Background: Distinguishing benign inverted papilloma (IP) tumors from those that have undergone malignant transformation to squamous cell carcinoma (IP-SCC) is important but challenging to do preoperatively. Magnetic resonance imaging (MRI) can help differentiate these 2 entities, but no established method exists that can automatically synthesize all potentially relevant MRI image features to distinguish IP and IP-SCC. We explored a deep learning approach, using 3-dimensional convolutional neural networks (CNNs), to address this challenge.Methods: Retrospective chart reviews were performed at 2 institutions to create a data set of preoperative MRIs with corresponding surgical pathology reports.The MRI data set included all available MRI sequences in the axial plane, which were used to train, validate, and test 3 CNN models. Saliency maps were generated to visualize areas of MRIs with greatest influence on predictions. Results: A total of 90 patients with IP (n = 64) or IP-SCC (n = 26) tumors were identified, with a total of 446 images of distinct MRI sequences for IP (n = 329) or IP-SCC (n = 117). The best CNN model, All-Net, demonstrated a sensitivity of 66.7%, specificity of 81.5%, overall accuracy of 77.9%, and receiver-operating characteristic area under the curve of 0.80 (95% confidence interval, 0.682-0.898) for test classification performance. The other 2 models, Small-All-Net and Elastic-All-Net, showed similar performance levels. Conclusion:A deep learning approach with 3-dimensional CNNs can distinguish IP and IP-SCC with moderate test classification performance. Although CNNs demonstrate promise to enhance the prediction of IP-SCC using MRIs, more data are needed before they can reach the predictive value already established by human MRI evaluation.

Objective To systematically review literature evidence on temporal bone–resurfacing techniques for pulsatile tinnitus (PT) associated with vascular wall anomalies. Data Sources We searched PubMed, Embase, and the Cochrane Database. The period covered was from 1962 to 2018. Review Methods We included studies in all languages that reported resurfacing outcomes for patients with PT and radiographic evidence or direct visualization of sigmoid sinus wall anomaly, jugular bulb wall anomaly, or dehiscent or aberrant internal carotid artery. Results Of 954 citations retrieved in database searches and 5 citations retrieved from reference lists, 20 studies with a total of 141 resurfacing cases involving 138 patients were included. Resurfacing outcomes for arterial sources of PT showed 3 of 5 cases (60%) with complete resolution and 2 (40%) with partial resolution. Jugular bulb sources of PT showed 11 of 14 cases (79%) with complete resolution and 1 (7%) with partial resolution. Sigmoid sinus sources of PT showed 91 of 121 cases (75%) with complete resolution and 12 (10%) with partial resolution. Symptoms occurred more in females and on the right side. Most cases (94%) used hard-density materials for resurfacing. Material density did not appear to be associated with resurfacing outcomes. Use of autologous materials was associated with improved outcomes for arterial sources resurfacing. Major complications involving sigmoid sinus thrombosis or compression were reported in 4% of cases without long-term morbidity or mortality. Conclusions Resurfacing surgery is likely effective and well tolerated for select patients with PT associated with various vascular wall anomalies.

Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

Diseases that cause hearing loss and/or vertigo in humans such as Meniere’s disease are often studied using animal models. The volume of endolymph within the inner ear varies with these diseases. Here, we used a mouse model of increased endolymph volume, endolymphatic hydrops, to develop a computer-aided objective approach to measure endolymph volume from images collected

Objective Safe surgery requires the accurate discrimination of tissue intraoperatively. We assess the feasibility of using multispectral imaging and deep learning to enhance surgical vision by automated identification of normal human head and neck tissues. Study Design Construction and feasibility testing of novel multispectral imaging system for surgery. Setting Academic university hospital. Subjects and Methods Multispectral images of fresh-preserved human cadaveric tissues were captured with our adapted digital operating microscope. Eleven tissue types were sampled, each sequentially exposed to 6 lighting conditions. Two convolutional neural network machine learning models were developed to classify tissues based on multispectral and white-light color images (ARRInet-M and ARRInet-W, respectively). Blinded otolaryngology residents were asked to identify tissue specimens from white-light color images, and their performance was compared with that of the ARRInet models. Results A novel multispectral imaging system was developed with minimal adaptation to an existing digital operating microscope. With 81.8% accuracy in tissue identification of full-size images, the multispectral ARRInet-M classifier outperformed the white-light-only ARRInet-W model (45.5%) and surgical residents (69.7%). Challenges with discrimination occurred with parotid vs fat and blood vessels vs nerve. Conclusions A deep learning model using multispectral imaging outperformed a similar model and surgical residents using traditional white-light imaging at the task of classifying normal human head and neck tissue ex vivo. These results suggest that multispectral imaging can enhance surgical vision and augment surgeons’ ability to identify tissues during a procedure.