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Background: Otitis media includes several common inflammatory conditions of the middle ear that can have severe complications if left untreated. Correctly identifying otitis media can be difficult and a screening system supported by machine learning would be valuable for this prevalent disease. This study investigated the performance of a convolutional neural network in screening for otitis media using digital otoscopic images labelled by an expert panel.

Methods: Five experienced otologists diagnosed 347 tympanic membrane images captured with a digital otoscope. Images with a majority expert diagnosis (n = 273) were categorized into three screening groups Normal, Pathological and Wax, and the same images were used for training and testing of the convolutional neural network. Expert panel diagnoses were compared to the convolutional neural network classification. Different approaches to the convolutional neural network were tested to identify the best performing model.

Results: Overall accuracy of the convolutional neural network was above 0.9 in all except one approach. Sensitivity to finding ears with wax or pathology was above 93% in all cases and specificity was 100%. Adding more images to train the convolutional neural network had no positive impact on the results. Modifications such as normalization of datasets and image augmentation enhanced the performance in some instances.

Conclusions: A machine learning approach could be used on digital otoscopic images to accurately screen for otitis media.

Background: There is a lack of hearing health care globally, and tele-audiology and mobile technologies have been proposed as important strategies to reduce the shortfall. Objectives: To investigate the accuracy and reliability of smartphone self-test audiometry in adults, in community clinics in low-income settings.

Methods: A prospective, intra-individual, repeated measurements design was used. Sixty-three adult participants (mean age 52 years, range 20-88 years) were recruited from ENT and primary health care clinics in a low-income community in Tshwane, South Africa. Air conduction hearing thresholds for octave frequencies 0.5 to 8 kHz collected with the smartphone self-test in non-sound treated environments were compared to those obtained by reference audiometry.

Results: The overall mean difference between threshold seeking methods (ie, smartphone thresholds subtracted from reference) was -2.2 dB HL (n = 467 thresholds, P = 0.00). Agreement was within 10 dB HL for 80.1% (n = 467 thresholds) of all threshold comparisons. Sensitivity for detection hearing loss >40 dB HL in one ear was 90.6% (n = 84 ears), and specificity 94.2% (n = 84 ears).

Conclusion: Smartphone self-test audiometry can provide accurate and reliable air conduction hearing thresholds for adults in community clinics in low-income settings.

Severe corona virus disease 2019 (Covid-19) is characterized by inflammation of the lungs with increasing respiratory impairment. In fatal Covid-19, lungs at autopsy have been filled with a clear liquid jelly. However, the nature of this finding has not yet been determined.The aim of the study was to demonstrate if the lungs of fatal Covid-19 contain hyaluronan as it is associated with inflammation and acute respiratory distress syndrome (ARDS) and may have the appearance of liquid jelly.Lung tissue obtained at autopsy from three deceased Covid-19 patients was processed for hyaluronan histochemistry using a direct staining method and compared with staining in normal lung tissue.Stainings confirmed that hyaluronan is obstructing alveoli with presence in exudate and plugs, as well as in thickened perialveolar interstitium. In contrast, normal lungs only showed hyaluronan in intact alveolar walls and perivascular tissue. This is the first study to confirm prominent hyaluronan exudates in the alveolar spaces of Covid-19 lungs, supporting the notion that the macromolecule is involved in ARDS caused by SARS-CoV-2. The present finding may open up for new treatment options in severe Covid-19, aiming at reducing the presence and production of hyaluronan in the lungs.

Background: Hyaluronan (HA) is a glycosaminoglycan with viscoelastic properties necessary for vocal fold (VF) vibration and voice production. Changes in HAs molecular mass, possibly related to human papilloma virus, could affect formation/persistence of recurrent respiratory papillomatosis (RRP).

Aims/Objective: Describing mass and localization of HA and localization of HA receptor CD44 in VF and false vocal folds (FVF) in RRP.

Materials and Methods: Biopsies from VF and FVF from 24 RRP patients. Twelve were studied with histo-/immunohistochemistry for HA and CD44 in epithelium, stroma and RRP lesions. Twelve samples were analyzed for HA molecular mass distribution with gas-phase-electrophoretic-molecular-mobility-analyzer (GEMMA).

Results: Three of 23 stains (VF and FVF combined) showed faint HA staining in the epithelium; there was more extensive staining in the stroma. CD44 was present throughout all areas in FVF and VF, it did not concur with HA. GEMMA analysis revealed very high mass HA (vHMHA) with more varying amounts in VF.

Conclusions/Significance: HA was mainly distributed in the stroma. CD44 not binding to HA might explain the non-inflammatory response described in RRP. Possibly crosslinked vHMHA was seen in VF and FVF, with more variable amounts in VF samples. Counteracting HA crosslinking could become a treatment option in RRP.

背景：透明质酸（HA）是一种糖胺聚糖, 具有声带（VF）振动和发声所必需的粘弹性。HA 分子量的变化可能与人乳头瘤病毒相关, 还可能影响复发性呼吸道乳头状瘤病（RRP）的形成或持续。

目的：描述HA的量和定位, HA受体CD44在VF中的定位和假性声带（FVF）在RRP中的定位。

材料和方法：24名RRP患者的VF和FVF的活组织检查。用组织/免疫组织化学方法研究12个样品的上皮、基质和RRP病变中的HA和CD44。用气-相-电泳 - 分子-迁移率-分析仪（GEMMA）分析另12个样品的HA分子量分布。

结果：23个染色中的3个（VF和FVF组合）在上皮细胞中显示出微弱的HA染色;基质中有更强的染色。 CD44存在于FVF和VF的所有区域, 它与HA不同时存在。 GEMMA分析显示非常高量的HA（vHMHA）, 它在VF中的量多变。

结论/意义：HA主要分布在基质中。 CD44不与HA接合可能解释所描述的RRP中的非炎症反应。在VF和FVF中观察到可能交合的vHMHA, 而在VF样品中具有更多的变量。抗HA交合可能成为RRP的治疗选择。

Odds media is one of the most common childhood illnesses. Access to ear specialists and specialist equipment is rudimentary in many third world countries, and general practitioners do not always have enough experience in diagnosing the different otitis medias. In this paper a system recently proposed by three of the authors for automated diagnosis of middle ear pathology, or otitis media, is extended to enable the use of the system on a smartphone with an Internet connection. In addition, a neural network is also proposed in the current system as a classifier, and compared to a decision tree similar to what was proposed before. The system is able to diagnose with high accuracy (1) a normal tympanic membrane, (2) obstructing wax or foreign bodies in the external ear canal (W/O), (3) acute otitis media (AOM), (4) otitis media with effusion (OME) and (5) chronic suppurative otitis media (CSOM). The average classification accuracy of the proposed system is 81.58% (decision tree) and 86.84% (neural network) for images captured with commercial video-otoscopes, using 80% of the 389 images for training, and 20% for testing and validation. 

The main aim of this study was to evaluate if the level of investigation with MRT or BRA (Brainstem Response Audiometry) is adequate in relation to the number of vestibular schwannomas (VS) found in a county like Dalarna (277 000 inhabitants) in Sweden. The reason to investigate was asymmetrical sensorineural hearing loss (ASHL) defined as a one-sided hearing loss exceeding 10 dB compared to the contralateral side. Between 2007–2014 a total of 55 patients (incidence 2.5/100 000) were diagnosed with a VS in the county. The local ENT department found two thirds of these patients. In the year of 2010, MRT was advocated by SBU (Swedish Agency for Health Technology Assessment and Assessment of Social Services) as the primary step of investigation. During 2011–2014, 981 patients underwent MRT and 482 patients BRA, because of ASHL. Seventeen VS were found, meaning that 58 MRT and 29 BRA were conducted to find one VS. According to earlier studies, only one third of VS show growth tendency. Taking this into consideration, 174 (58 × 3) investigations with MRT will only diagnose approximately 1 patient with a growing VS. We therefore question today’s level of investigation and ask for new uniform recommendations on how to investigate for VS in cases of ASHL.

Objective: Video-otoscopy is rapidly developing as a new method to diagnose common ear disease and can be performed by trained health care facilitators as well as by clinicians. This study aimed to compare remote asynchronous assessments of video-otoscopy with otoscopy performed by a general practitioner. Method: Children, aged 2-16 years, attending a health center in Johannesburg, South Africa, were examined. An otologist performed otomicroscopy and a general practitioner performed otoscopy. Video-otoscopy was performed by a health care facilitator and video sequences were stored on a server for assessment by the same general practitioner 4 and 8 weeks later. At all examinations, a diagnosis was set and the tympanic membrane appearance was graded using the OMgrade-scale. The otologist's otomicroscopic diagnosis was set as reference standard to compare the accuracy of the two otoscopic methods. Results: Diagnostic agreement between otologist's otomicroscopic examination and the general practitioner's otoscopic examination was substantial (kappa 0.66). Agreement between onsite otomicroscopy and the general practitioners asynchronous video assessments were also substantial (kappa 0.70 and 0.80). Conclusion: Video-otoscopy performed by a health care facilitator and assessed asynchronously by a general practitioner had similar or better accuracy compared to face-to-face otoscopy performed by a general practitioner.

OBJECTIVES/HYPOTHESIS: To study the hyaluronan occurrence in human vocal folds, with special regards to gender and smoking and to discuss the implications of findings.

STUDY DESIGN: This is a descriptive/morphologic study.

METHODS: Sixteen cadaveric vocal folds from eight individuals between 58 and 90 years old (six women and two men) were removed and studied morphologically. Three of the individuals had been cigarette smokers. A direct method for hyaluronan histochemistry using a hyaluronan-binding protein probe (HABP) was used to visualize the polysaccharide. Five examiners performed an analysis of the intensities of hyaluronan staining, independently.

RESULTS: We observed intense hyaluronan staining of the vocal folds of which those from women stained considerably stronger than those from men. Stratified squamous epithelium stained for hyaluronan in all sections, whereas respiratory epithelium only stained weakly or not at all. The highest accumulation of hyaluronan occurred subepithelially in the lamina propria, corresponding to Reinke's space. It was observed that vocal folds from smokers were more intensively stained than those from nonsmokers.

CONCLUSIONS: Hyaluronan is found in all layers of the human vocal fold. Contradictory to earlier studies, hyaluronan was visualized in squamous epithelium, where it may function as an impact protector. The occurrence of hyaluronan in smokers may have implications in the development of vocal fold inflammation and tumor initiation as hyaluronan is an important molecule in these processes.

Background: Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. Methods: A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. Findings: An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. Interpretation: The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (similar to 64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

OBJECTIVE: To validate a calibrated smartphone-based hearing test in a sound booth environment and in primary health-care clinics.

DESIGN: A repeated-measure within-subject study design was employed whereby air-conduction hearing thresholds determined by smartphone-based audiometry was compared to conventional audiometry in a sound booth and a primary health-care clinic environment.

STUDY SAMPLE: A total of 94 subjects (mean age 41 years ± 17.6 SD and range 18-88; 64% female) were assessed of whom 64 were tested in the sound booth and 30 within primary health-care clinics without a booth.

RESULTS: In the sound booth 63.4% of conventional and smartphone thresholds indicated normal hearing (≤15 dBHL). Conventional thresholds exceeding 15 dB HL corresponded to smartphone thresholds within ≤10 dB in 80.6% of cases with an average threshold difference of -1.6 dB ± 9.9 SD. In primary health-care clinics 13.7% of conventional and smartphone thresholds indicated normal hearing (≤15 dBHL). Conventional thresholds exceeding 15 dBHL corresponded to smartphone thresholds within ≤10 dB in 92.9% of cases with an average threshold difference of -1.0 dB ± 7.1 SD.

CONCLUSIONS: Accurate air-conduction audiometry can be conducted in a sound booth and without a sound booth in an underserved community health-care clinic using a smartphone.