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Modelos de Electrocardiograma Mejorados con Inteligencia Artificial para la Detección de Disfunción Ventricular

Philip M Croon1, Machteld J Boonstra2, Cornelis P Allaart3

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JACC. Advances
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Resumen

Los modelos de electrocardiograma mejorados con inteligencia artificial (IA-ECG) muestran un fuerte rendimiento en la detección de disfunción sistólica del ventrículo izquierdo (LVSD). Sin embargo, la disponibilidad limitada de modelos dificulta la validación independiente y la comparación de herramientas de IA-ECG para la detección de LVSD.

Palabras clave:
inteligencia artificialaprendizaje profundoelectrocardiografíainsuficiencia cardíacadisfunción ventricular izquierda

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Área de la Ciencia:

  • Cardiología
  • Inteligencia Artificial
  • Imágenes Médicas

Sus antecedentes:

  • Varios modelos de electrocardiograma mejorados con inteligencia artificial (IA-ECG) muestran potencial para detectar la disfunción sistólica del ventrículo izquierdo (LVSD).
  • Faltan comparaciones independientes cara a cara y evaluaciones de rendimiento dentro de una sola cohorte.
  • Los modelos existentes a menudo sufren de un alto riesgo de sesgo debido a descripciones limitadas de la cohorte y la falta de validación externa.

Objetivo del estudio:

  • Comparar de forma independiente el rendimiento de los modelos publicados de IA-ECG para la detección de LVSD.
  • Evaluar la transparencia y la reproducibilidad de los modelos de IA-ECG en cardiología.
  • Evaluar el rendimiento de los modelos de IA-ECG en una cohorte externa estandarizada.

Principales métodos:

  • Revisión sistemática de modelos de IA-ECG para la predicción de LVSD.
  • Validación externa de modelos de IA-ECG compartidos en un registro de resonancia magnética cardíaca bien fenotipado.
  • Evaluación del rendimiento en la cohorte general de pacientes y en un subgrupo de menor complejidad.

Principales resultados:

  • Se identificaron 51 modelos de IA-ECG de 35 estudios, y muchos reportaron un alto rendimiento (AUROC >0.80).
  • La validación independiente mostró un área bajo la curva característica operativa del receptor (AUROC) que oscilaba entre 0.83 y 0.93 en todos los pacientes y entre 0.87 y 0.96 en un subconjunto de menor complejidad.
  • El rendimiento del modelo se mantuvo constante en todos los subgrupos, observándose pequeñas disminuciones en ECGs con complejos QRS anchos o fibrilación auricular.

Conclusiones:

  • Los modelos de IA-ECG demuestran un fuerte rendimiento para la detección de LVSD, incluso cuando se entrenan en poblaciones diversas.
  • Este estudio representa la primera validación independiente y comparación cara a cara de modelos de IA-ECG para LVSD.
  • La disponibilidad limitada de modelos de IA-ECG dificulta la validación independiente integral y la adopción clínica.