A Rare Case of Subcutaneous Emphysema following Lateral Pharyngoplasty for Obstructive Sleep Apnea

Lateral pharyngoplasty is a surgical option for treatment of obstructive sleep apnea (OSA). Here, we present a case involving a 40-year-old healthy man who underwent surgery, including lateral pharyngoplasty and robotic tongue base resection, for OSA. There were no intraoperative or immediate postoperative complications. However, on postoperative day 3, the patient presented with swelling in the temporal and buccal areas and was diagnosed with subcutaneous emphysema, later confirmed by computed tomography. The patient was carefully monitored under conservative care and discharged without complications. Although subcutaneous emphysema following tonsillectomy is a rare complication and usually resolves with conservative management, in certain cases, it might require surgical intervention. Lateral pharyngoplasty involves tonsillectomy and additional incision along the tonsillar fossa, which makes it susceptible to pharyngeal wall defects and, consequently, subcutaneous emphysema. Additionally, lateral pharyngoplasty and robotic tongue base resection cause pain and might thus contribute to the increase in intrapharyngeal pressure, which might aggravate subcutaneous emphysema. Lateral pharyngoplasty should be performed with meticulous dissection of the superior pharyngeal constrictor muscle. Healthcare providers should be aware of these complications and, upon suspicion of the same, place the patient under close observation to prevent life-threatening situations.ope

Transoral robotic surgery in Eagle's syndrome: our experience on four patients

Eagle's syndrome is characterised by focal pain in the tonsillar fossa on wide mouth opening or head rotation and various accompanying symptoms. While the syndrome is difficult to diagnose, shortening the styloid process via a transoral or transcervical surgical approach has been shown to be the most effective treatment. The aim of this article was to document our experience with a transoral robotic approach to treat Eagle's syndrome and to present the outcomes of four patients. We reviewed the cases of four patients with Eagle's syndrome who underwent transoral robotic surgery (TORS). The average age of patients was 53.75 years, and there were equal numbers of males and females. The styloid processes were reconstructed in 3D from the preoperative CT scans and were measured as an average of 4.18 cm (range 3.3-5.1). The mean set-up time and operation times were less than 10 minutes and 30 minutes, respectively. All patients were completely relieved of symptoms, and were able to restart an oral diet on post-operative day 1. No patient suffered intraoperative or postoperative complication, including cranial nerve injury, haemorrhage, or deep neck infection. In our experience, transoral excision of the styloid process via a robotic approach can be considered as a feasible treatment option for Eagle's syndrome.ope

Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database

BACKGROUND: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. METHODS: Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performing models were selected to compose an ensemble classifier, by combining classification scores of each classifier. FINDINGS: According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. INTERPRETATION: The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trained with data in a various acquisition condition, which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. FUND: This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(NRF-2017M3C7A1049051).ope

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

Background: Privacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing. Objective: Using machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility. Methods: All data were normalized to a range between -1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data. Results: The misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 102 and 103, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10-1 and 104, respectively. Conclusions: We applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations.ope

Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Background: Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual's record is scattered among different sites. Objective: The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. Methods: We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site's data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). Results: The autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. Conclusions: We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.ope

Differential Biases and Variabilities of Deep Learning-Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study

Background: Deep learning (DL)-based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application. Objective: This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems. Methods: We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models. Results: Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07). Conclusions: Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.ope

임상적 진단을 위한 인공지능과 전문가와의 협업 연구

prohibition박

The Feasibility of a Modified Exclusive Endoscopic Transcanal Transpromontorial Approach for Vestibular Schwannomas

Objective  We evaluated the feasibility of an exclusive endoscopic transcanal transpromontorial approach (EETTA) for the treatment of small vestibular schwannomas (VSs) limited to the internal auditory canal (IAC), and introduced a modification without external auditory canal closure. Methods  Between June 2016 and June 2017, seven patients with VS underwent surgery using a modified EETTA. Treatment outcomes, including efficacy of tumor resection, preservation of function, operation time, and quality of life (QOL), were evaluated. Results  The patients preoperatively exhibited Koos Grade I/II tumors and severe-to-profound hearing loss. Gross total resection was accomplished in all cases. There were no major complications, and all patients exhibited normal facial nerve function immediately after surgery. The mean follow-up period was 12.9 months. The operation time (average 196.3 ± 64.9 minutes) and hospitalization period (average 7.4 ± 1.0 days) were favorable. Short Form-36 scores for QOL showed unremarkable results compared with previous reports. Conclusions  The modified EETTA was effective in the removal of VSs in the IAC. It can be an alternative surgical option for small VSs.restrictio

The long-term oncological and functional outcomes of transoral robotic surgery in patients with hypopharyngeal cancer

OBJECTIVE: We conducted a prospective clinical trial of transoral robotic surgery in patients with hypopharyngeal cancer and herein report the long-term oncological and functional outcomes. MATERIALS AND METHODS: Between April 2008 and March 2014, 45 patients diagnosed with hypopharyngeal cancer participated in this prospective study. RESULTS: All patients were male with a mean age of 66.7years. The median follow-up period was 60months. Patients were classified using the staging system of the American Joint Commission on Cancer, as follows: Stage I, 7.9%; Stage II, 5.3%; Stage III, 15.8%; Stage IV, 71.1. Of all 38 patients, 17 (44.7%) were alive with no evidence of disease at the last follow-up. Seven patients (18.4%) died of TNM-related disease and fourteen (36.8%) from other causes. The 5-year disease-specific survival rate of stage I and II patients was 100.0%, and that of stage III and IV patients was 74.0%. The 5-year disease-free survival rate was 100.0% for stage I and II patients and 68.6% for stage III and IV patients. CONCLUSIONS: Patients who underwent TORS exhibited oncological outcomes comparable to those of conventional therapies and rapid functional recovery with low surgical morbidity. TORS and simultaneous neck dissection, with or without adjuvant therapy, may be effective alternatives to existing treatment methods.restrictio

A New Clinical Trial of Neoadjuvant Chemotherapy Combined With Transoral Robotic Surgery and Customized Adjuvant Therapy for Patients With T3 or T4 Oropharyngeal Cancer

BACKGROUND: A prospective clinical trial of combination neoadjuvant chemotherapy, transoral robotic surgery (TORS), and customized adjuvant therapy for patients with locally advanced oropharyngeal cancer was conducted. METHODS: Between July 2009 and October 2016, 31 patients were enrolled in this clinical trial. RESULTS: The primary lesions were located in the tonsils of 27 patients and in the base of the tongue of 4 patients. Of the 31 patients, 16 (51.6%) were classified as T3 and 15 patients (48.4%) as T4a. Three patients (9.7%) had stage 3 disease, and 28 (90.3%) had stage 4 disease. The 5-year overall survival rate was 78.7%; the 5-year disease-specific survival rate was 85%; and the 5-year disease-free survival rate was 80.8%. At the final follow-up visit, 26 patients were alive with no evidence of disease, and 1 was alive with disease. Four patients died during the study: two of tumor-node-metastasis (TNM)-related disease and two of another condition. All the patients tolerated an oral diet at an average of 7.4 days postoperatively. At the subjective swallowing evaluation using the Functional Outcome Swallowing Scale score, 83.9% of the patients exhibited favorable outcomes. No patient was permanently dependent on a feeding tube. All the patients breathed and phonated in the absence of a permanent tracheotomy at the final follow-up evaluation. CONCLUSIONS: The treatment strategy in this study afforded good oncologic and functional outcomes for patients with locally advanced oropharyngeal cancer. Although future large-scale multicenter studies with longer follow-up periods are needed, this study showed that neoadjuvant chemotherapy combined with TORS is useful for treating advanced oropharyngeal cancer.restrictio