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RESUMEN Introducción: El número creciente de estudios ecocardiográficos y la necesidad de cumplir rigurosamente con las recomendaciones de guías internacionales de cuantificación, ha llevado a que los cardiólogos deban realizar tareas sumamente extensas y repetitivas, como parte de la interpretación y análisis de cantidades de información cada vez más abrumadoras. Novedosas técnicas de machine learning (ML), diseñadas para reconocer imágenes y realizar mediciones en las vistas adecuadas, están siendo cada vez más utilizadas para responder a esta necesidad evidente de automatización de procesos. Objetivos: Nuestro objetivo fue evaluar un modelo alternativo de interpretación y análisis de estudios ecocardiográficos, basado fundamentalmente en la utilización de software de ML, capaz de identificar y clasificar vistas y realizar mediciones estandarizadas de forma automática. Material y métodos: Se utilizaron imágenes obtenidas en 2000 sujetos normales, libres de enfermedad, de los cuales 1800 fueron utilizados para desarrollar los algoritmos de ML y 200 para su validación posterior. Primero, una red neuronal convolucional fue desarrollada para reconocer 18 vistas ecocardiográficas estándar y clasificarlas de acuerdo con 8 grupos (stacks) temáticos. Los resultados de la identificación automática fueron comparados con la clasificación realizada por expertos. Luego, algoritmos de ML fueron desarrollados para medir automáticamente 16 parámetros de eco Doppler de evaluación clínica habitual, los cuales fueron comparados con las mediciones realizadas por un lector experto. Finalmente, comparamos el tiempo necesario para completar el análisis de un estudio ecocardiográfico con la utilización de métodos manuales convencionales, con el tiempo necesario con el empleo del modelo que incorpora ML en la clasificación de imágenes y mediciones ecocardiográficas iniciales. La variabilidad inter e intraobservador también fue analizada. Resultados: La clasificación automática de vistas fue posible en menos de 1 segundo por estudio, con una precisión de 90 % en imágenes 2D y de 94 % en imágenes Doppler. La agrupación de imágenes en stacks tuvo una precisión de 91 %, y fue posible completar dichos grupos con las imágenes necesarias en 99% de los casos. La concordancia con expertos fue excelente, con diferencias similares a las observadas entre dos lectores humanos. La incorporación de ML en la clasificación y medición de imágenes ecocardiográficas redujo un 41 % el tiempo de análisis y demostró menor variabilidad que la metodología de interpretación convencional. Conclusión: La incorporación de técnicas de ML puede mejorar significativamente la reproducibilidad y eficiencia de las interpretaciones y mediciones ecocardiográficas. La implementación de este tipo de tecnologías en la práctica clínica podría resultar en reducción de costos y aumento en la satisfacción del personal médico.
ABSTRACT Background: The growing number of echocardiographic tests and the need for strict adherence to international quantification guidelines have forced cardiologists to perform highly extended and repetitive tasks when interpreting and analyzing increasingly overwhelming amounts of data. Novel machine learning (ML) techniques, designed to identify images and perform measurements at relevant visits, are becoming more common to meet this obvious need for process automation. Objectives: Our objective was to evaluate an alternative model for the interpretation and analysis of echocardiographic tests mostly based on the use of ML software in order to identify and classify views and perform standardized measurements automatically. Methods: Images came from 2000 healthy subjects, 1800 of whom were used to develop ML algorithms and 200 for subsequent validation. First, a convolutional neural network was developed in order to identify 18 standard echocardiographic views and classify them based on 8 thematic groups (stacks). The results of automatic identification were compared to classification by experts. Later, ML algorithms were developed to automatically measure 16 Doppler scan parameters for regular clinical evaluation, which were compared to measurements by an expert reader. Finally, we compared the time required to complete the analysis of an echocardiographic test using conventional manual methods with the time needed when using the ML model to classify images and perform initial echocardiographic measurements. Inter- and intra-observer variability was also analyzed. Results: Automatic view classification was possible in less than 1 second per test, with a 90% accuracy for 2D images and a 94% accuracy for Doppler scan images. Stacking images had a 91% accuracy, and it was possible to complete the groups with any necessary images in 99% of cases. Expert agreement was outstanding, with discrepancies similar to those found between two human readers. Applying ML to echocardiographic imaging classification and measurement reduced time of analysis by 41% and showed lower variability than conventional reading methods. Conclusion: Application of ML techniques may significantly improve reproducibility and efficiency of echocardiographic interpretations and measurements. Using this type of technologies in clinical practice may lead to reduced costs and increased medical staff satisfaction.
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INTRODUCTION: Neglected tropical diseases are a group of communicable diseases that occur in tropical and subtropical conditions and are closely related to poverty and inadequate sanitation conditions. Among these entities, chikungunya remains one of the most widely spread diseases. Although the main symptoms are related to a febrile syndrome, cardiovascular (CV) involvement has been reported, with short- and long-term implications. As part of the "Neglected Tropical Diseases and other Infectious Diseases involving the Heart" (NET-Heart) Project, the aim of this review is to compile all the information available regarding CV involvement of this disease, to help healthcare providers gain knowledge in this field, and contribute to improving early diagnosis, treatment, and prevention strategies. METHODS: We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement in conducting and reporting this systematic review. The search was conducted using MEDLINE/PubMed, SciELO, and LILACS databases to identify any relevant studies or reviews detailing an association between chikungunya and cardiac involvement published from January 1972 to May 31, 2020. RESULTS: Despite its mechanism not being fully understood, CV involvement has been described as the most frequent atypical presentation of chikungunya (54.2%). Myocarditis is the most prevalent CV complication. Different rhythm disturbances have been reported in 52% of cases, whereas heart failure was reported in 15% of cases, pericarditis in 5%, and acute myocardial infarction in 2%. Overall estimated CV mortality is 10%, although in patients with other comorbidities, it may increase up to 20%. In the proper clinical setting, the presence of fever, polyarthralgia, and new-onset arrhythmia suggests chikungunya virus-related myocarditis. CONCLUSION: Although most cases are rarely fatal, CV involvement in chikungunya infection remains the most frequent atypical presentation of this disease and may have severe manifestations. Timely diagnosis and appropriate management are necessary to improve patient outcomes.
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Febre de Chikungunya , Miocardite , Pericardite , Febre de Chikungunya/complicações , Febre de Chikungunya/diagnóstico , Febre de Chikungunya/epidemiologia , Comorbidade , Febre , Humanos , Miocardite/epidemiologiaRESUMO
Relapsing polychondritis (RP) is a rare multisystem disease characterized by inflammation in cartilaginous structures and other connective tissues throughout the body, affecting the ears, nose, eyes, joints, respiratory tract, heart, and blood vessels. Cardiovascular involvement is the second most common cause of mortality after laryngotracheal involvement.[1] Here, we report a successful surgical case of RP in which the patient underwent aortic and mitral valve replacement and concomitant coronary artery bypass grafting.