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

Background: It is generally accepted that intratympanic injection provides an effective approach to manage severe vertigo in Ménière's disease. Although there are several medications available, that which is the most effective is still subject to debate.Objective: To assess the effectiveness and safety of the different medications used in treatment of Ménière's disease by intratympanic injection using a network meta-analysis.Methods: PubMed, EMBASE, CINAHL and CENTRAL were searched. Only randomized controlled trials that compared the effectiveness of medications used for intratympanic injection to treat Ménière's disease with each other or a placebo were included. The primary outcome assessed was the effectiveness of medication in the management of vertigo Accepted ArticleThis article is protected by copyright. All rights reserved. symptoms. The effectiveness was expressed in terms of risk ratio (RR) with a 95% credible interval (CrI) for individual studies analyzed. Network meta-analyses was performed by Stata version 15.0 using the network package.Results: Nine studies involving 314 patients treated with 5 different medications were included in the present analysis. Number of injections given varied from 1 to 10 and follow-up time from 3 to 28 months. When compared to each other or to a placebo, Gentamicin was found to be the most efficacious medication, followed by Methylprednisolone, Latanoprost, Dexamethasone and Ganciclovir in order of effectiveness.However, no significant difference in efficacy was found between Gentamicin and Methylprednisolone when outcomes from studies with a follow-up time equal to or more than 24 months were analyzed. It was not possible to conduct subgroup and sensitivity analysis because of the limited number of studies that were included. Conclusion:All medications are more effective than a placebo in the treatment of Ménière's disease by intratympanic injection. According to the SUCRA, Gentamicin ranked the most effective, with Gentamicin and Methylprednisolone equally effective in the long-term. When the potential risk of hearing loss induced by Gentamicin is taken into consideration, Methylprednisolone may be the best choice for treatment of Ménière's disease by intratympanic injection.

Objective: To investigate whether acoustic neuroma is associated with noise. Design: PubMed, Cochrane, Embase and CINAHL databases were searched. A meta-analysis was performed to calculate odds ratio (OR) and 95% confidence interval (CI) using quality-effect models. Study sample: A total of 8 studies with moderate or high quality involving 75,571 participants met the inclusion criteria.

Purpose Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction. Methods The authors searched PubMed, EMBASE and Scopus on November 26, 2020. Results Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve. Conclusion In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.