Chinese journal of otorhinolaryngology head and neck surgery最新文献

Objective: To evaluate the voice outcomes of of the platysma skin and sternohyoid muscle composite flap in reconstructing hemilaryngealdefects following vertical partial laryngectomy (VPL). Methods: This study enrolled 26 patients with T2-T3 glottic carcinoma treated in the Otorhinolaryngology department of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, from December 2021 to September 2024.There were 24 males and 2 females, with an age range of 41-75 (69.2±9.2) years. All patients underwent VPL with platysma-sternohyoid muscle composite flap reconstruction (composite flap group). Postoperative assessments included oncologic outcomes, stroboscopy, aerodynamic testing, and subjective voice evaluation. Patients who underwent sternohyoid muscle flaps reconstruction during the same period were selected as the control group, consisting of 23 males and 0 females, aged 41-79 (60.0±9.3) years. Statistical analysis was performed using independent samples t-test, Mann-Whitney U tests, or chi-square tests. Results: Among the 26 patients in the composite flap group, the 2-year overall survival rate was 100.00%, and the disease-free survival rate was 95.83%. The fundamental frequency (F0) in the composite flap group was significantly lower than that in the sternohyoid muscle flap group [139.4 (118.8, 172.0) Hz vs. 188 (143.8, 274.3) Hz, F=137.5,P=0.001], whereas, the maximum phonation time (MPT) was longer [8.4 (6.2, 11.0) s vs. 5.0 (4.0, 8.3) s, F=159.5,P=0.005]. The composite flap group showed better breathiness (B) scores (P=0.086) as well as more favorable laryngeal morphology and higher rates of detectable flap vibrational wave. Conclusion: The short-term postoperative voice quality of patients who underwent platysma skin-sternohyoid muscle composite flap reconstruction for VPL-related hemilaryngeal defects is significantly superior to those who received sternohyoid muscle flap reconstruction.

Objectives: To develop and validate an interpretable endoscopic intelligence system for assisting in the diagnosis of nasopharyngeal carcinoma (NPC). Through an integrated model of "screening-segmentation-biopsy localization", to provide clinical decision support from initial lesion detection to precise tissue sampling. Methods: A multi-center model development and validation study was conducted. A total of 12 323 nasopharyngeal endoscopic images from three medical institutions (including Eye & ENT Hospital of Fudan University, the First Affiliated Hospital of Guangxi Medical University and Guangxi Zhuang Autonomous Region People's Hospital) from 2018 to 2022 were collected. Model performance was validated using a single-center training and multi-center testing approach. An encoder-multi-head decoder architecture was employed, optimized via a multi-task joint loss function to simultaneously perform classification, segmentation, and biopsy site localization. Permutation testing (10 000 permutations) was used to evalute the significance of sensitivity and specificity, and the DeLong test was applied for receiver operating characteristic (ROC) curve comparison. Results: The model exgibited strong screening performance in internal testing: area under curve (AUC) was 0.998 (95%CI: 0.998-0.999), sensitivity was 91.56% (95%CI: 90.64%-92.40%), and specificity was 99.61% (95%CI: 99.37%-99.80%). External validation confirmed strong generalizability with an AUC of 0.957 (95%CI: 0.949-0.964), sensitivity of 97.63% and specificity of 76.79%. In human-AI comparison, the model's sensitivity (92.00%, 95%CI: 86.79%-96.00%) significantly outperformed most physicians, e.g., higher than senior physicians' average sensitivity (model 92.00% vs 78.40%, P<0.05), while senior physicians exhibited superior specificity (95.00% vs model 90.00%). The segmentation task yielded a Dice coefficient of 0.86 (95%CI: 0.80-0.93), and the recommended biopsy sites were accurate in 67.7% of cases. Conclusion: This study successfully develops a highly sensitive multi-task AI system for NPC diagnosis, which complements physicians' diagnostic strengths.

Objective: To explore a 3D visualization method for tongue articulation based on ultrasound images and to construct a corresponding visualization database. Methods: A high-fidelity statistical tongue model was constructed from MRI data and parameterized with six independent, physiologically interpretable control parameters to capture tongue-shape variation. To provide speech-specific data for model fitting, mid-sagittal ultrasound images were collected for each phoneme in the corpus, and tongue contours were manually annotated. A fully connected neural network was then trained to map the ultrasound-derived tongue contours to the model's control parameters. The estimated parameters were further refined through manual adjustment to obtain 3D tongue shapes that accurately matched the observed contours. Finally, model-fitting accuracy was quantitatively evaluated, and statistical analyses were conducted to examine tongue-shape differences among easily confusable phonemes. Results: For the majority of phonemes, the similarity between the 3D model's mid-sagittal contour and the ultrasound-derived tongue contour exceeded 90%, and the average root mean square error(RMSE) was reduced by approximately 28% compared with conventional tongue models, thereby enabling the detection of subtle articulatory distinctions among phonemes. Conclusion: The constructed 3D tongue articulation visualization database for Mandarin phonemes provides a valuable tool for speech rehabilitation in individuals with hearing impairment and for visualization-based instruction in second-language learning, demonstrating strong potential for dissemination and application.