Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

Background: The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. Methods: In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. Results: The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. Discussion: The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches

Cardiovascular disease (CVD) is the leading cause of mortality and disability in developed countries. According to WHO, an estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to major adverse cardiac and cerebral events. Early detection and care for individuals at high risk could save lives, alleviate suffering, and diminish economic burden associated with these diseases. Carotid artery disease is not only a well-established risk factor for ischemic stroke, contributing to 10%–20% of strokes or transient ischemic attacks (TIAs), but it is also a surrogate marker of generalized atherosclerosis and a predictor of cardiovascular events. In addition to diligent history, physical examination, and laboratory detection of metabolic abnormalities leading to vascular changes, imaging of carotid arteries adds very important information in assessing stroke and overall cardiovascular risk. Spanning from carotid intima-media thickness (IMT) measurements in arteriopathy to plaque burden, morphology and biology in more advanced disease, imaging of carotid arteries could help not only in stroke prevention but also in ameliorating cardiovascular events in other territories (e.g. in the coronary arteries). While ultrasound is the most widely available and affordable imaging methods, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), their combination and other more sophisticated methods have introduced novel concepts in detection of carotid plaque characteristics and risk assessment of stroke and other cardiovascular events. However, in addition to robust progress in usage of these methods, all of them have limitations which should be taken into account. The main purpose of this consensus document is to discuss pros but also cons in clinical, epidemiological and research use of all these techniques

Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia

Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality

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

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces