repair After Stroke reviews the biology of spontaneous brain repair after stroke in animal models and in humans. Detailed chapters cover the many forms of therapy being explored to promote brain repair and consider clinical trial issues in this context. This book provides a summary of the neurobiology of innate and treatment-induced repair mechanisms after hypoxia and reviews the state of the art for human therapeutics in relation to promoting behavioral recovery after stroke. Essential reading for stroke physicians, neurologists, rehabilitation physicians and neuropsychologists.
Author: Alexander Y. Lau
Author: Alexander Y. Lau
Author: Emanuele Trucco
Author: Mahsa Dadar
Author: Michael Brainin
Author: David J. Werring
Author: Sarah T. Pendlebury
Author: Sumeet Dua
Author: Olivier Godefroy
Author: Massimo Filippi
Author: Steven C. Cramer