ArticleLaryngoscope investigative otolaryngology2026
Comprehensive Numerical and Experimental Investigations on Endolaryngeal Mobilization.
Article in Laryngoscope investigative otolaryngology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Objective: To evaluate how subperichondrial mobilization affects implant-tissue mechanics, glottal configuration, and vibratory behavior in computational simulations of medialization laryngoplasty (ML). Methods: We used a finite-discrete element method (FDEM) framework to simulate subperichondrial tissue-cartilage separation and implant insertion in laryngeal models reconstructed from high-resolution magnetic resonance imaging. Four dissection conditions were evaluated, ranging from no mobilization to increasing dissection length distal to the thyroplasty window. Outcome measures included change in glottal area, vocal fold medial displacement, finite element fracturing along the tissue-cartilage interface, and estimated vibratory frequency. Results: Increasing dissection length produced increased medial displacement, progressive reductions in glottal area, fewer secondary extensions of the dissection plane during implantation, and higher estimated vibratory frequencies (range: 100.4-120.8 Hz). These findings indicate that subperichondrial tissue mobilization alters implant-induced force transmission and modifies boundary conditions relevant to vibration. Conclusion: In this first application of FDEM to simulate laryngeal biomechanics in ML, subperichondrial dissection length demonstrated direct effects on model predictions relevant to implant sizing, placement strategy, and anticipated phonatory outcomes. Incorporating tissue mobilization into computational frameworks as a mechanically meaningful variable improves physiological realism and supports future development of subject-specific surgical planning tools. Level of Evidence: Level V. This study provides preclinical computational evidence using FDEM, supporting and extending clinical observations in ML.
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