In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging

La osteoporosis es una enfermedad ósea que se manifiesta con una menor densidad ósea y el deterioro de la arquitectura del hueso esponjoso. Ambos factores aumentan la fragilidad ósea y el riesgo de sufrir fracturas óseas, especialmente en mujeres, donde existe una alta prevalencia. El diagnóstico actual de la osteoporosis se basa en la cuantificación de la densidad mineral ósea (DMO) mediante la técnica de absorciometría dual de rayos X (DXA). Sin embargo, la DMO no puede considerarse de manera aislada para la evaluación del riesgo de fractura o los efectos terapéuticos. Existen otros factores, tales como la disposición microestructural de las trabéculas y sus características que es necesario tener en cuenta para determinar la calidad del hueso y evaluar de manera más directa el riesgo de fractura. Los avances técnicos de las modalidades de imagen médica, como la tomografía computarizada multidetector (MDCT), la tomografía computarizada periférica cuantitativa (HR-pQCT) y la resonancia magnética (RM) han permitido la adquisición in vivo con resoluciones espaciales elevadas. La estructura del hueso trabecular puede observarse con un buen detalle empleando estas técnicas. En particular, el uso de los equipos de RM de 3 Teslas (T) ha permitido la adquisición con resoluciones espaciales muy altas. Además, el buen contraste entre hueso y médula que proporcionan las imágenes de RM, así como la utilización de radiaciones no ionizantes sitúan a la RM como una técnica muy adecuada para la caracterización in vivo de hueso trabecular en la enfermedad de la osteoporosis. En la presente tesis se proponen nuevos desarrollos metodológicos para la caracterización morfométrica y mecánica del hueso trabecular en tres dimensiones (3D) y se aplican a adquisiciones de RM de 3T con alta resolución espacial. El análisis morfométrico está compuesto por diferentes algoritmos diseñados para cuantificar la morfología, la complejidad, la topología y los parámetros de anisotropía del tejido trabecular. En cuanto a la caracterización mecánica, se desarrollaron nuevos métodos que permiten la simulación automatizada de la estructura del hueso trabecular en condiciones de compresión y el cálculo del módulo de elasticidad. La metodología desarrollada se ha aplicado a una población de sujetos sanos con el fin de obtener los valores de normalidad del hueso esponjoso. Los algoritmos se han aplicado también a una población de pacientes con osteoporosis con el fin de cuantificar las variaciones de los parámetros en la enfermedad y evaluar las diferencias con los resultados obtenidos en un grupo de sujetos sanos con edad similar.Los desarrollos metodológicos propuestos y las aplicaciones clínicas proporcionan resultados satisfactorios, presentando los parámetros una alta sensibilidad a variaciones de la estructura trabecular principalmente influenciadas por el sexo y el estado de enfermedad. Por otra parte, los métodos presentan elevada reproducibilidad y precisión en la cuantificación de los valores morfométricos y mecánicos. Estos resultados refuerzan el uso de los parámetros presentados como posibles biomarcadores de imagen en la enfermedad de la osteoporosis.Alberich Bayarri, Á. (2010). In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8981Palanci

Információkinyerés magyar nyelvű gerinc MR leletekből

Cikkünkben magyar nyelvű radiológiai leletek automatikus feldolgozásának módszeréről és kezdeti kísérleteink eredményeiről számolunk be. Először bemutatjuk a felhasznált adatbázist és az alkalmazott annotációs elveket, majd ismertetjük kísérleti módszereinket. Bemutatjuk eredményeinket, ezt követően pedig ismertetjük a rendszer jelenlegi erősségeit és gyengébb pontjait, végül szót ejtünk a továbbfejlesztési lehetőségekről is

Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine

Artificial Intelligence: Development and Applications in Neurosurgery

The last decade has witnessed a significant increase in the relevance of artificial intelligence (AI) in neuroscience. Gaining notoriety from its potential to revolutionize medical decision making, data analytics, and clinical workflows, AI is poised to be increasingly implemented into neurosurgical practice. However, certain considerations pose significant challenges to its immediate and widespread implementation. Hence, this chapter will explore current developments in AI as it pertains to the field of clinical neuroscience, with a primary focus on neurosurgery. Additionally included is a brief discussion of important economic and ethical considerations related to the feasibility and implementation of AI-based technologies in neurosciences, including future horizons such as the operational integrations of human and non-human capabilities

Extraction and Analysis of Data for Fragility Fracture Patients to Help Determine the Likelihood of Follow-Up With a West Michigan Fragility Fracture Service

Fragility fractures and secondary fractures are a large physical, financial, and emotional drain on the individuals and families affected. Improving post fragility fracture care and increasing follow-up rates with specialty services, such as a fracture liaison service, may promote post-fracture recovery and help prevent future fractures. The purpose of this doctoral project was to increase and improve post osteoporotic fracture care by increasing appropriate referrals and follow-up care. Patient data was retrieved and analyzed on 60 fragility fracture patients referred to a local fracture liaison service. The analysis found that patient gender, fracture site, and history of a previous fracture all played a significant role as to whether the patient would follow-up with a fracture liaison service. The Health Belief Model and the Donabedian model were used to help drive this project and provide structure to the next steps in the project. To make the results relevant to primary care providers, local outcome data was used to provider a presentation to the group. The primary care providers then completed a survey answering questions based on their impression of the patient data presentation. The provider survey response data was then analyzed for trends. Most notably, the vast majority of providers agreed that after the presentation, they had a greater understanding of the patients that were most likely to receive inadequate fragility fracture care or refuse follow-up with a fracture liaison service. This implies that the providers now have a raised awareness about these patients and fewer patients will fall through the cracks when it comes to fragility fracture care and fracture liaison service follow-up. This project format could be applied to other populations to assist in revealing each unique population’s characteristics that impact fragility fracture care and fracture liaison service follow-up. Those results could then be presented to local primary care providers to raise awareness and improve their local outcomes

Monitoring Bone Micro-architecture with a Special Focus on Bone Strength

Introduction. Osteoporosis is a chronic disease characterized by the loss of bone mass and the deterioration of bone micro-architecture leading to a subsequent increase in fracture risk. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides non-invasive measures of bone micro-architecture and strength in live humans but its ability to monitor small skeletal changes is yet poorly understood. The objectives of this thesis were to 1) determine HR-pQCT precision for volumetric density, geometry, cortical and trabecular micro-architecture, as well as estimates of bone strength; 2) determine the monitoring time interval (MTI) and least significant change (LSC) metrics; and 3) to characterize annual changes in bone area, density, and micro-architecture at the distal radius and tibia using HR-pQCT in postmenopausal women. Methods. To determine precision error as well as monitoring and change metrics of the distal radius and tibia, 34 postmenopausal women (mean age 74, SD±7 years) from the Saskatoon cohort of the Canadian Multicentre Osteoporosis Study (CaMos) were measured using HR-pQCT. To characterize the annual change in bone outcomes of this same cohort, 51 women (mean age±SD: 77±7 years) were measuring at baseline and again 1 year later. Precision errors were calculated as coefficient of variation (CV% and CV%RMS). The LSC was determined from precision errors and then divided by the median annual percent changes to define MTIs for bone area, density, and micro-architecture. Repeated measures analysis of variance (ANOVA) with Bonferroni adjustment for multiple comparisons were used to characterize the mean annual change in total density, cortical perimeter, trabecular and cortical bone area, density, content, and micro-architecture. Significant changes were accepted at P<0.05. Results and Discussion. HR-pQCT precision errors were 3 years), while micro-architecture had monitoring times of ~2 years. The observed annual changes were statistically significant for several outcomes; however, only cortical and trabecular area, as well as cortical density at the distal tibia changed beyond the LSC. Overall, thesis findings will assist design and interpretation of prospective HR-pQCT studies in postmenopausal women