Voice quality after thyroplasty type I using a silicone block

Abstract

The aim of the work was to evaluate the voice quality of 10 adult patients after thyroplasty type I using a silicone block. Preoperatively patients suffered from unilateral vocal fold paralysis. MATERIAL AND METHODS: We evaluated selected preoperative and postoperative patient fi ndings (RBH according to Wendler classifi cation, videolaryngostroboscopy and maximum phonation time MPT). The evaluation was performed by a phoniatrician and clinical speech therapist, using patient medical records and the Lingwaves and Glottis Analysis Tools (GAT) programs. RESULTS: Preoperatively we identifi ed R1B1H1-R3B3H3, postoperatively R0B0H0-R3B3H2. In six cases the paralysed vocal cord was preoperatively in a paramedian position, twice in intermediate position and twice in a lateral position. In the case of 6 patients presbyphonic changes were present. Motility of the healthy vocal cord was within the norm. Postoperatively, the paretic vocal cord was medialised. We assessed the MPT value: preoperatively 3-10 seconds, postoperatively 9-17 seconds. Postoperatively we measured jitter (%), shimmer (%) and closing quotient values. In comparison with healthy subjects (Inwald et al. 2011), jitter and shimmer values were higher and closing quotient values after thyroplasty type I close to normal values (Tab.1). CONCLUSIONS: The postoperative voice quality was signifi cantly better in comparison with the preoperative state; though, it never achieved the parameters of a normal voice. We confi rmed the signifi cant contribution of TPL I for the patients´ quality of life through partial rehabilitation of their voices. Text in PDF www.elis.sk.

A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis

“…In a clinical setting, we recommend GAT for pre-post comparisons. GAT has been already used for comparing voice quality pre-post surgery (Sebova et al, 2019), pre-post voice treatment (Echternach, Raschka, et al, 2020), and pre-post vocal task (Dippold et al, 2015;Echternach et al, 2017) and how quantitative parameters vary in patient populations (Arbeiter et al, 2018;Ziethe et al, 2019). With our fully automatic glottis segmentation procedure, a straightforward workflow and an easy-to-use graphical user interface, a patient's data set can be segmented and analyzed in minutes even from untrained personnel.…”