Shannon entropy as a reliable score to diagnose human fibroelastic degenerative mitral chords: a micro-ct ex-vivo study

This paper is aimed at identifying by means of micro-CT the microstructural differences between normal and degenerative mitral marginal chordae tendineae. The control group is composed of 21 normal chords excised from 14 normal mitral valves from heart transplant recipients. The experimental group c...

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Detalhes bibliográficos
Autores: Ferreño Blanco, Diego|||0000-0003-3533-1881, Revuelta Soba, José Manuel, Sainz-Aja Guerra, José Adolfo|||0000-0003-3187-4790, Wert Carvajal, Carlos, Casado del Prado, José Antonio, Diego Cavia, Soraya|||0000-0003-4518-7449, Carrascal Vaquero, Isidro Alfonso|||0000-0002-7045-1267, Silva, Jacobo, Gutiérrez-Solana Salcedo, Federico|||0000-0003-2152-4148
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/27049
Acesso em linha:https://hdl.handle.net/10902/27049
Access Level:acceso abierto
Palavra-chave:Mitral chordae tendineae
Degenerative mitral valve disease
Micro computerized tomography
Shannon entropy
K-means
Machine learning
Descrição
Resumo:This paper is aimed at identifying by means of micro-CT the microstructural differences between normal and degenerative mitral marginal chordae tendineae. The control group is composed of 21 normal chords excised from 14 normal mitral valves from heart transplant recipients. The experimental group comprises 22 degenerative fibroelastic chords obtained at surgery from 11 pathological valves after mitral repair or replacement. In the control group the superficial endothelial cells and spongiosa layer remained intact, covering the wavy core collagen. In contrast, in the experimental group the collagen fibers were arranged as straightened thick bundles in a parallel configuration. 100 cross-sections were examined by micro-CT from each chord. Each image was randomized through the K-means machine learning algorithm and then, the global and local Shannon entropies were obtained. The optimum number of clusters, K, was estimated to maximize the differences between normal and degenerative chords in global and local Shannon entropy; the p-value after a nested ANOVA test was chosen as the parameter to be minimized. Optimum results were obtained with global Shannon entropy and 2≤K≤7, providing p < 0.01; for K=3, p = 2.86⋅10-³. These findings open the door to novel perioperative diagnostic methods in order to avoid or reduce postoperative mitral valve regurgitation recurrences.