Considering the Costs: Resin 3D Printing for a Temporal Bone Dissection Course.

Significant costs associated with obtaining cadaveric temporal bones (TBs) have led many to seek more cost-effective alternatives for TB surgical simulation. Multiple studies support the face validity of resin 3-dimensional (3D)-printed TBs as high-fidelity, useful alternatives for simulating TB dissection. However, a paucity of literature describes the cost or time associated with in-house manufacturing of resin TBs at scale.

Automated Real-Time Otologic Drill Motion Analysis.

In this work, we present a computer vision model for automatic otologic drill motion analysis during mastoidectomy and detail how to implement a computer vision model for real-time use. Automated real-time surgical analysis has the potential to enable efficient methods for technical skill assessment and broadly transform the landscape of surgical education. Laryngoscope, 135:836-839, 2025.

The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile.

Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.

An External Validation Study for Automated Segmentation of Vestibular Schwannoma.

Objective: To validate how an automated model for vestibular schwannoma (VS) segmentation developed on an external homogeneous dataset performs when applied to internal heterogeneous data.

Patients: The external dataset comprised 242 patients with previously untreated, sporadic unilateral VS undergoing Gamma Knife radiosurgery, with homogeneous magnetic resonance imaging (MRI) scans. The internal dataset comprised 10 patients from our institution, with heterogeneous MRI scans.

A Proof-of-Concept Computer Vision Approach for Measurement of Tympanic Membrane Perforations.

Objective: Size, an important characteristic of a tympanic membrane perforation (TMP), is commonly assessed with gross estimation via visual inspection, a practice which is prone to inaccuracy. Herein, we demonstrate feasibility of a proof-of-concept computer vision model for estimating TMP size in a small set of perforations.

Methods: An open-source deep learning architecture was used to train a model to segment and calculate the area of a perforation and the visualized tympanic membrane (TM) in a set of endoscopic images of mostly anterior and relatively small TMPs.

Proof-of-Concept Computer Vision Model for Instrument and Anatomy Detection During Transcanal Endoscopic Ear Surgery.

High-definition video captured during transcanal endoscopic ear surgery (TEES) can serve as imaging data for computer vision algorithms. This report describes a proof-of-concept model for automated anatomy and instrument detection during TEES.

Artificial Intelligence Governance and Otolaryngology-Head and Neck Surgery.

This rapid communication highlights components of artificial intelligence governance in healthcare and suggests adopting key governance approaches in otolaryngology – head and neck surgery.

Towards Medical Billing Automation: NLP for Outpatient Clinician Note Classification.

Objectives: Our primary objective was to develop a natural language processing approach that accurately predicts outpatient Evaluation and Management (E/M) level of service (LoS) codes using clinicians' notes from a health system electronic health record. A secondary objective was to investigate the impact of clinic note de-identification on document classification performance.

Methods: We used retrospective outpatient office clinic notes from four medical and surgical specialties.

Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery.

Objective: In an era of vestibular schwannoma (VS) surgery where functional preservation is increasingly emphasized, persistent postoperative dizziness is a relatively understudied functional outcome. The primary objective was to develop a predictive model to identify patients at risk for developing persistent postoperative dizziness after VS resection.

Methods: Retrospective review of patients who underwent VS surgery at our institution with a minimum of 12 months of postoperative follow-up.

Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction.

Background: Machine learning (ML) analysis of biometric data in non-controlled environments is underexplored.

Objective: To evaluate whether ML analysis of physical activity data can be employed to classify whether individuals have postural dysfunction in middle-aged and older individuals.

Methods: A 1 week period of physical activity was measured by a waist-worn uni-axial accelerometer during the 2003-2004 National Health and Nutrition Examination Survey sampling period.

Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data.

Synthetic clinical images could augment real medical image datasets, a novel approach in otolaryngology-head and neck surgery (OHNS). Our objective was to develop a generative adversarial network (GAN) for tympanic membrane images and to validate the quality of synthetic images with human reviewers. Our model was developed using a state-of-the-art GAN architecture, StyleGAN2-ADA.

A systematic review of federated learning applications for biomedical data.

Objectives: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology.

Paediatric sleep apnea event prediction using nasal air pressure and machine learning.

Sleep-disordered breathing is an important health issue for children. The objective of this study was to develop a machine learning classifier model for the identification of sleep apnea events taken exclusively from nasal air pressure measurements acquired during overnight polysomnography for paediatric patients. A secondary objective of this study was to differentiate site of obstruction exclusively from hypopnea event data using the model.

Development of a Predictive Model for Individualized Hearing Aid Benefit.

Objectives: To develop a model to predict individualized hearing aid benefit. To provide interpretations of model predictions on global and individual levels.

Methods: We compiled a data set of patients with hearing loss who trialed hearing aids and completed the Client Oriented Scale of Improvement (COSI) questionnaire, a validated patient-reported outcome measure of hearing aid benefit.

New Medical Device and Therapeutic Approvals in Otolaryngology: State of the Art Review of 2021.

Objective: To evaluate new medical devices and drugs pertinent to otolaryngology-head and neck surgery that were approved by the Food and Drug Administration (FDA) in 2021.

Data Sources: Publicly available FDA device and drug approvals from ENT (ear, nose, and throat), anesthesia, neurosurgery, plastic surgery, and general surgery FDA committees.

Review Methods: FDA device and therapeutic approvals were identified and reviewed by members of the American Academy of Otolaryngology-Head and Neck Surgery's Medical Devices and Drugs Committee.

"Human vs Machine" Validation of a Deep Learning Algorithm for Pediatric Middle Ear Infection Diagnosis.

Objective: We compared the diagnostic performance of human clinicians with that of a neural network algorithm developed using a library of tympanic membrane images derived from children taken to the operating room with the intent of performing myringotomy and possible tube placement for recurrent acute otitis media (AOM) or otitis media with effusion (OME).

Study Design: Retrospective cohort study.

Setting: Tertiary academic medical center from 2018 to 2021.

Predicting Progression of Oral Lesions to Malignancy Using Machine Learning.

Objective: To use large-scale electronic health record (EHR) data to develop machine learning models predicting malignant transformation of oral lesions.

Methods: A multi-institutional health system database was used to identify a retrospective cohort of patients with biopsied oral lesions. The primary outcome was malignant transformation.

The predictive power of geographic health care utilization for unintentional fatal fall rates.

Background: Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. The increase in fall mortality rates is likely multifactorial. With a lack of key drivers identified to explain rising rates of death from falls, accurate predictive modelling can be challenging, hindering evidence-based health resource and policy efforts.

Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology.

Objective: Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non-ML techniques for otolaryngology-head and neck surgery cases.

Care Gaps and Recommendations in Vestibular Migraine: An Expert Panel Summit.

Vestibular migraine (VM) is an increasingly recognized pathology yet remains as an underdiagnosed cause of vestibular disorders. While current diagnostic criteria are codified in the 2012 Barany Society document and included in the third edition of the international classification of headache disorders, the pathophysiology of this disorder is still elusive. The Association for Migraine Disorders hosted a multidisciplinary, international expert workshop in October 2020 and identified seven current care gaps that the scientific community needs to resolve, including a better understanding of the range of symptoms and phenotypes of VM, the lack of a diagnostic marker, a better understanding of pathophysiologic mechanisms, as well as the lack of clear recommendations for interventions (nonpharmacologic and pharmacologic) and finally, the need for specific outcome measures that will guide clinicians as well as research into the efficacy of interventions.