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PURPOSE: Otoacoustic emissions (OAE) are a common screening tool to evaluate cochlear function. Middle ear dysfunction has been shown to impact results of otoacoustic emission testing, but there are limited data on the effect of tympanostomy tubes on OAE. The purpose of this study was to determine whether tympanostomy tube placement significantly improved OAE. METHODS: A retrospective review of charts was completed for patients younger than 18 years old who underwent tympanostomy tube placement from January 1, 2018 to September 1, 2023 and had preoperative and postoperative OAE testing within 6 months of surgery. The primary variable was presence of OAE preoperatively and postoperatively. Chi-square analysis and t test were used for statistical analysis. RESULTS: A total of 212 ears were examined from 111 pediatric patients who underwent tympanostomy tube placement during the study period. Presence of OAE at 3000, 4000, and 5000 Hz were all noted to significantly increase following tympanostomy tube placement, with OAE presence increasing from approximately 27.8% of the sample preoperatively to 95.3% postoperatively at 3000 and 4000 Hz. Patients who noted improvement had a significantly higher proportion of type B tympanogram preoperatively, compared to a higher proportion of type A tympanogram noted in patients who did not note improvement. CONCLUSION: Tympanostomy tubes can significantly improve otoacoustic emissions in patients with middle ear dysfunction.
RESUMEN
OBJECTIVE: Diagnosis of otosclerosis on temporal bone CT images is often difficult because the imaging findings are frequently subtle. Our aim was to assess the utility of deep learning analysis in diagnosing otosclerosis on temporal bone CT images. METHODS: A total of 198 temporal bone CT images were divided into the training set (n = 140) and the test set (n = 58). The final diagnosis (otosclerosis-positive or otosclerosis-negative) was determined by an experienced senior radiologist who carefully reviewed all 198 temporal bone CT images while correlating with clinical and intraoperative findings. In deep learning analysis, a rectangular target region that includes the area of the fissula ante fenestram was extracted and fed into the deep learning training sessions to create a diagnostic model. Transfer learning was used with the deep learning model architectures of AlexNet, VGGNet, GoogLeNet, and ResNet. The test data set was subsequently analyzed using these models and by another radiologist with 3 years of experience in neuroradiology following completion of a neuroradiology fellowship. The performance of the radiologist and the deep learning models was determined using the senior radiologist's diagnosis as the gold standard. RESULTS: The diagnostic accuracies were 0.89, 0.72, 0.81, 0.86, and 0.86 for the subspecialty trained radiologist, AlexNet, VGGNet, GoogLeNet, and ResNet, respectively. The performances of VGGNet, GoogLeNet, and ResNet were not significantly different compared to the radiologist. In addition, GoogLeNet and ResNet demonstrated non-inferiority compared to the radiologist. CONCLUSIONS: Deep learning technique may be a useful supportive tool in diagnosing otosclerosis on temporal bone CT. KEY POINTS: ⢠Deep learning can be a helpful tool for the diagnosis of otosclerosis on temporal bone CT. ⢠Deep learning analyses with GoogLeNet and ResNet demonstrate non-inferiority when compared to the subspecialty trained radiologist. ⢠Deep learning may be particularly useful in medical institutions without experienced radiologists.
