Optimización de la interpretación de ecocardiogramas con la utilización de Machine Learning en el estudio WASE / Optimizing Echocardiograms Interpretation with the Application of Machine Learning in the WASE Study

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.

Deep learning assisted measurement of echocardiographic left heart parameters: improvement in interobserver variability and workflow efficiency.

Machine learning techniques designed to recognize views and perform measurements are increasingly used to address the need for automation of the interpretation of echocardiographic images. The current study was designed to determine whether a recently developed and validated deep learning (DL) algorithm for automated measurements of echocardiographic parameters of left heart chamber size and function can improve the reproducibility and shorten the analysis time, compared to the conventional methodology. The DL algorithm trained to identify standard views and provide automated measurements of 20 standard parameters, was applied to images obtained in 12 randomly selected echocardiographic studies. The resultant measurements were reviewed and revised as necessary by 10 independent expert readers. The same readers also performed conventional manual measurements, which were averaged and used as the reference standard for the DL-assisted approach with and without the manual revisions. Inter-reader variability was quantified using coefficients of variation, which together with analysis times, were compared between the conventional reads and the DL-assisted approach. The fully automated DL measurements showed good agreement with the reference technique: Bland-Altman biases 0-14% of the measured values. Manual revisions resulted in only minor improvement in accuracy: biases 0-11%. This DL-assisted approach resulted in a 43% decrease in analysis time and less inter-reader variability than the conventional methodology: 2-3 times smaller coefficients of variation. In conclusion, DL-assisted approach to analysis of echocardiographic images can provide accurate left heart measurements with the added benefits of improved reproducibility and time savings, compared to conventional methodology.

Normative values of the aortic valve area and Doppler measurements using two-dimensional transthoracic echocardiography: results from the Multicentre World Alliance of Societies of Echocardiography Study.

AIMS: Aortic valve area (AVA) used for echocardiographic assessment of aortic stenosis (AS) has been traditionally interpreted independently of sex, age and race. As differences in normal values might impact clinical decision-making, we aimed to establish sex-, age- and race-specific normative values for AVA and Doppler parameters using data from the World Alliance Societies of Echocardiography (WASE) Study. METHODS AND RESULTS: Two-dimensional transthoracic echocardiographic studies were obtained from 1903 healthy adult subjects (48% women). Measurements of the left ventricular outflow tract (LVOT) diameter and Doppler parameters, including AV and LVOT velocity time integrals (VTIs), AV mean pressure gradient, peak velocity, were obtained according to ASE/EACVI guidelines. AVA was calculated using the continuity equation. Compared with men, women had smaller LVOT diameters and AVA values, and higher AV peak velocities and mean gradients (all P < 0.05). LVOT and AV VTI were significantly higher in women (P < 0.05), and both parameters increased with age in both sexes. AVA differences persisted after indexing to body surface area. According to the current diagnostic criteria, 13.5% of women would have been considered to have mild AS and 1.4% moderate AS. LVOT diameter and AVA were lower in older subjects, both men and women, and were lower in Asians, compared with whites and blacks. CONCLUSION: WASE data provide clinically relevant information about significant differences in normal AVA and Doppler parameters according to sex, age, and race. The implementation of this information into clinical practice should involve development of specific normative values for each ethnic group using standardized methodology.

Normal Values of Left Atrial Size and Function and the Impact of Age: Results of the World Alliance Societies of Echocardiography Study.

BACKGROUND: Left atrial (LA) evaluation includes volumetric and functional parameters with an abundance of diagnostic and prognostic implications. Solid normal reference ranges are compulsory for accurate interpretation in individual patients, but previous studies have yielded mixed conclusions regarding the effects of age, sex, and/or race. The present report from the World Alliance Societies of Echocardiography study focuses on two-dimensional (2D) and three-dimensional (3D) measures of LA structure and function, with subgroup analysis by age, sex, and race. METHODS: Transthoracic 2D and 3D echocardiographic images were obtained in 1,765 healthy individuals (901 men, 864 women) evenly distributed among age subgroups: 18 to 40 years (n = 745), 41 to 65 years (n = 618), and >65 years (n = 402); the racial distribution was 38.4% white, 39.9% Asian, and 9.7% black. Images were analyzed using dedicated LA analysis software to measure LA volumes and phasic function from 3D volume and 2D strain curves. RESULTS: Three-dimensional maximum and minimum LA volumes adjusted for body surface area were nearly identical for men and women, but women demonstrated higher 3D total and passive emptying fractions (EFs). Two-dimensional reservoir strain was similar for both sexes. Age was associated with an incremental rise in LA volumes alongside characteristic shifts in functional indices. Total 2D EF and reservoir and conduit strain varied inversely with age, counteracted by higher booster strain, with a greater magnitude of effect in women. Active 3D EF was significantly higher, while total and passive EFs decreased with age. Interracial differences were noted in LA volumes, without substantial differences in functional indices. CONCLUSION: Although similar normal values for LA volumes and strain can be applied to both sexes, meaningful differences in LA size occur with aging. Indices of function also shift with age, with a compensatory rise in booster function, which may serve to counteract observed lower total and passive EFs. Defining age-associated normal values may help differentiate age-associated "healthy" LA aging from pathologic processes.

Percutaneous mitral valve repair: a feasibility study in an ovine model of acute ischemic mitral regurgitation.

Annuloplasty is the cornerstone of surgical mitral valve repair. A percutaneous transvenous catheter-based approach for mitral valve repair was tested by placing a novel annuloplasty device in the coronary sinus of sheep with acute ischemic mitral regurgitation. Mitral regurgitation was reduced from 3-4+ to 0-1+ in all animals (P < 0.03). The annuloplasty functioned by reducing septal-lateral mitral annular diameter (30 +/- 2.1 mm preinsertion vs. 24 +/- 1.7 mm postinsertion; P < 0.03). These preliminary experiments demonstrate that percutaneous mitral annuloplasty is feasible. Further study is necessary to demonstrate long-term safety and efficacy of this novel approach.