104 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario
[ES]Antecedentes: Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial. Uno de los campos de la inteligencia artificial con mayor aplicación a día de hoy en medicina es el de la predicción, recomendación o diagnóstico, donde se aplican
las técnicas machine learning. Asimismo, existe un creciente interés en las técnicas de medicina de precisión, donde las técnicas machine learning pueden ofrecer atención médica individualizada a cada paciente. El intervencionismo coronario percutáneo (ICP) con stent se ha convertido en una
práctica habitual en la revascularización de los vasos coronarios con enfermedad aterosclerótica obstructiva significativa. El ICP es asimismo patrón oro de tratamiento en pacientes con infarto agudo de miocardio; reduciendo las tasas de muerte e isquemia recurrente en comparación con el tratamiento médico. El éxito a largo plazo del procedimiento está limitado por la reestenosis del stent, un proceso patológico que provoca un estrechamiento arterial recurrente en el sitio de la ICP. Identificar qué
pacientes harán reestenosis es un desafío clínico importante; ya que puede manifestarse como un nuevo infarto agudo de miocardio o forzar una nueva resvascularización del vaso afectado, y que en casos de reestenosis recurrente representa un reto terapéutico.
Objetivos: Después de realizar una revisión de las técnicas de inteligencia artificial aplicadas a la medicina y con mayor profundidad, de las técnicas machine learning aplicadas a la cardiología, el objetivo principal de esta tesis doctoral ha sido desarrollar un modelo machine learning para predecir la aparición de reestenosis en pacientes con infarto agudo de miocardio sometidos a ICP con implante de un stent. Asimismo, han sido objetivos secundarios comparar el modelo desarrollado con machine learning con
los scores clásicos de riesgo de reestenosis utilizados hasta la fecha; y desarrollar un software que permita trasladar esta contribución a la práctica clínica diaria de forma sencilla. Para desarrollar un modelo fácilmente aplicable, realizamos nuestras predicciones sin variables adicionales a las obtenidas en la práctica rutinaria. Material: El conjunto de datos, obtenido del ensayo GRACIA-3, consistió en 263
pacientes con características demográficas, clínicas y angiográficas; 23 de ellos presentaron reestenosis a los 12 meses después de la implantación del stent. Todos los desarrollos llevados a cabo se han hecho en Python y se ha utilizado computación en la nube, en concreto AWS (Amazon Web Services).
Metodología: Se ha utilizado una metodología para trabajar con conjuntos de datos pequeños y no balanceados, siendo importante el esquema de validación cruzada anidada utilizado, así como la utilización de las curvas PR (precision-recall, exhaustividad-sensibilidad), además de las curvas ROC, para la interpretación de los modelos. Se han entrenado los algoritmos más habituales en la literatura para elegir el que mejor comportamiento ha presentado. Resultados: El modelo con mejores resultados ha sido el desarrollado con un clasificador extremely randomized trees; que superó significativamente (0,77; área
bajo la curva ROC a los tres scores clínicos clásicos; PRESTO-1 (0,58), PRESTO-2 (0,58) y TLR (0,62). Las curvas exhaustividad sensibilidad ofrecieron una imagen más precisa del rendimiento del modelo extremely randomized trees que muestra un algoritmo eficiente (0,96) para no reestenosis, con alta exhaustividad y alta sensibilidad. Para un umbral considerado óptimo, de 1,000 pacientes sometidos a
implante de stent, nuestro modelo machine learning predeciría correctamente 181 (18%) más casos en comparación con el mejor score de riesgo clásico (TLR). Las variables más importantes clasificadas según su contribución a las predicciones fueron diabetes, enfermedad coronaria en 2 ó más vasos, flujo TIMI post-ICP, plaquetas anormales, trombo post-ICP y colesterol anormal. Finalmente, se ha desarrollado una
calculadora para trasladar el modelo a la práctica clínica. La calculadora permite estimar el riesgo individual de cada paciente y situarlo en una zona de riesgo, facilitando la toma de decisión al médico en cuanto al seguimiento adecuado para el mismo. Conclusiones: Aplicado inmediatamente después de la implantación del stent, un modelo machine learning diferencia mejor a aquellos pacientes que presentarán o no reestenosis respecto a los discriminadores clásicos actuales
Blood
This book examines both the fluid and cellular components of blood. After the introductory section, the second section presents updates on various topics in hemodynamics. Chapters in this section discuss anemia, 4D flow MRI in cardiology, cardiovascular complications of robot-assisted laparoscopic pelvic surgery, altered perfusion in multiple sclerosis, and hemodynamic laminar shear stress in oxidative homeostasis. The third section focuses on thalassemia with chapters on diagnosis and screening for thalassemia, high blood pressure in beta-thalassemia, and hepatitis C infection in thalassemia patients
Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan
INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar
ligament on MRI between male and female. The specific objectives are to assess the prevalence
of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and
signal homogeneity and to find differences in alar ligament signal intensity between male and
female. This study also aims to determine the association between the heights of respondents
with alar ligament signal intensity and dimensions.
MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner
Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar
ligament is depicted in 3 planes and the visualization and variability of the ligament courses,
shapes and signal intensity characteristics were determined. The alar ligament dimensions were
also measured.
RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial
planes. The orientations were laterally ascending in most of the subjects (60%), predominantly
oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar
ligament signal intensity between male and female respondents. No significant association was
found between the heights of the respondents with alar ligament signal intensity and dimensions.
CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal
plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as
depicted in our data shows that caution needs to be exercised when evaluating alar ligament,
especially during circumstances of injury
CT Scanning
Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society
Case series of breast fillers and how things may go wrong: radiology point of view
INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging
breast due to breastfeeding or aging as well as small breast size. Recent years have shown the
emergence of a variety of injectable materials on market as breast fillers. These injectable
breast fillers have swiftly gained popularity among women, considering the minimal
invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know
that the procedure may pose detrimental complications, while visualization of breast
parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic
challenges. We present a case series of three patients with prior history of hyaluronic acid and
collagen breast injections.
REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening
shortness of breath, non-productive cough, central chest pain; associated with fever and chills
for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever
and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases
revealed non thrombotic wedge-shaped peripheral air-space densities.
The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2-
weeks duration. Previous collagen breast injection performed 1 year ago had impeded
sonographic visualization of the breast parenchyma. MRI breasts showed multiple non-
enhancing round and oval shaped lesions exhibiting fat intensity.
CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well
as limitations of imaging posed by breast fillers such that MRI is required as problem-solving
tool
Intracardiac Ultrasound Guided Systems for Transcatheter Cardiac Interventions
Transcatheter cardiac interventions are characterized by their percutaneous nature, increased patient safety, and low hospitalization times. Transcatheter procedures involve two major stages: navigation towards the target site and the positioning of tools to deliver the therapy, during which the interventionalists face the challenge of visualizing the anatomy and the relative position of the tools such as a guidewire. Fluoroscopic and transesophageal ultrasound (TEE) imaging are the most used techniques in cardiac procedures; however, they possess the disadvantage of radiation exposure and suboptimal imaging. This work explores the potential of intracardiac ultrasound (ICE) within an image guidance system (IGS) to facilitate the two stages of cardiac interventions. First, a novel 2.5D side-firing, conical Foresight ICE probe (Conavi Medical Inc., Toronto) is characterized, calibrated, and tracked using an electromagnetic sensor. The results indicate an acceptable tracking accuracy within some limitations. Next, an IGS is developed for navigating the vessels without fluoroscopy. A forward-looking, tracked ICE probe is used to reconstruct the vessel on a phantom which mimics the ultrasound imaging of an animal vena cava. Deep learning methods are employed to segment the complex vessel geometry from ICE imaging for the first time. The ICE-reconstructed vessel showed a clinically acceptable range of accuracy. Finally, a guidance system was developed to facilitate the positioning of tools during a tricuspid valve repair. The designed system potentially facilitates the positioning of the TriClip at the coaptation gap by pre-mapping the corresponding site of regurgitation in 3D tracking space
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