Evaluation of Vertical Level Differences Between Left and Right Vocal Folds Using Artificial Intelligence System in Excised Canine Larynx.

OBJECTIVES: This study aimed to establish an artificial intelligence (AI) system to classify vertical level differences between vocal folds during vocalization and to evaluate the accuracy of the classification. METHODS: We designed models with different depths between the right and left vocal folds using an excised canine larynx. Video files for the data set were obtained using a high-speed camera system and a color complementary metal oxide semiconductor camera with global shutter. The data sets were divided into training, validation, and testing. We used 20,000 images for building the model and 8000 images for testing. To perform deep learning multiclass classification and to estimate the vertical level difference, we introduced DenseNet121-ConvLSTM. RESULTS: The model was trained several times using different numbers of epochs. We achieved the most optimal results at 100 epochs, and the batch size used during training was 16. The proposed DenseNet121-ConvLSTM model achieved classification accuracies of 99.5% and 88.0% for training and testing, respectively. After verification using an external data set, the overall accuracy, precision, recall, and f1-score were 90.8%, 91.6%, 90.9%, and 91.2%, respectively. CONCLUSIONS: The newly developed AI system may be an easy and accurate method for classifying superior and inferior vertical level differences between vocal folds. Thus, this AI system can be applied and may help in the assessment of vertical level differences in patients with unilateral vocal fold paralysis.

Small bowel diaphragm disease with multiple cluster lesions in one segment of the small bowel mimicking an adhesion band: A case report.

RATIONALE: Small bowel diaphragm disease (SBDD) is a rare case, caused by long-term administration of nonsteroidal anti-inflammatory drugs (NSAIDs). The circumferential diaphragm in the lumen of small bowel causing mechanical obstruction is the characteristic finding. PATIENT CONCERNS: A 74-year-old male was transferred to Pusan National University Yangsan Hospital (PNUYH) due to abdominal pain lasting for 2 months. He was treated in the local medical center (LMC) with Levin tube insertion and Nil Per Os (NPO) but showed no improvement. DIAGNOSIS: According to abdomen-pelvis computed tomography (CT) result, small bowel obstruction due to the adhesion band was identified, showing dilatation of the small bowel with abrupt narrowing of the ileum. INTERVENTIONS: Laparoscopic exploration was done but failed to find an adhesion band. An investigation of the whole small bowel was done with mini-laparotomy. At the transitional zone, the intraluminal air could not pass so the segmental resection of small bowel including the transitional zone and end-to-end anastomosis was done. OUTCOMES: After surgery, every laboratory finding recovered to the normal range in 4 days, but the patient's ileus lasted for 8 days. The patient's symptoms were relieved after defecation, he was discharged on postoperative day 10. LESSONS: For patients who show mechanical obstruction without an operation history but with long-term administration of NSAIDs, the clinicians should suspect small bowel diaphragm disease.

A Cepstral Analysis of Pathological Voice Quality in the Korean Population using Praat.

OBJECTIVES: This study aimed to investigate the reference values for cepstral peak prominence (CPP) and smoothed CPP (CPPS) measured using Praat in Korean speakers with the normal, healthy and pathological voice. METHODS: A total of 4,524 Korean participants with vocally healthy (n = 410) and dysphonic voices (n = 4,114) participated in this study. The speech task consisted of a sustained vowel /a/ and a sentence reading the Korean passage "Walk". CPP and CPPS values were quickly and automatically measured in sustained vowel and continuous speech tasks using Praat script. Furthermore, three veteran speech language pathologists (SLPs) scored the severity of dysphonia using the GRBAS scale (grade, roughness, breathiness, asthenia, strain) and Consensus Auditory Perceptual Evaluation of Voice (CAPE-V). RESULTS: Three SLPs showed high inter- and intra-rater reliabilities (IRR) in auditory-perceptual (A-P) evaluation. Significant differences were confirmed in CPP and CPPS between the normally healthy and pathological voice groups for both voice tasks (P < 0.01). The measured values of CPP and CPPS varied depending on the laryngeal pathology. In the receiver operating characteristic (ROC) curve analysis, the CPP_Vowel (CPP_V), CPPS_V, CPP_Sentence (CPP_S), and CPPS_S cut-off values were <21.5, <12.0, <19.7, and <10.1, respectively. Through ROC curve analysis, it was confirmed that CPP and CPPS had excellent diagnostic accuracy in distinguishing disordered voice (area under the ROC: 0.951-0.966). CONCLUSION: We investigated the reference values for CPP and CPPS measured with Praat for Korean speakers and confirmed that cepstral analysis is a promising tool for differentiating pathological voice.

Diagnosis of Early Glottic Cancer Using Laryngeal Image and Voice Based on Ensemble Learning of Convolutional Neural Network Classifiers.

OBJECTIVES: The purpose of study is to improve the classification accuracy by comparing the results obtained by applying decision tree ensemble learning, which is one of the methods to increase the classification accuracy for a relatively small dataset, with the results obtained by the convolutional neural network (CNN) algorithm for the diagnosis of glottal cancer. METHODS: Pusan National University Hospital (PNUH) dataset were used to establish classifiers and Pusan National University Yangsan Hospital (PNUYH) dataset were used to verify the classifier's performance in the generated model. For the diagnosis of glottic cancer, deep learning-based CNN models were established and classified using laryngeal image and voice data. Classification accuracy was obtained by performing decision tree ensemble learning using probability through CNN classification algorithm. In this process, the classification and regression tree (CART) method was used. Then, we compared the classification accuracy of decision tree ensemble learning with CNN individual classifiers by fusing the laryngeal image with the voice decision tree classifier. RESULTS: We obtained classification accuracy of 81.03 % and 99.18 % in the established laryngeal image and voice classification models using PNUH training dataset, respectively. However, the classification accuracy of CNN classifiers decreased to 73.88 % in voice and 68.92 % in laryngeal image when using an external dataset of PNUYH. To solve this problem, decision tree ensemble learning of laryngeal image and voice was used, and the classification accuracy was improved by integrating data of laryngeal image and voice of the same person. The classification accuracy was 87.88 % and 89.06 % for the individualized laryngeal image and voice decision tree model respectively, and the fusion of the laryngeal image and voice decision tree results represented a classification accuracy of 95.31 %. CONCLUSION: The results of our study suggest that decision tree ensemble learning aimed at training multiple classifiers is useful to obtain an increased classification accuracy despite a small dataset. Although a large data amount is essential for AI analysis, when an integrated approach is taken by combining various input data high diagnostic classification accuracy can be expected.