Cross-cultural adaptation of the Hebrew Nasal Obstruction Symptom Evaluation (NOSE) scale.

Objective: Nasal obstruction is a common complaint. The Nasal Obstruction Symptom Evaluation scale (NOSE) is a reliable validated tool used to assess the quality of life of patients with nasal obstruction. The purpose of this study is to validate the Hebrew version of the NOSE scale (He-NOSE). Methods: A prospective instrument validation was conducted. The NOSE scale was translated primarily from English to Hebrew and then back from Hebrew to English according to the accepted guidelines of the cross-cultural adaptation process. The study group included surgery candidates suffering from nasal obstruction due to a deviated nasal septum and/or inferior turbinate hypertrophy. The study group completed the validated He-NOSE questionnaire twice prior to the surgery and once again, a month post-surgery. A control group of individuals with no history of nasal complaints or surgeries was asked to complete the questionnaire once. Reliability, internal consistency, validity, and responsiveness to change of the He-NOSE were evaluated. Results: Fifty-three patients and 100 controls were included in this study. The scale showed excellent ability to discriminate between the study and the control group, exhibiting significantly lower scores in the control group (73.8 and 7 average scores respectively, p < .001). Good internal consistency (Cronbach's alpha .71 and .76) and test-retest reliability (Spearman rank correlation r = .752, p < .0001) were measured. Moreover, the scale revealed remarkable responsiveness to change (p < .00001). Conclusion: The translated and adapted He-NOSE scale can be a useful tool to be applied in both clinical and research fields when assessing nasal obstruction. Level of evidence: N/A.

COVID-19 classification of X-ray images using deep neural networks.

OBJECTIVES: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. METHODS: In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. RESULTS: Our model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97). CONCLUSION: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. KEY POINTS: • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings.

The natural neck crease as an anatomic landmark for thyroid surgery incision.

BACKGROUND: The anatomical relations between the neck skin crease and the thyroid gland isthmus and cricoid cartilage are important for surgical planning. METHODS: Between 2016 and 2018 anatomical measurements were performed on 118 participants during a presurgical ultrasound exam and were verified during surgery. The data were stratified according to age, sex, patient's body mass index (BMI), and height. FINDINGS: The cricoid cartilage was found to be lower than the second skin neck crease by 0.2 ± 0.9 cm on average and the thyroid isthmus lower by 0.9 ± 1 cm from the second neck crease. No correlation was found to subjects' age, BMI, or height. Patient satisfaction, measured by the dermatologic life quality index, was high. CONCLUSIONS: A surgical incision made along the second neck crease will allow for a predicted and proper exposure in thyroid surgeries. The possibility of concealing the surgical scar in the natural neck skin crease can increase patients' aesthetic satisfaction.