Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment

Because coronary artery calcified plaques can hinder or eliminate stent deployment, interventional cardiologists need a better way to plan interventions, which might include one of the many methods for calcification modification (e.g., atherectomy). We are imaging calcifications with intravascular optical coherence tomography (IVOCT), which is the lone intravascular imaging technique with the ability to image the extent of a calcification, and using results to build vessel-specific finite element models for stent deployment. We applied methods to a large set of image data (\u3e45 lesions and \u3e 2,600 image frames) of calcified plaques, manually segmented by experts into calcified, lumen and “other” tissue classes. In optimization experiments, we evaluated anatomical (x, y) versus acquisition (r,θ) views, augmentation methods, and classification noise cleaning. Noisy semantic segmentations are cleaned by applying a conditional random field (CRF). We achieve an accuracy of 0.85 ± 0.04, 0.99 ± 0.01, and 0.97 ± 0.01, and F-score of 0.88 ± 0.07, 0.97 ± 0.01, and 0.91 ± 0.04 for calcified, lumen, and other tissues classes respectively across all folds following CRF noise cleaning. As a proof of concept, we applied our methods to cadaver heart experiments on highly calcified plaques. Following limited manual correction, we used our calcification segmentations to create a lesion-specific finite element model (FEM) and used it to predict direct stenting deployment at multiple pressure steps. FEM modeling of stent deployment captured many features found in the actual stent deployment (e.g., lumen shape, lumen area, and location and number of apposed stent struts)

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries. Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,theta) images. Data were augmented in a natural way by changing theta in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837+/-0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0+/-0.3%, Dice: 0.846+/-0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95+/-20.73 um), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9+/-128.0 deg / 202.0+/-121.1 deg). Our method will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.Comment: 24 pages, 9 figures, 2 tables, 2 supplementary figures, 3 supplementary 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

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine

Advanced Photothermal Optical Coherence Tomography (PT-OCT) for Quantification of Tissue Composition

Optical coherence tomography (OCT) is an imaging technique that forms 2D or 3D images of tissue structures with micron-level resolution. Today, OCT systems are widely used in medicine, especially in the fields of ophthalmology, interventional cardiology, oncology, and dermatology. Although OCT images provide insightful structural information of tissues, these images are not specific to the chemical composition of the tissue. Yet, chemical tissue composition is frequently relevant to the stage of a disease (e.g., atherosclerosis), leading to poor diagnostic performance of structural OCT images. Photo-thermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the potential to overcome this shortcoming by overlaying the 3D structural images of OCT with depth-resolved light absorption information. Potentially, signal analysis of the light absorption maps can be used to obtain refined insight into the chemical composition of tissue. Such analysis, however, is complex because the underlying physics of PT-OCT is multifactorial. Aside from tissue chemical composition, the optical, thermal, and mechanical properties of tissue affect PT-OCT signals; system/instrumentation parameters also influence PT-OCT signals. As such, obtaining refined insight into tissue chemical composition requires in-depth research aimed at answering several key unknowns and questions about this technique. The goal of this dissertation is to generate in-depth knowledge on sample and system parameters affecting PT-OCT signals, to develop strategies for optimal detection of a molecule of interest (MOI) and potentially for its quantification, and to improve the imaging rate of the system. The following items are major outcomes of this dissertation: 1- Generated comprehensive theory that discovers relations between sample/tissue properties and experimental conditions and their multifactorial effects on PT-OCT signals. 2- Developed system and experimentation strategies for detection of multiple molecules of interest with high specificity. 3- Generated optimized machine learning-powered model, in light of the above two outcomes, for automated depth-resolved interpretation of tissue composition from PT-OCT images. 4- Increased the imaging rate of PT-OCT by orders of magnitude by introducing a new variant of PT-OCT based on pulsed photothermal excitation. 5- Developed algorithms for signal denoising and improving the quality of received signals and the contrast in images which in return enables faster PT-OCT imaging

Advanced Photothermal Optical Coherence Tomography (PT-OCT) for Quantification of Tissue Composition

Optical coherence tomography (OCT) is an imaging technique that forms 2D or 3D images of tissue structures with micron-level resolution. Today, OCT systems are widely used in medicine, especially in the fields of ophthalmology, interventional cardiology, oncology, and dermatology. Although OCT images provide insightful structural information of tissues, these images are not specific to the chemical composition of the tissue. Yet, chemical tissue composition is frequently relevant to the stage of a disease (e.g., atherosclerosis), leading to poor diagnostic performance of structural OCT images. Photo-thermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the potential to overcome this shortcoming by overlaying the 3D structural images of OCT with depth-resolved light absorption information. Potentially, signal analysis of the light absorption maps can be used to obtain refined insight into the chemical composition of tissue. Such analysis, however, is complex because the underlying physics of PT-OCT is multifactorial. Aside from tissue chemical composition, the optical, thermal, and mechanical properties of tissue affect PT-OCT signals; system/instrumentation parameters also influence PT-OCT signals. As such, obtaining refined insight into tissue chemical composition requires in-depth research aimed at answering several key unknowns and questions about this technique. The goal of this dissertation is to generate in-depth knowledge on sample and system parameters affecting PT-OCT signals, to develop strategies for optimal detection of a molecule of interest (MOI) and potentially for its quantification, and to improve the imaging rate of the system. The following items are major outcomes of this dissertation: 1- Generated comprehensive theory that discovers relations between sample/tissue properties and experimental conditions and their multifactorial effects on PT-OCT signals. 2- Developed system and experimentation strategies for detection of multiple molecules of interest with high specificity. 3- Generated optimized machine learning-powered model, in light of the above two outcomes, for automated depth-resolved interpretation of tissue composition from PT-OCT images. 4- Increased the imaging rate of PT-OCT by orders of magnitude by introducing a new variant of PT-OCT based on pulsed photothermal excitation. 5- Developed algorithms for signal denoising and improving the quality of received signals and the contrast in images which in return enables faster PT-OCT imaging