A prognostic value of estimated pulse wave velocity in idiopathic sudden sensorineural hearing loss.

PURPOSE: Arterial stiffness, represented by estimated pulse wave velocity (ePWV), is the independent surrogate marker for cardiovascular event. The aim of the study was to investigate the significance of ePWV in the treatment outcome of idiopathic sudden sensorineural hearing loss (SSNHL). METHODS: One hundred and ten patients with idiopathic SSNHL who hospitalized between April 2019 and March 2022 were evaluated. Arterial stiffness was calculated with formula for ePWV and other cardiovascular parameters of body mass index (BMI), and serum lipid level was determined. All patients received systemic high-dose steroid therapy and intratympanic steroid injections as a salvage management. Treatment outcome was assessed at 6 months after treatment, and classified as recovery and nonrecovery groups according to hearing recovery. RESULTS: The initial pure-tone hearing threshold was 72.6 ± 23.8 dB and final hearing threshold was 52.63 ± 31.10 dB. After treatment, 60 (54.5%) patients included in recovery group and other 50 (45.5%) were classified as nonrecovery group. Age, days of onset to treatment, BMI, waist circumference, and ePWV were higher in the nonrecovery group compared to recovery group in univariate analysis (p = 0.039, p = 0.049, p = 0.003, p = 0.004, p = 0.007, respectively). In multivariate analysis, days of onset to treatment, BMI, and ePWV were associated with recovery (p = 0.030, p = 0.007, p = 0.022). CONCLUSION: Higher ePWV, a measure of arterial stiffness, was associated with a poor hearing recovery of SSNHL.

Sudden Hearing Loss and Vertigo With Silent Pontine Infarction: A Case Report.

Most cases of sudden sensorineural hearing loss (SSNHL) occur without a specific identifiable cause, although vascular factors may serve as potential etiological contributors. Silent infarction refers to ischemic changes observed on imaging studies without accompanying clinical symptoms; however, this condition is clinically significant owing to the increased risk of future stroke. We report a case of left-sided SSNHL accompanied by dizziness in a 62-year-old male patient who was diagnosed with left pontine infarction without any other neurological symptoms. The cochlea and pons receive blood supply from the anterior inferior cerebellar artery; the cochlea lacks collateral vessels and is therefore susceptible to fluctuations in blood flow. This case report provides evidence to support the vascular hypothesis as the etiology underlying SSNHL.

Malnutrition and Associated Factors in Acute and Subacute Stroke Patients with Dysphagia.

Most patients with stroke suffer from complications and these include dysphagia. Dysphagia can cause malnutrition, and malnutrition affects prognosis and recovery. However, there is a lack of accurate studies on the nutritional status of stroke patients with dysphagia and its associated factors in different phases of stroke. This study retrospectively investigated 620 stroke patients who underwent a videofluoroscopic swallowing study (VFSS) due to dysphagia, from March 2018 to February 2021. The study aimed to evaluate the nutritional state and associated factors of malnutrition in acute and subacute stroke patients with dysphagia. Serum albumin and percentage of current weight to ideal weight were used to determine nutritional status. Malnutrition was observed in 58.9 and 78.9% of acute and subacute stroke patients. Exact logistic regression analysis revealed that old age and high penetration-aspiration scale score were significantly associated factors for malnutrition in patients with acute stroke. Old age, stroke history, bilateral hemiplegia, high modified Rankin score, low Korean Mini-Mental State Examination, pneumonia, and high functional dysphagia score were significantly associated factors for malnutrition in patients with subacute stroke. Patients with these associated factors in each phase of stroke require active nutritional assessment and care to decrease the risk of malnutrition.

A content analysis of YouTube videos on tinnitus in South Korea.

More people use the internet for medical information, especially YouTube. Nevertheless, no study has been conducted to analyze the quality of YouTube videos about tinnitus in Korea. This study aims to review the contents and quality of YouTube videos on tinnitus. The top 100 Korean YouTube videos on tinnitus were reviewed by a tinnitus expert. This study assessed video details: title, creator, length, and popularity indicators-subscribers, views, and likes. The contents of the video clips were analyzed to determine the relevance, understandability, actionability, and quality of information. Out of 100 tinnitus videos, 27 were created by otolaryngologists, 25 by traditional Korean medicine doctors, 25 by other medical professionals, and 3 by lay persons. Sensorineural tinnitus was frequently dealt, and hearing loss, stress, and noise were introduced as main causes of tinnitus. Otolaryngologists' videos covered verified treatments, but others suggested unproven therapies including herbal medicine or acupressure. Otolaryngologists' videos showed significantly higher understandability and quality of information compared to others (p < 0.001). This study found that tinnitus YouTube videos frequently present low-quality and incorrect material, which could have an adverse effect on patients. Results highlight the need for tinnitus specialists to provide accurate information.

Change of Femoral Anteversion Angle in Children With Intoeing Gait Measured by Three-Dimensional Computed Tomography Reconstruction: 3-Year Follow-Up Study.

OBJECTIVE: To investigate long-term changes in femoral anteversion angle (FAA) in children with intoeing gait and to identify factors that affect FAA changes. METHODS: We retrospectively analyzed three-dimensional computed tomography data from 2006 to 2022 of children with intoeing gait with ≥3 years of follow-up without active treatment. The study examined the mean changes in FAA, the effects of sex, age, and initial FAA on FAA change, and mean FAAs by age. Changes in FAA severity up to eight years of age were also observed and analyzed by sex. RESULTS: A total of 126 lower limbs of 63 children (30 males, 33 females) with intoeing gait were included, with a mean age of 5.11±1.05 years and a mean follow-up period of 43.59±7.74 months. The initial FAA was 41.42°±8.29° and the follow-up FAA was 33.25°±9.19°, indicating a significant decrease (p<0.001). Significant correlations were observed between age and changes in FAA, as well as between initial FAA and changes in FAA (r=0.248, p=0.005; r=-0.333, p<0.001). At age 8 years, only 22 limbs were classified as having mild FAA severity. CONCLUSION: During the follow-up period, children with intoeing gait had a significant decreased in FAA. No significant difference in FAA change was found between sex, but younger children and those with greater initial FAA were more likely to have decreased FAA. However, most children retained moderate to severe severity of increased FAA. Further studies are required to validate these findings.

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet).

Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.

Feasibility of the Machine Learning Network to Diagnose Tympanic Membrane Lesions without Coding Experience.

A machine learning platform operated without coding knowledge (Teachable machine®) has been introduced. The aims of the present study were to assess the performance of the Teachable machine® for diagnosing tympanic membrane lesions. A total of 3024 tympanic membrane images were used to train and validate the diagnostic performance of the network. Tympanic membrane images were labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), and cholesteatoma. According to the complexity of the categorization, Level I refers to normal versus abnormal tympanic membrane; Level II was defined as normal, OME, or COM + cholesteatoma; and Level III distinguishes between all four pathologies. In addition, eighty representative test images were used to assess the performance. Teachable machine® automatically creates a classification network and presents diagnostic performance when images are uploaded. The mean accuracy of the Teachable machine® for classifying tympanic membranes as normal or abnormal (Level I) was 90.1%. For Level II, the mean accuracy was 89.0% and for Level III it was 86.2%. The overall accuracy of the classification of the 80 representative tympanic membrane images was 78.75%, and the hit rates for normal, OME, COM, and cholesteatoma were 95.0%, 70.0%, 90.0%, and 60.0%, respectively. Teachable machine® could successfully generate the diagnostic network for classifying tympanic membrane.

Effects of preoperative personal education on shoulder function and lymphedema in patients with breast cancer: A consort.

BACKGROUND: To compare the incidence and severity of ipsilateral shoulder dysfunction and lymphedema of 2 groups of patients needing to undergo unilateral breast cancer surgery, one of which had only received printed education materials and the other group which had received educational materials plus preoperative education. METHODS: We selected 61 patients who had been diagnosed with unilateral breast cancer and planned to undergo surgery. Before surgery, patients were randomly assigned, either to a control group that only received printed education materials about exercise for shoulder pain relief and lymphatic edema prevention following breast cancer surgery, or to an experimental group that received the printed education material with personal education. Participants were evaluated at 1, 3, 6, and 12 months after the surgery. To evaluate the impairment of shoulder function, we measured the passive shoulder range of motion (ROM), the degree of pain as visual analog scale (VAS), the short version of the disability of arm, shoulder, and hand (short DASH) scores, and the shoulder pain and disability index (SPADI). We checked arm circumferences to evaluate lymphedema. RESULTS: There was no significant difference in demographic or clinical variables between the control and experimental groups. The experimental group showed significantly less limitation in abduction (P = .042) and forward flexion (P = .039) in the 6 months following surgery. Change in the VAS, short DASH, and SPADI scores were 1.633 (P < .001), 2.167 (P < .001), and 4.1 (P = .003) at 1 month following surgery, respectively. These then decreased with time. These changes started before shoulder ROM and arm circumference changes had occurred, which had started 3 months following surgery. CONCLUSIONS: Preoperative education might be helpful for the prevention of a shoulder ROM limitation, and we need to focus on pain and disability in patients immediately following breast cancer surgery, and then on ROM and lymphedema.

Position Classification of the Endotracheal Tube with Automatic Segmentation of the Trachea and the Tube on Plain Chest Radiography Using Deep Convolutional Neural Network.

Background: This study aimed to develop an algorithm for multilabel classification according to the distance from carina to endotracheal tube (ETT) tip (absence, shallow > 70 mm, 30 mm ≤ proper ≤ 70 mm, and deep position < 30 mm) with the application of automatic segmentation of the trachea and the ETT on chest radiographs using deep convolutional neural network (CNN). Methods: This study was a retrospective study using plain chest radiographs. We segmented the trachea and the ETT on images and labeled the classification of the ETT position. We proposed models for the classification of the ETT position using EfficientNet B0 with the application of automatic segmentation using Mask R-CNN and ResNet50. Primary outcomes were favorable performance for automatic segmentation and four-label classification through five-fold validation with segmented images and a test with non-segmented images. Results: Of 1985 images, 596 images were manually segmented and consisted of 298 absence, 97 shallow, 100 proper, and 101 deep images according to the ETT position. In five-fold validations with segmented images, Dice coefficients [mean (SD)] between segmented and predicted masks were 0.841 (0.063) for the trachea and 0.893 (0.078) for the ETT, and the accuracy for four-label classification was 0.945 (0.017). In the test for classification with 1389 non-segmented images, overall values were 0.922 for accuracy, 0.843 for precision, 0.843 for sensitivity, 0.922 for specificity, and 0.843 for F1-score. Conclusions: Automatic segmentation of the ETT and trachea images and classification of the ETT position using deep CNN with plain chest radiographs could achieve good performance and improve the physician's performance in deciding the appropriateness of ETT depth.

The trend in adoption of hearing aids following changes in provision policy in South Korea.

The Korean government started to cover part of the price of hearing aids ($200) for individuals with hearing disabilities in 1997, and the reimbursement for a hearing aid increased in 2005 ($300) and again in 2015 ($1000). The aim of this study was to evaluate the annual trend of newly-registered individuals with hearing disability according to the changes of the hearing aid provision scheme. Subjects with newly-registered hearing disabilities were assessed using Korean National Health Insurance Service (KNHIS) data from January 1, 2004, to December 31, 2018. A total of 271,742 individuals were newly registered during the index period. Records of hearing aid prescriptions and hearing aid subsidies were used to assess the adoption of hearing aids. This study also assessed the intervals between registration of hearing disability and the adoption of hearing aids, as well as the number of hearing aid subsidies provided. From 2004 to 2009 there was a slight increase in the number of individuals newly registered with hearing disabilities, and from 2011 to 2015, the number showed a tendency to decrease. Then, from 2015, the number of individuals with hearing disabilities increased abruptly, and the proportion of subjects receiving hearing aid subsidies also increased. Between 2004 and 2018, the time interval from hearing disability registration to hearing aid adoption showed a decreasing trend. We conclude that the annual number of individuals with newly-registered hearing disabilities is affected by the level of the hearing aid subsidy, and there is much unregistered or unaddressed hearing loss prior to the introduction of realistic hearing aid provision.

Dementia in a Hearing-impaired Population According to Hearing Aid Use: A Nationwide Population-based Study in Korea.

BACKGROUND: Hearing loss is considered a potentially modifiable risk factor for dementia. However, the effect of use of a hearing aid on the development of dementia has not been clearly established. We aimed to assess the incidence of dementia in hearing-impaired individuals according to hearing aid use in a nationwide population-based cohort study with matched controls. METHODS: This was a retrospective, longitudinal, cohort study of South Korean national claims data for individuals newly registered with hearing disabilities (better ear ≥40 dBHL with worse ear ≥80 dBHL, or better ear ≥60 dBHL) between 2004 and 2008. The hearing aid cohort comprised individuals who received hearing aid subsidies from the National Health Insurance within a year from disability registration. The comparison cohort comprised individuals without a record of a hearing aid claim during the study period after 1:1 matching for audiologic and sociodemographic factors with the hearing aid cohort. The occurrence of dementia was followed up until 2018. RESULTS: Each cohort comprised 8780 individuals. Overall incidence of dementia in the hearing aid and comparison cohorts were 156.0 and 184.5 per 10,000 person-years, respectively (incidence rate ratio = 0.85, 95% confidence interval 0.79-0.91). In a multivariable analysis of the whole study populations, hearing aid use (hazard ratio = 0.75, 95% confidence interval 0.70-0.81) attenuated the risk of dementia. CONCLUSIONS: Dementia incidence in individuals with hearing disabilities was lower in hearing aid users than that in nonusers. Hearing rehabilitation with hearing aids should be encouraged for individuals with hearing loss.

Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.

OBJECTIVES: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movements of the tympanic membrane and part of the ossicles, analyzing VPO images was expected to be useful in predicting the presence of middle ear transmission problems. Using a convolutional neural network (CNN), a deep neural network implementing computer vision, this preliminary study aimed to create a deep learning model that detects the presence of an air-bone gap, conductive component of hearing loss, by analyzing VPO findings. DESIGN: The medical records of adult patients who underwent VPO tests and pure-tone audiometry (PTA) on the same day were reviewed for enrollment. Conductive hearing loss was defined as an average air-bone gap of more than 10 dB at 0.5, 1, 2, and 4 kHz on PTA. Two significant images from the original VPO videos, at the most medial position on positive pressure and the most laterally displaced position on negative pressure, were used for the analysis. Applying multi-column CNN architectures with individual backbones of pretrained CNN versions, the performance of each model was evaluated and compared for Inception-v3, VGG-16 or ResNet-50. The diagnostic accuracy predicting the presence of conductive component of hearing loss of the selected deep learning algorithm used was compared with experienced otologists. RESULTS: The conductive hearing loss group consisted of 57 cases (mean air-bone gap = 25 ± 8 dB): 21 ears with effusion, 14 ears with malleus-incus fixation, 15 ears with stapes fixation including otosclerosis, one ear with a loose incus-stapes joint, 3 cases with adhesive otitis media, and 3 ears with middle ear masses including congenital cholesteatoma. The control group consisted of 76 cases with normal hearing thresholds without air-bone gaps. A total of 1130 original images including repeated measurements were obtained for the analysis. Of the various network architectures designed, the best was to feed each of the images into the individual backbones of Inception-v3 (three-column architecture) and concatenate the feature maps after the last convolutional layer from each column. In the selected model, the average performance of 10-fold cross-validation in predicting conductive hearing loss was 0.972 mean areas under the curve (mAUC), 91.6% sensitivity, 96.0% specificity, 94.4% positive predictive value, 93.9% negative predictive value, and 94.1% accuracy, which was superior to that of experienced otologists, whose performance had 0.773 mAUC and 79.0% accuracy on average. The algorithm detected over 85% of cases with stapes fixations or ossicular chain problems other than malleus-incus fixations. Visualization of the region of interest in the deep learning model revealed that the algorithm made decisions generally based on findings in the malleus and nearby tympanic membrane. CONCLUSIONS: In this preliminary study, the deep learning algorithm created to analyze VPO images successfully detected the presence of conductive hearing losses caused by middle ear effusion, ossicular fixation, otosclerosis, and adhesive otitis media. Interpretation of VPO using the deep learning algorithm showed promise as a diagnostic tool to differentiate conductive hearing loss from sensorineural hearing loss, which would be especially useful for patients with poor cooperation.

The impact of the COVID-19 pandemic on outpatients of internal medicine and pediatrics: A descriptive study.

ABSTRACT: This study analyzed the changes in the number of outpatients and disease presentation during the entirety of 2020, the period of COVID-19 pandemic.The average annual number of outpatient visits between 2017 and 2019 (before COVID-19) and the total number of outpatient visits in 2020 (COVID-19 period) were compared. Diagnostic codes were identified during 2 periods to analyze changes in the number of outpatient visits according to disease and month.The average annual number of outpatient visits was 47,105 before, and 40,786 during the COVID-19 pandemic, with a decrease of 13.4%. The number of outpatient visits in internal medicine decreased by 10.2% during the COVID-19 pandemic and tended to rebound during the second half of the year. However, the number of outpatient visits in the pediatric department decreased by 37.5% overall throughout the COVID-19 period and continued to decline in the second half of the year. The number of outpatients with infectious diseases decreased significantly (35.9%) compared to noninfectious diseases (cancer, 5.0%; circulatory disease, 4.1%). In addition, the number of outpatient visits due to viral diseases continued to decline, while the incidence of bacterial diseases increased rapidly in the second half of the year.This study confirmed that the number of outpatient visits due to bacterial or viral infections decreased throughout the COVID-19 crisis. Therefore, expanding public health and telemedicine services is necessary to prevent secondary health problems caused by essential medical use restrictions.

Effects of Carbonated Water Concentration on Swallowing Function in Healthy Adults.

In this study, we investigated the effects of carbonated water concentration on swallowing function using surface electromyography (sEMG). Healthy subjects (n = 52, 26.77 ± 3.21 years old) were asked to perform two swallows each of noncarbonated water, low-concentration carbonated water, medium-concentration carbonated water, and high-concentration carbonated water. Onset time, the mean sEMG activity amplitude, and duration of muscle activity in each swallow were measured and analyzed for orbicularis oris, masseter, submental muscle complex and infrahyoid muscles. Onset time significantly decreased and mean sEMG activity amplitude significantly increased with carbonation concentration. Therefore, stimulation with carbonation can be effective for modulating a faster and stronger swallow in the oral and pharyngeal phases of swallowing, and its effect on amplitude was greater in the oral phase than in the pharyngeal phase.Clinical Trials Registration This study is registered with Clinical Research Information Service (KCT0005925).

Possible Existence of Cochlear Synaptopathy in Patients Completely Recovered from Idiopathic Sudden Sensorineural Hearing Loss.

Cochlear synaptopathy refers to a subclinical hearing pathology which could potentially explain hearing difficulties within the normal hearing threshold; it is also called "hidden hearing loss". We hypothesized that a temporary threshold shift in sudden sensorineural hearing loss (ISSNHL) also affects the function in the synapse. The aim of the study was to evaluate the presence of cochlear synaptopathy in patients who had completely recovered from unilateral SSNHL Nineteen patients who had completely recovered from ISSNHL from January 2018 to June 2021 were assessed. Complete recovery was established by pure tone audiometry (PTA) 3 months after treatment, according to the American Academy of Otolaryngology-Head and Neck Surgery criteria. Subjects completed the pure tone audiometry, speech audiometry and auditory brain stem response (ABR) test, and completed a questionnaire regarding hearing loss after hearing recovery. The ABR amplitudes of wave I and wave V, and the ratio of wave I/V of both ears (recovered side and healthy side) were assessed. A visual analog scale (VAS) and a hidden hearing loss questionnaire were used to evaluate subjective hearing difficulty. The ABR waves I of the recovered ears had a significantly lower amplitude (p = 0.002) than those of the healthy side, whereas there was no difference in wave V (p = 0.985) or in the ratio of wave I/V (p = 0.107). Some patients still felt mild hearing difficulty although their PTA results were normal, but there was no clear relationship between the VAS score, wave I amplitude and speech recognition scores. The present findings point to the possible existence of cochlear synaptopathy in ears that have completely recovered from unilateral sudden sensorineural hearing loss. We suggest that the causes of cochlear synaptopathy and of idiopathic sudden hearing loss may have something in common.

Vestibular mapping assessment in idiopathic sudden sensorineural hearing loss.

OBJECTIVE: The aim of this study was to investigate patterns of semicircular canal (SCC) and otolith organ dysfunction by vestibular mapping, and to determine the clinical implications of treatment outcomes in idiopathic sudden sensorineural hearing loss (ISSNHL). METHODS: We retrospectively reviewed 135 consecutive patients diagnosed with ISSNHL from January 2016 to December 2020. Patients underwent video-head impulse tests (vHIT) for each SCC, cervical vestibular-evoked myogenic potential test for the saccules, ocular vestibular-evoked myogenic potential test for the utricles, and hearing tests. Hearing outcomes were evaluated according to the American Academy of Otolaryngology-Head and Neck Surgery criteria and factors associated with prognosis were assessed. We also conducted vestibular mapping assessments and hierarchical cluster analysis. RESULTS: Overall, utricular impairment (76, 56.3%) was the most frequent diagnosis in the 135 ISSNHL patients, followed by saccular impairment (59, 43.7%) and posterior SCC impairment (30, 22.2%). The mean number of affected end organs was 1.37 ± 1.24, with higher numbers in the complete recovery group than in the partial/no recovery groups. In a multivariate analysis, higher initial hearing level and abnormal vHIT results in the posterior SCC were associated with poor prognosis in ISSNHL. In hierarchical cluster analysis, horizontal SCC and anterior SCC showed the highest similarity but were in different clusters than posterior SCC, and the saccule and utricle were in separate clusters from the three SCCs. CONCLUSIONS: The vestibular end organ showed various patterns of dysfunction in patients with ISSNHL. Of the five vestibular end organs, only abnormal posterior SCC was associated with poor prognosis for hearing recovery.

Incidence of peripheral vestibular disorders in individuals with obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) could influence peripheral vestibular function adversely via intermittent hypoxia and its consequences. OBJECTIVE: This study aimed to evaluate the risk of peripheral vestibular disorders in OSA using a nationwide population-based retrospective cohort study. METHODS: The National Health Insurance Service-National Sample Cohort represents the entire Korean population from 2002 to 2015. OSA was defined as individuals who had used medical services twice or more under a diagnosis of OSA(G47.33 in ICD-10). A comparison cohort consisted of socio-demographically matched non-OSA subjects in a ratio of 1:4. The incidences of benign paroxysmal positional vertigo(BPPV), Meniere's disease, and vestibular neuritis were evaluated in each cohort. RESULTS: A total of 2,082 individuals with OSA and 8,328 matched non-OSA subjects were identified. The incidence rates(IRs) of peripheral vertigo in OSA and non-OSA were 149.86 and 23.88 per 10,000 persons, respectively (Ratio of IR, IRR = 6.28, 95%CI 4.89 to 8.08). In multivariable analysis, the risk of peripheral vertigo was significantly higher in OSA(adjusted HR = 6.64, 95%CI 5.20 to 8.47), old age(adjusted HR = 1.03, 95%CI 1.02 to 1.04), female sex(adjusted HR = 1.92, 95%CI 1.48 to 2.50), and comorbidities(adjusted HR = 1.09, 95%CI 1.003 to 1.19). The IRRs of each vestibular disorder in the two groups were 7.32(95%CI 4.80 to 11.33) for BPPV, 3.61(95%CI 2.24 to 5.81) for Meniere's disease, and 9.51(95%CI 3.97 to 25.11) for vestibular neuritis. CONCLUSIONS: Subjects diagnosed with OSA had a higher incidence of peripheral vestibular disorders than those without OSA, according to national administrative claims data. It is recommended to take peripheral vertigo into account when counseling OSA.

External Validation of Deep Learning Algorithm for Detecting and Visualizing Femoral Neck Fracture Including Displaced and Non-displaced Fracture on Plain X-ray.

This study aimed to develop a method for detection of femoral neck fracture (FNF) including displaced and non-displaced fractures using convolutional neural network (CNN) with plain X-ray and to validate its use across hospitals through internal and external validation sets. This is a retrospective study using hip and pelvic anteroposterior films for training and detecting femoral neck fracture through residual neural network (ResNet) 18 with convolutional block attention module (CBAM) + + . The study was performed at two tertiary hospitals between February and May 2020 and used data from January 2005 to December 2018. Our primary outcome was favorable performance for diagnosis of femoral neck fracture from negative studies in our dataset. We described the outcomes as area under the receiver operating characteristic curve (AUC), accuracy, Youden index, sensitivity, and specificity. A total of 4,189 images that contained 1,109 positive images (332 non-displaced and 777 displaced) and 3,080 negative images were collected from two hospitals. The test values after training with one hospital dataset were 0.999 AUC, 0.986 accuracy, 0.960 Youden index, and 0.966 sensitivity, and 0.993 specificity. Values of external validation with the other hospital dataset were 0.977, 0.971, 0.920, 0.939, and 0.982, respectively. Values of merged hospital datasets were 0.987, 0.983, 0.960, 0.973, and 0.987, respectively. A CNN algorithm for FNF detection in both displaced and non-displaced fractures using plain X-rays could be used in other hospitals to screen for FNF after training with images from the hospital of interest.

An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases.

The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the "ResNet18 + Shuffle" network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.