A tracheotomy is a basic operation for the otorhinolaryngologist. According to reports from the United States and from our experience, there has been a steady decline in the number of tracheostomies performed by young resident doctors. Due to concerns for inadequate training of young residents, we developed a tracheotomy course consisting of a lecture, a live animal model, and questionnaires. The aim of this study was to evaluate the effectiveness of this training model. Twelve volunteer resident doctors joined the training course and, following a lecture by a senior surgeon, practiced tracheostomies with a 4-month-old female swine weighing 32 kg. We recorded the procedure time, blood loss, and complications. The doctors’ procedural competence was recorded using questionnaires before and after the training. All operations were completed within 30 minutes, and the blood loss was less than 5 ml. There were no serious acute complications. After the training, young residents had improved scores on surgical landmark recognition, overall procedural competence, confidence in performing the procedure, and understanding of the surgical procedures and equipment (P < .05). Our findings reveal that an animal model-based tracheotomy course is an effective training model for young resident doctors.

Ultrasound is the primary tool for evaluating salivary gland tumors (SGTs); however, tumor diagnosis currently relies on subjective features. This study aimed to establish an objective ultrasound diagnostic method using deep learning. We collected 446 benign and 223 malignant SGT ultrasound images in the training/validation set and 119 benign and 44 malignant SGT ultrasound images in the testing set. We trained convolutional neural network (CNN) models from scratch and employed transfer learning (TL) with fine-tuning and gradual unfreezing to classify malignant and benign SGTs. The diagnostic performances of these models were compared. By utilizing the pretrained ResNet50V2 with fine-tuning and gradual unfreezing, we achieved a 5-fold average validation accuracy of 0.920. The diagnostic performance on the testing set demonstrated an accuracy of 89.0%, a sensitivity of 81.8%, a specificity of 91.6%, a positive predictive value of 78.3%, and a negative predictive value of 93.2%. This performance surpasses that of other models in our study. The corresponding Grad-CAM visualizations were also presented to provide explanations for the diagnosis. This study presents an effective and objective ultrasound method for distinguishing between malignant and benign SGTs, which could assist in preoperative evaluation.

We aimed to validate the diagnostic accuracy of a novel sonographic scoring model and compare it with other methods in the evaluation of malignant major salivary gland tumors. We enrolled 138 patients who received neck ultrasound (US) with fine needle aspiration (FNA) and further operations or core needle biopsies for major salivary gland tumors from June 2015 to October 2021. The sonographic scoring model was presented as 2.08 × (vague boundary) + 1.75 × (regional lymphadenopathy) + 1.18 × (irregular or speculated shape) + 1.45 × (absence of posterior acoustic enhancement) + 2.4 × (calcification). We compared the diagnostic accuracy of the sonographic scoring model with shear wave elastography (SWE), real-time elastography (RTE), and US-FNA cytology for differentiating between benign and malignant lesions. The sensitivity, specificity, and accuracy were 58%, 89%, and 85% for the sonographic scoring model, 74%, 62%, and 64% for the SD of SWE with optimal cutoff value of 31.5 kPa, 69%, 70%, and 70% for the 4-point scoring system of RTE, and 74%, 93%, and 91% for US-FNA cytology, respectively. The sonographic scoring model is feasible as assistance in the evaluation of major salivary gland tumors. US-FNA cytology remains the tool of choice in diagnosing malignant salivary gland tumors.