Detecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review

Abstract

Background: Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly. Methods: We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). Results: A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%. Conclusions: To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care; thus, AI only serves as complementary assistance for clinicians.

Experiencia con la herramienta ultrasonográfica Kosmos en el abordaje y tratamiento de pacientes de la consulta externa de insuficiencia cardiaca: un estudio transversal unicéntrico

“…La integración de la IA ha tenido un gran impacto dentro del sector salud, ya que ofrece información sobre cuál es la mejor manera de abordar este tipo de situaciones 14,15 . Además, el reciente aumento de disponibilidad de los dispositivos portátiles de USG ha mejorado el acceso a los USG para muchos médicos.…”

“…Además, el reciente aumento de disponibilidad de los dispositivos portátiles de USG ha mejorado el acceso a los USG para muchos médicos. La inmediatez de resultados nos permite guiar la toma de decisiones clínicas, donde se ha visto utilidad de estos dispositivos [14][15][16] . Otros estudios, con poblaciones similares, han descrito la utilidad de los dispositivos de USG portátil evaluando su confiabilidad y precisión diagnóstica con algoritmos asistidos por IA.…”

Experience with the Kosmos ultrasonographic tool in the approach and treatment of ambulatory patients of a heart failure clinic: a single-center cross-sectional study

“…The integration of AI has had a significant impact on the health care setting by providing information on the best way to approach such situations 14,15 . Moreover, the recent increase in the availability of portable US devices has improved access to US for many physicians.…”

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

“…Research mainly involves electrocardiogram signals processing for the detection of heart disease (Cheng et al, 2020;Hadiyoso & Rizal, 2017;Pestana et al, 2020) or processing heart sounds (Rizal & Suratman, 2020;Zeinali & Niaki, 2022). Heart imaging techniques through echocardiography are also an alternative for the detection of heart disease (Liastuti et al, 2022;Mabrouk et al, 2016). Another imaging technique for analyzing blood vessels related to heart disease is angiography imaging (Khan Mamun & Elfouly, 2023).…”