Complete laryngotracheal separation after a clothesline-type injury: A case report.

This case report presents management of a complete laryngotracheal separation after a 'clothesline' type injury. Laryngeal trauma to some extent was suspected but the diagnosis was confirmed after massive subcutaneous emphysema occurred after seemingly successful intubation. The airway was secured with a tracheostomy and secondary laryngotracheal anastomosis was done.

Categorizing normal and pathological voices: automated and perceptual categorization.

OBJECTIVES: The aims of the present study were to evaluate the accuracy of an elaborated automated voice categorization system that classified voice signal samples into healthy and pathological classes and to compare it with classification accuracy that was attained by human experts. MATERIAL AND METHODS: We investigated the effectiveness of 10 different feature sets in the classification of voice recordings of the sustained phonation of the vowel sound /a/ into the healthy and two pathological voice classes, and proposed a new approach to building a sequential committee of support vector machines (SVMs) for the classification. By applying "genetic search" (a search technique used to find solutions to optimization problems), we determined the optimal values of hyper-parameters of the committee and the feature sets that provided the best performance. Four experienced clinical voice specialists who evaluated the same voice recordings served as experts. The "gold standard" for classification was clinically and histologically proven diagnosis. RESULTS: A considerable improvement in the classification accuracy was obtained from the committee when compared with the single feature type-based classifiers. In the experimental investigations that were performed using 444 voice recordings coming from 148 subjects, three recordings from each subject, we obtained the correct classification rate (CCR) of over 92% when classifying into the healthy-pathological voice classes, and over 90% when classifying into three classes (healthy voice and two nodular or diffuse lesion voice classes). The CCR obtained from human experts was about 74% and 60%, respectively. CONCLUSION: When operating under the same experimental conditions, the automated voice discrimination technique based on sequential committee of SVM was considerably more effective than the human experts.

Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders.

OBJECTIVE: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data. METHODS: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search. RESULTS: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging. CONCLUSIONS: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar.

Quantitative microlaryngoscopic measurements of vocal fold polyps, glottal gap and their relation to vocal function.

OBJECTIVES: The purpose of this study was to quantify the size of vocal fold polyps and to investigate the relationship between the glottal gap and parameters of acoustic voice analysis and phonetography. MATERIAL AND METHODS: Eighty-one microlaryngoscopic images and digital recordings of voices (acoustic analysis and phonetogram) acquired from the patients with vocal fold polyps (VFPs) were employed in this study. Vocal fold (VF) images were collected during routine direct microlaryngoscopy using Moller-Wedel Universa 300 surgical microscope, 3-CCD Elmo 768 x 576-pixel color video camera and a 300 W Xenon light source. Acoustic voice analysis and phonetography were established using Dr. Speech (Tiger Electronics Inc.) software. Microlaryngoscopic images were processed by original software created by ELINTA and displayed on a monitor. The relative lengths and widths of vocal fold polyps as well as percentage area of VFP were calculated. The Pearson's correlation was applied to reveal the correlation between VFP dimensions and acoustic voice parameters. RESULTS: There were no statistically significant differences between the dimensions of left and right vocal folds and VFPs. Statistically significant slight to mild correlations between measured dimensions of VFP acoustic and phonetogram parameters were revealed, with HNR and phonetogram area showing the strongest correlation to the size of VFPs. CONCLUSION: The results of our study confirm that quantitative microlaryngoscopic measurements of vocal fold polyp and glottal gap dimensions may be a useful tool for objective assessment of glottic incompetence and voice impairment.