A New Community-to-Hospital Specialist Otology Pathway Using Telemedicine: A Pilot Study of 50 Patients.

INTRODUCTION: Pathway innovation using smartphone otoscopy and tablet-based audiometry technologies to deliver ear and hearing services via trained audiologists may improve efficiency of the service. An ENT-integrated-community-ear service (ENTICES-combining community audiology management, remote ENT review and novel technologies) was piloted. We aimed to assess the efficiency and safety of ENTICES. METHOD: ENTICES was a community-based and audiologist-led pathway. Patients with otological symptoms were self-referred to this service. Smartphone otoscopy and tablet-based audiograms were performed. Two otologists reviewed all decisions made in the community by audiologists based on video-otoscopy, hearing tests and chart reviews. Data on the first 50 consecutive new patients attending either consultant-led hospital otology clinics (HOC), audiologist-led hospital advanced audiology diagnostics (AAD) or ENTICES clinics were collected between 1 August 2021 and 31 December 2021. Data were collected through chart reviews and questionnaires to compare the three pathways with respect to efficiency, patient satisfaction, technology utility and safety. RESULTS: No audiology-led ENTICES decisions were amended by hospital otologists following remote review. Remote review of video-otoscopy with history was sufficient for a diagnosis in 80% of cases. Adding hearing tests and standardised history increased the diagnostic yield to 98%. Patient satisfaction scores showed 100% service recommendation. The cost per patient, per visit, was £83.36, £99.07 and £69.72 for AAD, HOC or ENTICES, respectively. CONCLUSION: ENTICES provides a safe ear and hearing service that patients rated highly. Thirty-two per cent of hospital otology patients were eligible for this service. For those patients, ENTICES is 20% more cost-effective and can reduce the number of clinic visits by up to 60% compared with HOC.

Pediatric tympanostomy tube assessment via deep learning.

PURPOSE: Tympanostomy tube (TT) placement is the most frequently performed ambulatory surgery in children under 15. After the procedure it is recommended that patients follow up regularly for "tube checks" until TT extrusion. Such visits incur direct and indirect costs to families in the form of days off from work, copays, and travel expenses. This pilot study aims to compare the efficacy of tympanic membrane (TM) evaluation by an artificial intelligence algorithm with that of clinical staff for determining presence or absence of a tympanostomy tube within the TM. METHODS: Using a digital otoscope, we performed a prospective study in children (ages 10 months-10 years) with a history of TTs who were being seen for follow up in a pediatric otolaryngology clinic. A smartphone otoscope was used by study personnel who were not physicians to take ear exam images, then through conventional otoscopic exam, ears were assessed by a clinician for tubes being in place or tubes having extruded from the TM. We trained and tested a deep learning (artificial intelligence) algorithm to assess the images and compared that with the clinician's assessment. RESULTS: A total of 123 images were obtained from 28 subjects. The algorithm classified images as TM with or without tube in place. Overall classification accuracy was 97.7 %. Recall and precision were 100 % and 96 %, respectively, for TM without a tube present, and 95 % and 100 %, respectively, for TM with a tube in place. DISCUSSION: This is a promising deep learning algorithm for classifying ear tube presence in the TM utilizing images obtained in awake children using an over-the-counter otoscope available to the lay population. We are continuing enrollment, with the goal of building an algorithm to assess tube patency and extrusion.

Recreational music exposure and hearing health in young adults.

OBJECTIVE: This study aimed to compare daily and total recreational music exposure levels and extended-spectrum audiogram results in young adults without pre-existing hearing problems. DESIGN: The study included healthy volunteers aged 18-25 with no known ear disease or hearing loss. Participants completed a questionnaire, underwent otoscopic and tympanometric examinations, and determined preferred music volumes in an audiometry booth using calibrated music samples of their preferred genres. Hearing thresholds up to 16 kiloHertz (kHz) were measured. Daily music exposure for each participant was normalized to 8 h to calculate a time-weighted average of 8 h (TWA8). Total exposure (TE) was calculated by multiplying TWA8 by the number of years of music listening. RESULTS: A total of 32.4% of participants had TWA8s above 65 dB. Their hearing thresholds at 125, 250, 500, and 16,000 Hz and the average of 125 Hz-8 kHz were significantly higher. Participants with TWA8s above 65 dB were also more prone to speaking loudly and experiencing communication difficulties on the phone. Those with a TE of more than 400 experienced significantly more speech discrimination difficulty in noisy environments and temporary hearing loss/tinnitus after exposure to loud music. Participants with a TE above 700 had worse thresholds at 4, 14, and 16 kHz frequencies, as well as 125-8000 Hz and 500-4000 Hz averages compared to those with a TE below 700. CONCLUSIONS: This study provides evidence that recreational music with much lower exposure levels than the universally accepted TWA8 of 85 dB could negatively impact hearing in healthy young adults. Therefore, maintaining a maximum TWA8 of 65 dB is recommended.

"Seeing inside out": revealing the effectiveness of otoscopy training in virtual reality enhanced practical exams - a randomized controlled trial.

BACKGROUND: The study aimed to assess the impact of different training modalities on otoscopy performance during a practical exam using a high-fidelity simulator and to determine if objective evaluation of otoscopy is feasible using a simulator that records insertion depth and tympanic membrane coverage. METHODS: Participants were assigned to one of four groups: control and three intervention groups with varying training approaches. Participants received otoscopy training and then were assessed through a practical exam on a high-fidelity simulator that uses virtual reality to visualize the ear canal and middle ear. Performance was evaluated using a modified Objective Structured Assessment of Technical Skills checklist and Integrated Procedural Performance Instrument checklist. Insertion depth, tympanic membrane coverage, and correct diagnosis were recorded. Data were tested for normal distribution using the Shapiro-Wilk test. One-way ANOVA and, for non-normally distributed data, Kruskal-Wallis test combined with Dunn's test for multiple comparisons were used. Interrater reliability was assessed using Cohen's κ and Intraclass correlation coefficient. RESULTS: All groups rated their training sessions positively. Performance on the OSATS checklist was similar among groups. IPPI scores indicated comparable patient handling skills. The feedback group examined larger tympanic membrane areas and had higher rates of correct diagnosis. The correct insertion depth was rarely achieved by all participants. Interrater reliability for OSATS was strong. IPPI reliability showed good correlation. CONCLUSION: Regardless of training modality, participants perceived learning improvement and skill acquisition. Feedback improved examination performance, indicating simulator-guided training enhances skills. High-fidelity simulator usage in exams provides an objective assessment of performance.

Bone cement repair of malleus handle fractures: Intraoperative video and case report of two patients.

Isolated malleus fractures are a rare occurrence with few reported cases in the literature. Symptoms include sudden otalgia, hearing loss, tinnitus and aural fullness. Work-up and diagnosis are based on a combination of thorough anamnesis and careful otoscopic evaluation or high-resolution computer tomography. We present two cases of isolated malleus handle fractures who were diagnosed based on a combination of pneumatic otoscopy and tympanometry. Both fractures were surgically repaired using hydroxyapatite bone cement as showcased in the supplemental video material. Post-operative audiometry showed improvement in the pure-tone-average of both patients as well as normalisation of tympanometry. Isolated malleus fracture should be suspected in cases of sudden hearing loss and tinnitus following digital manipulation of the outer ear canal together with a conductive hearing loss with a mostly high-frequent air-bone-gap and hypercompliant tympanometry with hypermobility of the tympanic membrane on pneumatic insufflation. Surgical repair of the fracture using bone cement has good hearing outcomes and leads to improvement in auditory symptoms.

Algorithm-Driven Tele-otoscope for Remote Care for Patients With Otitis Media.

OBJECTIVE: The COVID-19 pandemic has spurred a growing demand for telemedicine. Artificial intelligence and image processing systems with wireless transmission functionalities can facilitate remote care for otitis media (OM). Accordingly, this study developed and validated an algorithm-driven tele-otoscope system equipped with Wi-Fi transmission and a cloud-based automatic OM diagnostic algorithm. STUDY DESIGN: Prospective, cross-sectional, diagnostic study. SETTING: Tertiary Academic Medical Center. METHODS: We designed a tele-otoscope (Otiscan, SyncVision Technology Corp) equipped with digital imaging and processing modules, Wi-Fi transmission capabilities, and an automatic OM diagnostic algorithm. A total of 1137 otoscopic images, comprising 987 images of normal cases and 150 images of cases of acute OM and OM with effusion, were used as the dataset for image classification. Two convolutional neural network models, trained using our dataset, were used for raw image segmentation and OM classification. RESULTS: The tele-otoscope delivered images with a resolution of 1280 × 720 pixels. Our tele-otoscope effectively differentiated OM from normal images, achieving a classification accuracy rate of up to 94% (sensitivity, 80%; specificity, 96%). CONCLUSION: Our study demonstrated that the developed tele-otoscope has acceptable accuracy in diagnosing OM. This system can assist health care professionals in early detection and continuous remote monitoring, thus mitigating the consequences of OM.

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.

Importance: Acute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple neural networks have been developed to recognize the presence of AOM with limited clinical application. Objective: To develop and internally validate an artificial intelligence decision-support tool to interpret videos of the tympanic membrane and enhance accuracy in the diagnosis of AOM. Design, Setting, and Participants: This diagnostic study analyzed otoscopic videos of the tympanic membrane captured using a smartphone during outpatient clinic visits at 2 sites in Pennsylvania between 2018 and 2023. Eligible participants included children who presented for sick visits or wellness visits. Exposure: Otoscopic examination. Main Outcomes and Measures: Using the otoscopic videos that were annotated by validated otoscopists, a deep residual-recurrent neural network was trained to predict both features of the tympanic membrane and the diagnosis of AOM vs no AOM. The accuracy of this network was compared with a second network trained using a decision tree approach. A noise quality filter was also trained to prompt users that the video segment acquired may not be adequate for diagnostic purposes. Results: Using 1151 videos from 635 children (majority younger than 3 years of age), the deep residual-recurrent neural network had almost identical diagnostic accuracy as the decision tree network. The finalized deep residual-recurrent neural network algorithm classified tympanic membrane videos into AOM vs no AOM categories with a sensitivity of 93.8% (95% CI, 92.6%-95.0%) and specificity of 93.5% (95% CI, 92.8%-94.3%) and the decision tree model had a sensitivity of 93.7% (95% CI, 92.4%-94.9%) and specificity of 93.3% (92.5%-94.1%). Of the tympanic membrane features outputted, bulging of the TM most closely aligned with the predicted diagnosis; bulging was present in 230 of 230 cases (100%) in which the diagnosis was predicted to be AOM in the test set. Conclusions and Relevance: These findings suggest that given its high accuracy, the algorithm and medical-grade application that facilitates image acquisition and quality filtering could reasonably be used in primary care or acute care settings to aid with automated diagnosis of AOM and decisions regarding treatment.

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.

PURPOSE: Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery. METHODS: CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances. RESULTS: The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of 0.65 ± 0.60 mm for the incus and 0.48 ± 0.32 mm for the round window. The average Hausdorff distance for these 2 targets was 0.98 ± 0.60 mm and 0.78 ± 0.34 mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18 s. CONCLUSIONS: A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Remote paediatric ear examination comparing video-otoscopy and still otoscopy clinician rated outcomes.

OBJECTIVE: Telemedicine, particularly real time video-otoscopy in rural and remote Australia holds great potential in assessing and managing otology conditions. There is good evidence of store and forward images for assessment, however limited evidence exists for the use of real-time video-otoscopy. The objective of this study was to assess the validity of using real time video-otoscopy, compared to standard store and forward still image otoscopy, in a paediatric population. METHOD: Fifty-two paediatric tympanic membranes in 27 patients were examined and photographed by a telehealth facilitator with prior otoscope training. This occurred at two rural Western Australian health centre sites. These images were stored and forwarded to a tertiary paediatric hospital for otolaryngology department assessment on the day of real-time video-otoscopy consultation. During this consultation the same twenty-seven patients underwent real-time video-otoscopy assessment, which was recorded. Across six domains including, image quality, focus, light, cerumen amount, field of view and tympanic membrane landmarks, real-time video-otoscopy was compared against still image capture. The recording of each real-time video-otoscopy and still image tympanic membrane was assessed by two otology specialists for the ability to diagnose each as either normal or abnormal. An inter-rater reliability agreement was then calculated. RESULTS: There was greater image adequacy across five of the six domains for real time video-otoscopy compared to standard store and forward otoscopy images. Substantial agreement in diagnosing each tympanic membrane as either normal or abnormal between each rater was evident. CONCLUSION: This study supports the use of real time video-otoscopy during telemedicine consultation. With greater image quality, focus, light, field of view and identification of tympanic membrane landmarks video-otoscopy compared to still images has broad clinical applications. This includes primary assessment of the tympanic membrane and post operative follow-up clinical settings. Video-otoscopy offers a promising new way to over-come barriers in delivering ear health care in rural populations.

Diagnostic accuracy of the video otoscope in tympanic membrane perforation.

OBJECTIVE: The video otoscope has already proven to be useful for the diagnosis of several pathologies, so the objective of this study was to evaluate the diagnostic accuracy of the video otoscope in cases of tympanic membrane perforation. METHODS: This is a diagnostic accuracy study performed at the hearing health division of a tertiary-level referral hospital. Patients older than 8 years of age who had any symptom that could be related to perforation (otalgia, otorrhea, tinnitus, and/or hypoacusis) were invited to participate in the study. Participants were evaluated by three different diagnostic methods (otomicroscope, conventional otoscope, and video otoscope) performed by three different evaluators in a blind fashion. The microscope was considered the reference standard. RESULTS: 176 patients were evaluated, totaling 352 tympanic membranes. Twenty-seven tympanic membrane perforations were diagnosed by the microscope, a prevalence of 7.7%. The video otoscope showed a sensitivity of 85.2% (95% CI 81.5%‒88.9%), specificity of 98.1% (95% CI 96.7%‒99.5%) and accuracy of 97.1% (95% CI 95.4 %-98.8 %). The conventional otoscope showed a sensitivity of 96.3% (95% CI 94.3-98.3), specificity of 98.8% (95% CI 97.7-99.9) and accuracy of 98.6% (95% CI 97.4-99.8). The Kappa value between the microscope and the video otoscope was 0.8 and between the microscope and the conventional otoscope was 0.9. Regarding the participants' perception, 53.4% (p< 0.001) considered the video otoscope as the best method for understanding the tympanic membrane condition presented by them. CONCLUSIONS: The video otoscope showed relevant sensitivity and specificity for clinical practice in the diagnosis of tympanic membrane perforation. Moreover, this is an equipment that can facilitate the patient's understanding of the otologic pathology presented by him/her. In this regard, this method may be important for better patient compliance, requiring further studies to evaluate this hypothesis. LEVEL OF EVIDENCE: Is this diagnostic or monitoring test accurate? (Diagnosis)-Level 2 (Individual cross-sectional studies with consistently applied reference standard and blinding).

Feasibility of a Video Otoscope for Diagnosis of Otologic Pathology in the Pediatric Emergency Department.

OBJECTIVES: Performing pediatric otoscopy can be difficult secondary to patient compliance, which potentiates misdiagnosis and inaccurate treatment of acute otitis media. This study used a convenience sample to assess the feasibility of using a video otoscope for the examination of tympanic membranes in children presenting to a pediatric emergency department. METHODS: We obtained otoscopic videos using the JEDMED Horus + HD Video Otoscope. Participants were randomized to video or standard otoscopy, and a physician completed their bilateral ear examinations. In the video group, physicians reviewed otoscope videos with the patient's caregiver. The caregiver and physician completed separate surveys using a 5-point Likert Scale regarding perceptions of the otoscopic examination. A second physician reviewed each otoscopic video. RESULTS: We enrolled 213 participants in 2 groups (standard otoscopy, n = 94; video otoscopy, n = 119). We used Wilcoxon rank sum, Fisher exact test, and descriptive statistics to compare results across groups. For physicians, there were no statistically significant differences between groups with ease of device use, quality of otoscopic view, or diagnosis. There was moderate agreement between physician video otoscopic view satisfaction and slight agreement between physician video otologic diagnosis. Estimates of length of time to complete the ear examinations were longer more often for the video otoscope compared with standard for both caregivers (OR, 2.00; 95% confidence interval, 1.10-3.70; P = 0.02) and physicians (OR, 3.08; 95% confidence interval, 1.67-5.78; P < 0.01). There were no statistically significant differences between video and standard otoscopy with regard to caregiver perception of comfort, cooperation, satisfaction, or diagnosis understanding. CONCLUSIONS: Caregivers perceive that video otoscopy and standard otoscopy are comparable in comfort, cooperation, examination satisfaction, and diagnosis understanding. Physicians made a wider range of more subtle diagnoses with the video otoscope. However, examination length of time may limit the JEDMED Horus + HD Video Otoscope's feasibility in a busy pediatric emergency department.

Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.

To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep learning methods to identify middle ear disease from otoscopic images, between internal to external performance. 1842 otoscopic images were collected from three independent sources: (a) Van, Turkey, (b) Santiago, Chile, and (c) Ohio, USA. Diagnostic categories consisted of (i) normal or (ii) abnormal. Deep learning methods were used to develop models to evaluate internal and external performance, using area under the curve (AUC) estimates. A pooled assessment was performed by combining all cohorts together with fivefold cross validation. AI-otoscopy algorithms achieved high internal performance (mean AUC: 0.95, 95%CI: 0.80-1.00). However, performance was reduced when tested on external otoscopic images not used for training (mean AUC: 0.76, 95%CI: 0.61-0.91). Overall, external performance was significantly lower than internal performance (mean difference in AUC: -0.19, p ≤ 0.04). Combining cohorts achieved a substantial pooled performance (AUC: 0.96, standard error: 0.01). Internally applied algorithms for otoscopy performed well to identify middle ear disease from otoscopy images. However, external performance was reduced when applied to new test cohorts. Further efforts are required to explore data augmentation and pre-processing techniques that might improve external performance and develop a robust, generalizable algorithm for real-world clinical applications.

Comparative validity of three simulation platforms for objective assessment of otoscopy skills.

OBJECTIVES: To assess the face, construct and content validity of three different platforms for otoscopy skills assessment, using a traditional otoscope with manikin, digital otoscope (Tympahealth) with manikin, and traditional otoscope with a low-cost model ear (SimEar). DESIGN: Prospective mixed methods study. SETTING: Tertiary hospital. PARTICIPANTS: Postgraduate trainees and expert assessors. MAIN OUTCOME MEASURES: Face and Content validity based on expert assessor ranking on each model and their feedback from semi-structured interviews. Construct validity based on Objective Structured Clinical Examination scores. RESULTS: Each platform differed in face, construct and content validity scores, with no one platform consistently outperforming others. Three main themes were identified during thematic analysis of expert assessor interviews: ability to assess what is seen, anatomical reality, and ease of use. The low-cost model showed greatest potential, where modification to include a silicone ear could lead to high validity with marginal increase in cost. CONCLUSION: Several modalities for assessing otoscopy skills exist, each with advantages and disadvantages. Modifications to a low-cost model, for use with either a traditional or digital otoscope, could prove to be the best model.

Machine Learning in Diagnosing Middle Ear Disorders Using Tympanic Membrane Images: A Meta-Analysis.

OBJECTIVE: To systematically evaluate the development of Machine Learning (ML) models and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) using Tympanic Membrane (TM) images. METHODS: PubMed, EMBASE, CINAHL, and CENTRAL were searched up until November 30, 2021. Studies on the development of ML approaches for diagnosing MED using TM images were selected according to the inclusion criteria. PRISMA guidelines were followed with study design, analysis method, and outcomes extracted. Sensitivity, specificity, and area under the curve (AUC) were used to summarize the performance metrics of the meta-analysis. Risk of Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool in combination with the Prediction Model Risk of Bias Assessment Tool. RESULTS: Sixteen studies were included, encompassing 20254 TM images (7025 normal TM and 13229 MED). The sample size ranged from 45 to 6066 per study. The accuracy of the 25 included ML approaches ranged from 76.00% to 98.26%. Eleven studies (68.8%) were rated as having a low risk of bias, with the reference standard as the major domain of high risk of bias (37.5%). Sensitivity and specificity were 93% (95% CI, 90%-95%) and 85% (95% CI, 82%-88%), respectively. The AUC of total TM images was 94% (95% CI, 91%-96%). The greater AUC was found using otoendoscopic images than otoscopic images. CONCLUSIONS: ML approaches perform robustly in distinguishing between normal ears and MED, however, it is proposed that a standardized TM image acquisition and annotation protocol should be developed. LEVEL OF EVIDENCE: NA Laryngoscope, 133:732-741, 2023.

Smartphone-enabled otoscopy: method evaluation in clinical practice.

OBJECTIVE: To assess the diagnostic agreement between smartphone-enabled otoscopy and rigid otoendoscopy in tympanic membrane and middle ear diseases. METHODS: A cross-sectional study was carried out to analyze otoscopies in patients seen at a general otorhinolaryngology (ORL) outpatient clinic, from June to December 2019. Eighty-three images of patients obtained from otoscopies performed through a smartphone device and a rigid endoscope were included, recorded, and stored for further analysis. The images were first analyzed by an experienced otologist, who assigned his diagnostic impression (defined as the gold standard) on each of the images. After this analysis, the images were displayed to a group of secondary raters (an experienced otorhinolaryngologist, a second-year resident in ORL, and a general practitioner). A questionnaire was applied related to each image. RESULTS: There was high agreement between the smartphone device and the otoendoscopy images for all professionals, with a Kappa coefficient of 0.97 (p < 0.001). The smartphone device showed a diagnostic sensitivity of 81.1% and a specificity of 71.1%. As for the otoendoscopy, it showed a sensitivity of 84.7% and a specificity of 72.4%. The image classification as "2 = Good" was the most frequent one, with 34.9% for otoendoscopy and 31.6% for the smartphone device. CONCLUSION: There was a high diagnostic agreement between smartphone device-guided otoscopy and the rigid otoendoscopy, demonstrating the feasibility of using this device in clinical practice.

Clinician-rated quality of video otoscopy recordings and still images for the asynchronous assessment of middle-ear disease.

INTRODUCTION: Video otoscopy plays an important role in improving access to ear health services. This study investigated the clinician-rated quality of video otoscopy recordings and still images, and compared their suitability for asynchronous diagnosis of middle-ear disease. METHODS: Two hundred and eighty video otoscopy image-recording pairs were collected from 150 children (aged six months to 15 years) by an ear, nose, and throat (ENT) specialist, audiologists, and trained research assistants, and independently rated by an audiologist and ENT surgeon. On a five-point scale, clinicians rated the cerumen amount, field of view, quality, focus, light, and gave an overall rating, and asked whether they could make an accurate diagnosis for both still images and recordings. RESULTS: More video otoscopy recordings were rated as 'good' or 'excellent' compared to still images across all domains. The mean difference between the two otoscopic procedures ratings was significant across almost all domains (p < 0.05), except 'cerumen amount'. The suitability to make a diagnosis significantly improved when using recordings (p<0.05). Younger participant age was found to have a significant, negative impact on the ratings across all domains (p < 0.03). The role of the tester conducting video otoscopy did not have a significant impact on the ratings. DISCUSSION: Video otoscopy recordings were found to provide clearer views of the tympanic membrane and increase the ability to make diagnoses, compared to still images, for both audiologists and ENT surgeons. Research assistants with limited practice were able to obtain video otoscopy images and recordings that were comparable to the ones obtained by clinicians.

Improving otoscopy education and diagnostic accuracy: A prospective interventional study.

OBJECTIVE: To assess and improve otoscopy examination skills across various medical specialities who perform otoscopy during their professional practice. METHODS: A pre-intervention survey was created using www.surveymonkey.com, which included several preliminary questions to clarify the participant's speciality and training level, followed by 25 individual otoscopy images. The participants were given 12 possible diagnoses for each otoscopy image and asked to choose the single best answer. After completing the survey, participants were asked to watch an otoscopy teaching session. This teaching session was created with multidisciplinary feedback, and the content included a demonstration video and a didactic lecture. Finally, a post-intervention survey was circulated four weeks later to the same cohort of doctors to assess improvement. RESULTS: A total of 79 pre-intervention surveys were collected with an average score of 53% (range 20-100%). The spectrum of medical specialities that completed the pre-intervention survey included paediatrics, ear, nose, and throat (ENT), emergency medicine, and general practice. The largest cohort of surveys came from senior house officers (SHO). In addition, 78.5% of responses were completed by doctors who had not worked in ENT before. After completing the otoscopy teaching session and at least four weeks after the initial survey, 23 post-intervention surveys were completed with an average score of 66% (range 32-100%), a 13% improvement. CONCLUSIONS: The results of the pre-intervention survey show that many doctors have difficulty diagnosing ear conditions. The implementation of a 25-min teaching session achieved a 13% improvement in the otoscopy knowledge of doctors across a variety of specialities.

Artificial intelligence and tele-otoscopy: A window into the future of pediatric otology.

Telehealth in otolaryngology is gaining popularity as a potential tool for increased access for rural populations, decreased specialist wait times, and overall savings to the healthcare system. The adoption of telehealth has been dramatically increased by the COVID-19 pandemic limiting patients' physical access to hospitals and clinics. One of the key challenges to telehealth in general otolaryngology and otology specifically is the limited physical examination possible on the ear canal and middle ear. This is compounded in pediatric populations who commonly present with middle ear pathologies which can be challenging to diagnose even in the clinic. To address this need, various otoscopes have been designed to allow patients, their parents, or primary care providers to image the tympanic membrane and middle ear, and send data to otolaryngologists for review. Furthermore, the ability of these devices to capture images in digital format has opened the possibility of using artificial intelligence for quick and reliable diagnostic workup. In this manuscript, we provide a concise review of the literature regarding the efficacy of remote otoscopy, as well as recent efforts on the use of artificial intelligence in aiding otologic diagnoses.

Evaluating the inclusion of tympanometry in population-based surveys of hearing loss.

OBJECTIVE: This study aimed to determine the implications of including tympanometry in the Rapid Assessment of Hearing Loss survey protocol. A comparative study design was employed, with findings from otoscopy compared with the results of tympanometry. METHOD: A population-based survey of the prevalence and causes of hearing loss among adults aged over 35 years in The Gambia was conducted. Clinical assessments included air conduction audiometry, otoscopy and clinical history. Otoscopy outcome was recorded and for those with hearing loss, a probable cause was assigned. Following otoscopy, tympanometry was completed. Otoscopy outcome was not changed as a result of tympanometry. Clinician assigned cause was compared to the results of tympanometry. The proportion of causes potentially misclassified by excluding tympanometry was determined. RESULTS: Among people with hearing loss, including tympanometry led to a higher proportion diagnosed with middle-ear conditions. CONCLUSION: The value of adding tympanometry to population-based survey protocols is a higher estimated proportion of hearing loss being attributed to middle-ear disease rather than sensorineural causes. This can inform service needs as more people will be classified as needing medical or surgical services, and a slightly lower number will need rehabilitative services, such as hearing assistive devices. It is highly recommended that tympanometry is included in the protocol.

Endoscopic ear examination improves self-reported confidence in ear examination skills among undergraduate medical students compared with handheld otoscopy.

Objectives: Handheld otoscopy is the standard tool used to teach ear examination in undergraduate and postgraduate medical education. Previous studies have shown that the undergraduate teaching of ear examination with handheld otoscopes is inadequate, resulting in low self-reported levels of student confidence in their diagnostic skills. With the increase in popularity of endoscopic ear surgery, an increasing number of otolaryngologists are using endoscopes for office examinations of the ear due to the method's superior visualization and excellent image qualities. However, medical students usually do not receive exposure to endoscopic ear examination during their undergraduate curriculum. The aim of this study is to assess our preliminary experience with teaching endoscopic ear examination to undergraduate medical students. Methods: A two-hour-long pilot practical course on basic ear examination was administered to undergraduate medical students with little to no previous experience with ear examination. The course was designed to minimize the duration of campus attendance and patient contact during the COVID-19 pandemic. The course included theoretical didactics, exemplary digital endoscopic images and peer physical practice of ear examination with both a handheld otoscope and a 0-degree endoscope. At the end of the course, the students completed a survey questionnaire consisting of eight questions mainly relating to their subjective confidence level with ear examination using either handheld otoscopes or endoscopes and their overall preference for either examination tool. Results: Most students expressed a preference for ear examination with endoscopes over that with handheld otoscopes and reported an improved confidence level in their diagnostic ability with the former technique. The vast majority of students supported the teaching of endoscopic ear examination to future medical students. Conclusion: The findings of this pilot project report and survey study support the early exposure of novice medical learners to endoscopic ear examination, which may help improve the confidence and diagnostic skill of medical students with regard to ear examination. The findings may have implications for the undergraduate teaching of ear examination in the post-COVID-19 era.