Using Nasal Self-Esteem to Predict Revision in Cosmetic Rhinoplasty.

BACKGROUND: It would be useful if existing tools or outcomes measures could predict which patients are at greater risk of revision surgery following rhinoplasty. OBJECTIVES: The authors sought to determine if a single question assessing nasal self-esteem could be utilized to predict which patients are at greatest risk of revision surgery following rhinoplasty. METHODS: The authors conducted a retrospective chart review of 148 patients who underwent cosmetic rhinoplasty. Results of pre- and postoperative Standardized Cosmesis and Health Nasal Outcomes Survey questionnaires and rates of revision or patient-initiated revision discussions (RD) were collected. Patients were stratified based on answers to Standardized Cosmesis and Health Nasal Outcomes Survey question 5 (SQ5), "Decreased mood and self-esteem due to my nose." RESULTS: Of the 148 patients included in the analysis, 72.9% were women, and the mean age was 30.9 (15-59, standard deviation = 10.3) years. Those patients who selected 4 or 5 on SQ5 had an overall revision rate of 16.7% and 18.8%, respectively, and a RD rate of 27.8% and 31.25%, respectively. Those patients who selected 0 through 3 on SQ5 had an overall revision rate of 0% and an overall RD rate of 10.4%. Only SQ5 was predictive of revision and RD on logistic regression analysis (P = 0.0484 and P = 0.0257) after Bonferroni correction. CONCLUSIONS: SQ5 appears to offer a useful adjunct to guide surgical management of the cosmetic rhinoplasty patient. Those patients who reported worse nasal self-esteem and associated mood preoperatively were more likely to request and undergo revision.

Glomangiopericytoma Presenting as a Middle Ear Mass.

We describe an unusual case of glomangiopericytoma presenting as a mass filling the middle ear, enveloping the ossicles, and extending into the mastoid antrum without bony destruction. Management involved three surgeries and stereotactic radiosurgery, which achieved short-term local control with no evidence of disease on MRI imaging 12 months after radiation. Facial nerve function and hearing were preserved. This is the first report to our knowledge of a glomangiopericytoma presenting as a primary temporal bone lesion. Treatment with surgery and stereotactic radiosurgery for residual or recurrent disease is a reasonable approach to achieve local control and functional preservation. Laryngoscope, 134:1426-1430, 2024.

A Scoping Review of Artificial Intelligence Detection of Voice Pathology: Challenges and Opportunities.

OBJECTIVE: Survey the current literature on artificial intelligence (AI) applications for detecting and classifying vocal pathology using voice recordings, and identify challenges and opportunities for advancing the field forward. DATA SOURCES: PubMed, EMBASE, CINAHL, and Scopus databases. REVIEW METHODS: A comprehensive literature search was performed following the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews guidelines. Peer-reviewed journal articles in the English language were included if they used an AI approach to detect or classify pathological voices using voice recordings from patients diagnosed with vocal pathologies. RESULTS: Eighty-two studies were included in the review between the years 2000 and 2023, with an increase in publication rate from one study per year in 2012 to 10 per year in 2022. Seventy-two studies (88%) were aimed at detecting the presence of voice pathology, 24 (29%) at classifying the type of voice pathology present, and 4 (5%) at assessing pathological voice using the Grade, Roughness, Breathiness, Asthenia, and Strain scale. Thirty-six databases were used to collect and analyze speech samples. Fourteen articles (17%) did not provide information about their AI model validation methodology. Zero studies moved beyond the preclinical and offline AI model development stages. Zero studies specified following a reporting guideline for AI research. CONCLUSION: There is rising interest in the potential of AI technology to aid the detection and classification of voice pathology. Three challenges-and areas of opportunities-for advancing this research are heterogeneity of databases, lack of clinical validation studies, and inconsistent reporting.

Estimation of Cochlear Implant Insertion Depth Using 2D-3D Registration of Postoperative X-Ray and Preoperative CT Images.

OBJECTIVE: To improve estimation of cochlear implant (CI) insertion depth in postoperative skull x-rays using synthesized information from preoperative CT scans. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary referral center. PATIENTS: Ten adult cochlear implant recipients with preoperative and postoperative temporal bone computed tomography (CT)scans and postoperative skull x-ray imaging. INTERVENTIONS: Postoperative x-rays and digitally reconstructed radiographs (DRR) from preoperative CTs were registered using 3D Slicer and MATLAB to enhance localization of the round window and modiolus. Angular insertion depth (AID) was estimated in unmodified and registration-enhanced x-rays and DRRs in the cochlear view. Linear insertion depth (LID) was estimated in registered images by two methods that localized the proximal CI electrode or segmented the cochlea. Ground truth assessments were made in postoperative CTs. MAIN OUTCOME MEASURES: Errors of insertion depth estimates were calculated relative to ground truth measurements and compared with paired t t ests. Pearson correlation coefficient was used to assess inter-rater reliability of two reviewer's measurements of AID in unmodified x-rays. RESULTS: In postoperative x-rays, AID estimation errors were similar with and without registration enhancement (-1.3 ± 20.7° and -4.8 ± 24.9°, respectively; mean ± SD; p = 0.6). AID estimation in unmodified x-rays demonstrated strong interrater agreement (ρ = 0.79, p < 0.05) and interrater differences (-15.0 ± 35.3°) comparable to estimate errors. Registering images allowed measurement of AID in the cochlear view with estimation errors of 14.6 ± 30.6° and measurement of LID, with estimate errors that were similar between proximal electrode localization and cochlear segmentation methods (-0.9 ± 2.2 mm and -2.1 ± 2.7 mm, respectively; p = 0.3). CONCLUSIONS: 2D-3D image registration allows measurement of AID in the cochlear view and LID using postoperative x-rays and preoperative CT imaging. The use of this technique may reduce the need for postimplantation CT studies to assess these metrics of CI electrode position. Further work is needed to improve the accuracy of AID assessment in the postoperative x-ray view with registered images compared with established methods.

Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging.

Significance: Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contributions of spectral channels to tissue discrimination is important for improving MSI systems. Aim: Develop a metric to quantify the contributions of individual spectral channels to tissue classification in MSI. Approach: MSI was integrated into a digital operating microscope with three sensors and seven illuminants. Two convolutional neural network (CNN) models were trained to classify 11 head and neck tissue types using white light (RGB) or MSI images. The signal to noise ratio (SNR) of spectral channels was compared with the impact of channels on tissue classification performance as determined using CNN visualization methods. Results: Overall tissue classification accuracy was higher with use of MSI images compared with RGB images, both for classification of all 11 tissue types and binary classification of nerve and parotid ( p < 0.001 ). Removing spectral channels with SNR > 20 reduced tissue classification accuracy. Conclusions: The spectral channel SNR is a useful metric for both understanding CNN tissue classification and quantifying the contributions of different spectral channels in an MSI system.

Automated Radiomic Analysis of Vestibular Schwannomas and Inner Ears Using Contrast-Enhanced T1-Weighted and T2-Weighted Magnetic Resonance Imaging Sequences and Artificial Intelligence.

OBJECTIVE: To objectively evaluate vestibular schwannomas (VSs) and their spatial relationships with the ipsilateral inner ear (IE) in magnetic resonance imaging (MRI) using deep learning. STUDY DESIGN: Cross-sectional study. PATIENTS: A total of 490 adults with VS, high-resolution MRI scans, and no previous neurotologic surgery. INTERVENTIONS: MRI studies of VS patients were split into training (390 patients) and test (100 patients) sets. A three-dimensional convolutional neural network model was trained to segment VS and IE structures using contrast-enhanced T1-weighted and T2-weighted sequences, respectively. Manual segmentations were used as ground truths. Model performance was evaluated on the test set and on an external set of 100 VS patients from a public data set (Vestibular-Schwannoma-SEG). MAIN OUTCOME MEASURES: Dice score, relative volume error, average symmetric surface distance, 95th-percentile Hausdorff distance, and centroid locations. RESULTS: Dice scores for VS and IE volume segmentations were 0.91 and 0.90, respectively. On the public data set, the model segmented VS tumors with a Dice score of 0.89 ± 0.06 (mean ± standard deviation), relative volume error of 9.8 ± 9.6%, average symmetric surface distance of 0.31 ± 0.22 mm, and 95th-percentile Hausdorff distance of 1.26 ± 0.76 mm. Predicted VS segmentations overlapped with ground truth segmentations in all test subjects. Mean errors of predicted VS volume, VS centroid location, and IE centroid location were 0.05 cm 3 , 0.52 mm, and 0.85 mm, respectively. CONCLUSIONS: A deep learning system can segment VS and IE structures in high-resolution MRI scans with excellent accuracy. This technology offers promise to improve the clinical workflow for assessing VS radiomics and enhance the management of VS patients.

End-to-end deep learning classification of vocal pathology using stacked vowels.

Objectives: Advances in artificial intelligence (AI) technology have increased the feasibility of classifying voice disorders using voice recordings as a screening tool. This work develops upon previous models that take in single vowel recordings by analyzing multiple vowel recordings simultaneously to enhance prediction of vocal pathology. Methods: Voice samples from the Saarbruecken Voice Database, including three sustained vowels (/a/, /i/, /u/) from 687 healthy human participants and 334 dysphonic patients, were used to train 1-dimensional convolutional neural network models for multiclass classification of healthy, hyperfunctional dysphonia, and laryngitis voice recordings. Three models were trained: (1) a baseline model that analyzed individual vowels in isolation, (2) a stacked vowel model that analyzed three vowels (/a/, /i/, /u/) in the neutral pitch simultaneously, and (3) a stacked pitch model that analyzed the /a/ vowel in three pitches (low, neutral, and high) simultaneously. Results: For multiclass classification of healthy, hyperfunctional dysphonia, and laryngitis voice recordings, the stacked vowel model demonstrated higher performance compared with the baseline and stacked pitch models (F1 score 0.81 vs. 0.77 and 0.78, respectively). Specifically, the stacked vowel model achieved higher performance for class-specific classification of hyperfunctional dysphonia voice samples compared with the baseline and stacked pitch models (F1 score 0.56 vs. 0.49 and 0.50, respectively). Conclusions: This study demonstrates the feasibility and potential of analyzing multiple sustained vowel recordings simultaneously to improve AI-driven screening and classification of vocal pathology. The stacked vowel model architecture in particular offers promise to enhance such an approach. Lay Summary: AI analysis of multiple vowel recordings can improve classification of voice pathologies compared with models using a single sustained vowel and offer a strategy to enhance AI-driven screening of voice disorders. Level of Evidence: 3.

Benign Ectopic Thyroid in the Lateral (Level II) Neck Compartment.

Ectopic thyroid most commonly presents in the midline and is typically associated with the absence of an orthotopic thyroid. Less commonly, ectopic thyroid can present in the lateral neck, typically with a coexisting orthotopic thyroid and abnormal pathology in either the ectopic or orthotopic thyroid tissue. This paper describes a rare case of a benign, ectopic thyroid in the lateral neck (level II) associated with a normal, benign orthotopic thyroid. This report illustrates clinical pearls for the management of this unusual entity.

Deep learning classification of inverted papilloma malignant transformation using 3D convolutional neural networks and magnetic resonance imaging.

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.

Multispectral Imaging for Automated Tissue Identification of Normal Human Surgical Specimens.

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.

Prospective Evaluation of the Safety and Efficacy of THRIVE for Children Undergoing Airway Evaluation.

Transnasal Humidified Rapid-Insufflation Ventilatory Exchange (THRIVE) is a humidified high-flow nasal cannula capable of extending apneic time. Although THRIVE is assumed to stent upper airway soft tissues, this has not been objectively evaluated. Also, there are no prior studies providing safety and efficacy data for those patients undergoing upper airway evaluation using THRIVE. METHODS: This report is a prospective study of the safety and efficacy of THRIVE in pediatric patients younger than 18 years old undergoing drug-induced sleep endoscopy. We positioned a flexible laryngoscope to view the larynx, and photographs were taken with no THRIVE flow (control) and with THRIVE flow at 10 and 20 liters per minute (LPM). Upper airway patency was measured using epiglottis to posterior pharynx distance, laryngeal inlet area, and modified Cormack-Lehane score at the trialed parameters. Vomiting and aspiration were our primary safety endpoints. RESULTS: Eleven patients (6 women) with a mean age of 5.3 ± 2.1 years (2-8 years; SD, 2.05) were enrolled. Measurements of upper airway patency showed a significant THRIVE flow-associated increase in epiglottis to posterior pharynx distance (105 ± 54 at 10 L/min and 199 ± 67 at 20 L/min; P = 0.007) and nonsignificant increase of laryngeal inlet area (206 ± 148 at 10 L/min and 361 ± 190 at 20 L/min; P = 0.07). Cormack-Lehane score improved significantly at higher THRIVE volumes (P = 0.006). CONCLUSIONS: THRIVE appears to safely improve upper airway patency during sleep endoscopy in the pediatric patient. In this study, we objectively document the flow-dependent increase in laryngeal patency associated with THRIVE.

Hypotympanic Sound Baffle for Amelioration of Pulsatile Tinnitus due to Carotid and Jugular Bulb Dehiscence.

OBJECTIVE: To share our experience with treating pulsatile tinnitus by insulating a dehiscent carotid artery with a hypotympanic sound baffle, and compare outcomes with a similar resurfacing approach for jugular bulb wall anomalies. STUDY DESIGN: Retrospective case series. SETTING: Tertiary academic medical center. PATIENTS: Adult patients with troublesome pulsatile tinnitus with radiologic evidence of carotid artery dehiscence or jugular bulb wall anomaly within the temporal bone. INTERVENTION: Hypotympanic exposure of vessel followed by resurfacing using hydroxyapatite cement (carotid dehiscence) or autologous tissue (jugular bulb wall anomalies). MAIN OUTCOMES MEASURES: Alleviation or reduction of pulsatile tinnitus. RESULTS: Two patients presented with unilateral, debilitating pulsatile tinnitus and history and imaging consistent with carotid dehiscence and underwent hypotympanic resurfacing with hydroxyapatite cement. Both had considerable initial improvement of tinnitus, and 40% resolution of tinnitus with improved quality of life at an average follow-up of 13.5 months. Two patients with jugular bulb dehiscence/diverticulum treated by resurfacing had complete elimination of symptoms at an average follow up of 17.3 months. There were no major adverse outcomes (permanent hearing loss, vascular injury, or intracranial hypertension). CONCLUSION: Creation of a hypotympanic sound baffle offers promise as a means of reducing pulsatile tinnitus emanating from a dehiscent carotid artery transmitted to the tympanum, with substantial improvement in reported functional ability. Treatment of venous etiologies of pulsatile tinnitus with similar techniques demonstrates higher success rates, which may be attributable to incomplete resurfacing of carotid artery dehiscence along its extent towards the petrous apex due to safety concerns.

Systematic Review of Temporal Bone-Resurfacing Techniques for Pulsatile Tinnitus Associated with Vascular Wall Anomalies.

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.

Thyroid cancer risk in airline cockpit and cabin crew: a meta-analysis.

BACKGROUND: Airline crew are exposed to ionizing radiation as part of their occupation and have a documented increased risk of melanoma and cataracts. However, whether their occupation predisposes them to an increased risk of thyroid cancer is not established. The purpose of this systematic review and meta-analysis was to assess the risk of thyroid cancer in airline cockpit and cabin crew compared with the general population. METHODS: The MEDLINE database accessed via PubMed and Cochrane Database were searched. We included cohort studies reporting the standardized incidence ratio (SIR) or standardized mortality ratio (SMR) of thyroid cancers in any flight-based occupation. RESULTS: Of the 1777 citations retrieved in PubMed, eight studies with a total of 243,088 aircrew members and over 3,334,114 person-years of follow-up were included in this meta-analysis. No relevant studies were identified on Cochrane Database. The overall summary SIR of participants in any flight-based occupation was 1.11 (95% CI, 0.79-1.57; p = 0.613; 6 records). The summary SIR for cockpit crew was 1.21 (95% CI, 0.75-1.95; p = 0.383; 4 records) and the summary SIR for cabin crew was 1.00 (95% CI, 0.60-1.66; p = 0.646; 2 records). The overall summary standardized mortality ratio for airline crew was 1.19 (95% CI, 0.59-2.39; p = 0.773; 2 records). CONCLUSION: Airline crew were not found to have a significantly elevated risk of thyroid cancer incidence or mortality relative to the general population. Future research should capitalize on the growing occupational cohort dataset and employ innovative methods to quantify lifetime radiation exposure to further assess thyroid cancer risk in airline crew.

The effect of antibiotics on protein diffusion in the Escherichia coli cytoplasmic membrane.

Accumulating evidence suggests that molecular motors contribute to the apparent diffusion of molecules in cells. However, current literature lacks evidence for an active process that drives diffusive-like motion in the bacterial membrane. One possible mechanism is cell wall synthesis, which involves the movement of protein complexes in the cell membrane circumferentially around the cell envelope and may generate currents in the lipid bilayer that advectively transport other transmembrane proteins. We test this hypothesis in Escherichia coli using drug treatments that slow cell wall synthesis and measure their effect on the diffusion of the transmembrane protein mannitol permease using fluorescence recovery after photobleaching. We found no clear decrease in diffusion in response to vancomycin and no decrease in response to mecillinam treatment. These results suggest that cell wall synthesis is not an active contributor to mobility in the cytoplasmic membrane.

Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography.

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

ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

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.