Objectives: To develop a multiclass-classifier deep learning model and website for distinguishing tympanic membrane (TM) pathologies based on otoscopic images. Methods: An otoscopic image database developed by utilizing publicly available online images and open databases was assessed by convolutional neural network (CNN) models including ResNet-50, Inception-V3, Inception-Resnet-V2, and MobileNetV2. Training and testing were conducted with a 75:25 breakdown. Area under the curve of receiver operating characteristics (AUC-ROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to compare different CNN models’ performances in classifying TM images. Results: Our database included 400 images, organized into normal (n = 196) and abnormal classes (n = 204), including acute otitis media (n = 116), otitis externa (n = 44), chronic suppurative otitis media (n = 23), and cerumen impaction (n = 21). For binary classification between normal versus abnormal TM, the best performing model had average AUC-ROC of 0.902 (MobileNetV2), followed by 0.745 (Inception-Resnet-V2), 0.731 (ResNet-50), and 0.636 (Inception-V3). Accuracy ranged between 0.73–0.77, sensitivity 0.72–0.88, specificity 0.58–0.84, PPV 0.68–0.81, and NPV 0.73–0.83. Macro-AUC-ROC for MobileNetV2 based multiclass-classifier was 0.91, with accuracy of 66%. Binary and multiclass-classifier models based on MobileNetV2 were loaded onto a publicly accessible and user-friendly website (https://headneckml.com/tympanic). This allows the readership to upload TM images for real-time predictions using the developed algorithms. Conclusions: Novel CNN algorithms were developed with high AUC-ROCs for differentiating between various TM pathologies. This was further deployed as a proof-of-concept publicly accessible website for real-time predictions.

Objectives: To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence.Methods: Seven hundred eighty‐nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four‐layer 9‐5‐10‐1 feedforward backpropagation ANN for prediction of recurrence.Results: The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases.Conclusion: The constructed ANN model was superior to logistic regression in predicting patient‐answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.

Objectives: To evaluate the presence of migraine features in patients with persistent postural-perceptual dizziness (PPPD). Methods: In a retrospective survey study, consecutive patients presenting to a tertiary care neurotology clinic during an 18-month period were given questionnaires about headache and dizziness symptoms. The survey responses plus history and examination of the patient were used to diagnose patients with PPPD. The prevalence of migraine headache, vestibular migraine (VM), and migraine characteristics was evaluated. Results: In total, 36 subjects with PPPD were included in the study. The mean age of the subjects was 56 ± 16 years with a female (72%) predominance. A total of 19 (53%) patients met the International Classification of Headache Disorders criteria for migraine headache, and 6 of those (17%) met the criteria for definite VM. Of the patients who did not meet full migraine headache criteria, 6 (17%) patients met 4 of 5 criteria, and 5 (14%) patients met 3 of 5 criteria. There was no significant difference between PPPD patients who fulfilled full migraine headache criteria and those who did not in sensitivity to light, sound, smells, weather changes, feelings of mental fog/confusion, and sinus pain/facial pressure. Conclusions: This study demonstrates that a majority of patients with PPPD fulfill the criteria for migraine headache. A large proportion of PPPD patients who do not meet the full criteria for migraine headache still meet a majority of the migraine headache criteria. This suggests an association between the 2 conditions. PPPD may be a part of the spectrum of otologic migraine, where migraine manifests as otologic symptoms.

Objective: Tinnitus is defined as the perception of sound in the absence of an external source. We propose the hypothesis that migraine can cause exacerbation of tinnitus in some patients.Methods: English literature from PubMed has been reviewed.Results: Studies have reported a high prevalence of cochlear symptoms in patients with migraine headaches and up to 45% of tinnitus patients have been shown to concomitantly suffer from migraine. Both conditions are thought to stem from central nervous system disturbances, involving disruption of the auditory and trigeminal nerve pathways. One proposed mechanism of this association is the modulation of sound sensitivity by trigeminal nerve activation of the auditory cortex during migraine attacks, resulting in tinnitus fluctuation in some patients. Increased brain and inner ear vascular permeability resulting from trigeminal nerve inflammation, can also cause observed headache and auditory symptoms. Tinnitus and migraine also share a number of symptom triggers including stress, sleep disturbances, and dietary factors. These shared features may help explain promising results of migraine therapies for the treatment of tinnitus.Conclusion: Given the complex association between tinnitus and migraine, further investigation is needed to identify the underlying mechanisms and determine the optimal treatment strategies for managing migraine‐related tinnitus patients.