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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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BACKGROUND: Although the assessment of right ventricular (RV) diastolic function is feasible, it has garnered far less momentum for use compared with its left ventricular counterpart. The scarcity of data defining normative RV diastolic function and the fact that implications of RV diastolic dysfunction in different disease states on outcomes are less well known both hinder integration into routine clinical assessment. The aim of this study was to establish normal values of RV diastolic parameters stratified by sex, age, and race using data from the World Alliance Societies of Echocardiography study. METHODS: A subset of 888 normal subjects from the World Alliance Societies of Echocardiography database were analyzed, including measurements of tricuspid valve (TV) inflow E- and A-wave velocities, E-wave deceleration time, and TV annular tissue Doppler e' and a' velocities. Additionally, right atrial (RA) maximal volume and RA peak reservoir strain were measured. Patients were grouped by age (<40, 41-65, and >65 years) and stratified by sex and race. Differences were analyzed using unpaired t tests. RESULTS: Compared with men, women had significantly higher TV e' and E-wave and A-wave velocities, though differences were modest. Increasing age was associated with stepwise lower TV E wave, e' velocity, and TV E/A ratio and higher a' velocity and E/e' ratio. RA peak reservoir strain was also lower, and RA end-systolic volume trended toward being smaller for older age groups. Asian subjects demonstrated significantly higher a' velocities, lower E wave, the smallest RA end-systolic volumes, and the lowest RA peak strain values compared with white subjects of both sexes. CONCLUSIONS: This study provides normal values for parameters used in the assessment of RV diastolic function stratified by race, sex, and age. The results demonstrate significant differences in RV diastolic parameters between age groups, which manifest in both individual parameters and composite ratios of TV inflow and annular velocities. Although limited sex- and race-related differences were also noted, age appears to have the most significant impact on RV diastolic parameters. These findings may aid in refining current normative values.
Assuntos
Ventrículos do Coração , Função Ventricular Direita , Idoso , Diástole , Ecocardiografia , Feminino , Ventrículos do Coração/diagnóstico por imagem , Humanos , Masculino , Valores de ReferênciaRESUMO
BACKGROUND: The high and increasing prevalence of Dilated Cardiomyopathy (DCM) represents a serious public health problem. New technologies are being used aiming at more accurate diagnoses in order to improve therapeutic approach. In this scenario, speckle tracking echocardiography (STE) uses natural myocardial markers to analyze the systolic deformation of the left ventricle (LV). OBJECTIVE: To measure the longitudinal transmural global strain (GS) of the LV through STE in patients with severe DCM, comparing the results with normal individuals and with echocardiographic parameters established for the analysis of LV systolic function, validating the method in this population. METHODS: We studied 71 patients with severe DCM (53 ± 12 years, 72% men) and 20 controls (30 ± 8 years, 45% men). We obtained LV volumes and ejection fraction by two and three-dimensional echocardiography, Doppler parameters, tissue Doppler and GS was obtained by STE. RESULTS: Compared to controls, LV volumes were higher in the DCM group; however, LVEF and peak velocity of E wave were lower in the latter. The myocardial performance index was higher among patients. Myocardial velocities at the tissue Doppler (S', e', a') were significantly lower and E/e' ratio was higher in the DCM group. The GS was decreased in the DCM group (-5.5% ± 2.3%) when compared to controls (-14.0% ± 1.8%). CONCLUSION: In this study, GS was significantly lower in patients with severe DCM, bringing new perspectives for therapeutic approaches in this specific population.
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Cardiomiopatia Dilatada/diagnóstico por imagem , Disfunção Ventricular Esquerda/diagnóstico por imagem , Adolescente , Adulto , Idoso , Cardiomiopatia Dilatada/fisiopatologia , Estudos de Casos e Controles , Ecocardiografia/métodos , Ecocardiografia Doppler , Ecocardiografia Tridimensional , Feminino , Ventrículos do Coração/diagnóstico por imagem , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Reprodutibilidade dos Testes , Índice de Gravidade de Doença , Adulto JovemRESUMO
FUNDAMENTO: A alta e crescente prevalência de Cardiomiopatia Dilatada (CMD) representa sério problema de saúde pública. Novas tecnologias vêm sendo utilizadas objetivando diagnósticos mais sofisticados, que melhorem a abordagem terapêutica. Nesse cenário, o Speckle Tracking (STE) utiliza marcadores miocárdicos naturais para analisar a deformação sistólica do Ventrículo Esquerdo (VE). OBJETIVO: Mensurar o strain transmural longitudinal global (SG) do VE através do STE em pacientes com CMD grave, comparando os resultados com indivíduos normais e com parâmetros ecocardiográficos consagrados para análise da função sistólica do VE, validando o método nessa população. MÉTODOS: Foram estudados 71 pacientes com CMD grave, (53 ± 12a, 72% homens) e 20 controles (30 ± 8a, 45% homens). Foram obtidos os volumes e a FEVE pela ecocardiografia bi e tridimensional, parâmetros do Doppler, Doppler tecidual e o SG pelo STE. RESULTADOS: Comparados ao grupo controle, os volumes do VE foram maiores no grupo CMD; entretanto, a FEVE e velocidade de pico da onda E foram menores neste último. O índice de performance miocárdica foi maior entre os pacientes. As velocidades do miocárdio pelo Doppler tecidual (S', e', a') foram consideravelmente menores e a relação E/e' foi maior no grupo CMD. O SG apresentou-se diminuído no grupo CMD (-5,5% ± 2,3%), em relação aos controles (-14,0% ± 1,8%). CONCLUSÃO: No presente estudo, o SG foi significativamente menor nos pacientes com CMD grave, abrindo novas perspectivas para abordagens terapêuticas nessa população específica.
BACKGROUND: The high and increasing prevalence of Dilated Cardiomyopathy (DCM) represents a serious public health problem. New technologies are being used aiming at more accurate diagnoses in order to improve therapeutic approach. In this scenario, speckle tracking echocardiography (STE) uses natural myocardial markers to analyze the systolic deformation of the left ventricle (LV). OBJECTIVE: To measure the longitudinal transmural global strain (GS) of the LV through STE in patients with severe DCM, comparing the results with normal individuals and with echocardiographic parameters established for the analysis of LV systolic function, validating the method in this population. METHODS: We studied 71 patients with severe DCM (53 ± 12 years, 72% men) and 20 controls (30 ± 8 years, 45% men). We obtained LV volumes and ejection fraction by two and three-dimensional echocardiography, Doppler parameters, tissue Doppler and GS was obtained by STE. RESULTS: Compared to controls, LV volumes were higher in the DCM group; however, LVEF and peak velocity of E wave were lower in the latter. The myocardial performance index was higher among patients. Myocardial velocities at the tissue Doppler (S', e', a') were significantly lower and E/e' ratio was higher in the DCM group. The GS was decreased in the DCM group (-5.5% ± 2.3%) when compared to controls (-14.0% ± 1.8%). CONCLUSION: In this study, GS was significantly lower in patients with severe DCM, bringing new perspectives for therapeutic approaches in this specific population.