Cuantificación automática de los volúmenes y función de ambos  ventrículos en resonancia cardíaca. Propuesta y evaluación de un  método de inteligencia artificial: pp.350-354

Ariel H. Curiale; Matías E. Calandrelli; Lucca Dellazoppa; Mariano Trevisan; Jorge L. Bocián; Juan P. Bonifacio; Germán Mato

doi:10.7775/rac.v89.i4.20427

Authors

Ariel H. Curiale Applied Chest Imaging Laboratory Brigham and Women’s Hospital, Harvard Medical School, Boston USA; Department of Medical Physics - The Bariloche Atomic Center - CONICET, San Carlos de Bariloche, Río Negro; Instituto Balseiro, Universidad Nacional de Cuyo. https://orcid.org/0000-0003-3102-4374
Matías E. Calandrelli Department of Medical Physics - The Bariloche Atomic Center - CONICET, San Carlos de Bariloche, Río Negro. https://orcid.org/0000-0002-8303-5078
Lucca Dellazoppa Instituto Balseiro, Universidad Nacional de Cuyo. https://orcid.org/0000-0002-2885-6607
Mariano Trevisan Sanatorio San Carlos, San Carlos de Bariloche, Río Negro. https://orcid.org/0000-0003-0667-2409
Jorge L. Bocián Sanatorio San Carlos, San Carlos de Bariloche, Río Negro. https://orcid.org/0000-0002-5986-9806
Juan P. Bonifacio Sanatorio San Carlos, San Carlos de Bariloche, Río Negro.
Germán Mato 1 Department of Medical Physics - The Bariloche Atomic Center - CONICET, San Carlos de Bariloche, Río Negro. 2 Instituto Balseiro, Universidad Nacional de Cuyo. 3 National Atomic Energy Commission (Comisión Nacional de Energía Atómica, CNEA). https://orcid.org/0000-0003-3106-1423

DOI:

https://doi.org/10.7775/rac.v89.i4.20427

Keywords:

Deep Learning – Heart Diseases / Diagnostic Imaging – Open Source - Magnetic Resonance Imagin.

Abstract

Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons.

Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods.

Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors.

Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study.

Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.

Curiale AH. Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach. REV ARGENT CARDIOL 2021;89:337-341.

http://dx.doi.org/10.7775/rac.v89.i4.20427