Machine Learning And Quantitative Neuroimaging In Epilepsy And Low Field Mri

Medical imaging plays a key role in the diagnosis and management of neurological disorders. Magnetic resonance imaging (MRI) has proven particularly useful, as it produces high resolution images with excellent tissue contrast, permitting clinicians to identify lesions and select appropriate treatments. However, demand for MRI services has outpaced the availability of qualified experts to operate, maintain, and interpret images from these devices. Radiologists often rely on time-consuming manual analyses, which further limits throughput. Moreover, a large portion of the world’s population cannot currently access MRI, and demand for medical imaging services will continue to increase as healthcare quality improves globally. To address these challenges, we must find innovative ways to automate medical processing and produce lower-cost medical imaging devices. Recent advances in deep learning and low-field MRI hardware offer potential solutions, providing lower-cost methods for processing and collecting images, respectively. This thesis aims to develop and validate lower-cost methods for collecting and interpreting neuroimaging using machine learning algorithms and portable, low-field MRI technology. In the first section, I develop a deep learning algorithm that automatically segments resection cavities in epilepsy surgery patients and quantifies removed tissues. I also compare the impacts of epilepsy surgery on remote brain regions, demonstrating that more selective procedures minimize postoperative cortical thinning. In the second section, I explore and validate clinical applications for a new portable, low-field MRI device. Using open-source imaging and machine learning, I propose a low-cost method for simulating diagnostic performance for novel imaging devices when only sparse data is available. Additionally, I validate device performance in multiple sclerosis by directly comparing the low-field device to standard-of-care imaging using a range of manual and automated analyses. My hope is that machine learning and low-field MRI will increase medical imaging access and improve patient care worldwide

Pilot\u27s Handbook of Aeronautical Knowledge, 2016

The Pilot’s Handbook of Aeronautical Knowledge provides basic knowledge that is essential for pilots. This handbook introduces pilots to the broad spectrum of knowledge that will be needed as they progress in their pilot training. Except for the Code of Federal Regulations pertinent to civil aviation, most of the knowledge areas applicable to pilot certification are presented. The Pilot’s Handbook of Aeronautical Knowledge provides basic knowledge for the student pilot learning to fly, as well as pilots seeking advanced pilot certification. For detailed information on a variety of specialized flight topics, see specific Federal Aviation Administration (FAA) handbooks and Advisory Circulars (ACs). Occasionally the word “must” or similar language is used where the desired action is deemed critical. The use of such language is not intended to add to, interpret, or relieve a duty imposed by Title 14 of the Code of Federal Regulations (14 CFR). It is essential for persons using this handbook to become familiar with and apply the pertinent parts of 14 CFR and the Aeronautical Information Manual (AIM). The AIM is available online at www.faa.gov. The current Flight Standards Service airman training and testing material and learning statements for all airman certificates and ratings can be obtained from https://www.faa.gov

Pilot\u27s Handbook of Aeronautical Knowledge, 2016

The Pilot’s Handbook of Aeronautical Knowledge provides basic knowledge that is essential for pilots. This handbook introduces pilots to the broad spectrum of knowledge that will be needed as they progress in their pilot training. Except for the Code of Federal Regulations pertinent to civil aviation, most of the knowledge areas applicable to pilot certification are presented. The Pilot’s Handbook of Aeronautical Knowledge provides basic knowledge for the student pilot learning to fly, as well as pilots seeking advanced pilot certification. For detailed information on a variety of specialized flight topics, see specific Federal Aviation Administration (FAA) handbooks and Advisory Circulars (ACs). Occasionally the word “must” or similar language is used where the desired action is deemed critical. The use of such language is not intended to add to, interpret, or relieve a duty imposed by Title 14 of the Code of Federal Regulations (14 CFR). It is essential for persons using this handbook to become familiar with and apply the pertinent parts of 14 CFR and the Aeronautical Information Manual (AIM). The AIM is available online at www.faa.gov. The current Flight Standards Service airman training and testing material and learning statements for all airman certificates and ratings can be obtained from https://www.faa.gov

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations