Shannon entropy as a reliable score to diagnose human fibroelastic degenerative mitral chords: A micro-ct ex-vivo study. (December 2022)

• Record Type: Journal Article
• Authors: Ferreño, Diego; Revuelta, José M.; Sainz-Aja, José A.; Wert-Carvajal, Carlos; Casado, José A.; Diego, Soraya; Carrascal, Isidro A.; Silva, Jacobo; Gutiérrez-Solana, Federico
• Is Part Of: Medical engineering & physics. Volume 110(2022)
• Publication Date: 2022-12
• Subjects: Mitral chordae tendineae -- Degenerative mitral valve disease -- Micro computerized tomography -- Shannon entropy -- K-means -- Machine learning; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2022.103919
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Normal and degenerative mitral chords have been examined through micro-CT. The arrangement of collagen fibers shows substantial differences. Collagen fibers were arranged as parallel thick bundles in degenerative chords. Shannon entropy provides a reliable score to identify degenerative chords. This finding opens the door to novel methods to diagnose incipient degeneracy. Abstract: 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.