Background:The endoscopic modified Lothrop procedure (EMLP) is used to manage ongoing refractory frontal sinusitis a er failed previous endoscopic sinus surgery (ESS), but this approach has a significant restenosis rate. We evaluated the potential benefits of mucosal gra s and pedicled flaps on the opening of the newly formed frontal ostium. Methods:Fi y patients with refractory frontal sinusitis or mucoceles a er ESS were randomized to undergo EMLP, either with (n = 27) or without (n = 23) mucosal gra s and pedicled flap reconstruction of the neo-ostium. The frontal neo-ostium was measured with Lindholm distending forceps, and anteroposterior (A-P) and lateral dimensions were measured intraoperatively, and then again at 6 weeks, 6 months, and 12 months postoperatively. Olfaction outcomes were assessed using the Taiwan Smell Identification Test (TWSIT) and a smell visual analog scale (VAS) score preoperatively and at 6 months postsurgery. Results:The initial intraoperative mean lateral and A-P dimensions were 23.2 ± 2.7 mm and 14.8 ± 2.3 mm and were significantly decreased at all time-points postoperatively. The mucosal gra s and pedicled flaps had greater lateral and A-P dimensions, and a greater percentage of intraoperative frontal neo-ostium area at all time-points postoperatively (all p < 0.05). At 6 months postoperatively, TWSIT (p = 0.027), but not the smell VAS score (p = 0.063), was significantly improved compared with baseline. TWSIT and smell VAS score changes did not differ between groups (p = 0.92 and p = 0.85, respectively). Conclusion:The use of mucosal gra s and pedicled flaps reduces stenosis of the frontal neo-ostium postsurgery and should be considered a er EMLP. C 2019 ARS-AAOA, LLC.

This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NM...

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Similar to caloric and rotational chair tests, vHIT reflects the function of the entire semicircular canal VOR pathway but cannot identify whether a peripheral vestibular sensory organ or vestibular nerve is damaged. The diagnostic value of vHIT is high (sensitivity of 87.9% and specificity of 94.8%) for VN, and other types of acute vertigo and its positive predictive and negative predictive values have been reported as 85.3% and 95.8%, respectively. 9 vHIT, as a high-frequency measure tool, is a useful complement to caloric and rotational tests. | CONCLUSIONSThe present study showed that vHIT results were different in VN and SHLV groups, which may underlie different aetiologies of VN and SHLV; thus, further study will be necessary to confirm these results and determine the different aetiologies.The exclusive transcanal endoscopic tympanoplasty with tragal perichondrium is feasible, high success rate and good hearing results.This method also had the advantages of the minimised, better cosmetic results and free of suture. CONF LICT OF I NTERESTSNone to be declared. O R C I D Li-Chun Hsieh