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Objectives/Hypothesis This scoping review aims to provide a broad overview of the applications of artificial intelligence (AI) to office laryngoscopy to identify gaps in knowledge and guide future research. Study Design Scoping Review. Methods Searches for studies on AI and office laryngoscopy were conducted in five databases. Title and abstract and then full‐text screening were performed. Primary research studies published in English of any date were included. Studies were summarized by: AI applications, targeted conditions, imaging modalities, author affiliations, and dataset characteristics. Results Studies focused on vocal fold vibration analysis (43%), lesion recognition (24%), and vocal fold movement determination (19%). The most frequently automated tasks were recognition of vocal fold nodules (19%), polyp (14%), paralysis (11%), paresis (8%), and cyst (7%). Imaging modalities included high‐speed laryngeal videos (45%), stroboscopy (29%), and narrow band imaging endoscopy (7%). The body of literature was primarily authored by science, technology, engineering, and math (STEM) specialists (76%) with only 30 studies (31%) involving co‐authorship by STEM specialists and otolaryngologists. Datasets were mostly from single institution (84%) and most commonly originated from Germany (23%), USA (16%), Spain (9%), Italy (8%), and China (8%). Demographic information was only reported in 39 studies (40%), with age and sex being the most commonly reported, whereas race/ethnicity and gender were not reported in any studies. Conclusion More interdisciplinary collaboration between STEM and otolaryngology research teams improved demographic reporting especially of race and ethnicity to ensure broad representation, and larger and more geographically diverse datasets will be crucial to future research on AI in office laryngoscopy. Level of Evidence NA Laryngoscope, 132:1993–2016, 2022

Objective The coronavirus disease 2019 (COVID-19) pandemic has reduced the demand for, and supply of, head and neck cancer services. This study compares the times to diagnosis, staging, and treatment of head and neck cancers before and during the COVID-19 pandemic. Study Design Retrospective cohort study. Setting Tertiary academic medical center in New York City (NYC). Methods The times to diagnosis, staging, and treatment of head and neck cancer for patients presenting to the clinics of 4 head and neck oncology surgeons with newly diagnosed head and neck cancers were compared between pre–COVID-19 and COVID-19 periods. Results Sixty-eight patients in the pre–COVID-19 period and 26 patients in the COVID-19 period presented with newly diagnosed head and neck cancer. Patients in the COVID-19 group had a significantly longer time to diagnosis than the pre–COVID-19 group after adjustment for age and cancer diagnosis ( P = .02; hazard ratio [HR], 0.54; 95% CI, 0.32-0.92). Patients in the pre–COVID-19 and COVID-19 groups had no statistically significant differences in time to staging ( P > .9; HR, 1.01; 95% CI, 0.58-1.74) or time to treatment ( P = .12; HR, 1.55; 95% CI, 0.89-2.72). Conclusion This study found that time to diagnosis for head and neck cancers was delayed during a COVID-19 period compared to a pre–COVID-19 period. However, there was no evidence of delays in time to staging and time to treatment during the COVID-19 period. Our results prompt further investigations into the factors contributing to diagnostic delays but provide reassurance that despite COVID-19, patients were receiving timely staging and treatment for head and neck cancers.

Objective: This study aims to develop and validate a convolutional neural network (CNN)-based algorithm for automatic selection of informative frames in flexible laryngoscopic videos. The classifier has the potential to aid in the development of computer-aided diagnosis systems and reduce data processing time for cliniciancomputer scientist teams. Methods: A dataset of 22,132 laryngoscopic frames was extracted from 137 flexible laryngostroboscopic videos from 115 patients. 55 videos were from healthy patients with no laryngeal pathology and 82 videos were from patients with vocal fold polyps. The extracted frames were manually labeled as informative or uninformative by two independent reviewers based on vocal fold visibility, lighting, focus, and camera distance, resulting in 18,114 informative frames and 4018 uninformative frames. The dataset was split into training and test sets. A pre-trained ResNet-18 model was trained using transfer learning to classify frames as informative or uninformative.Hyperparameters were set using cross-validation. The primary outcome was precision for the informative class and secondary outcomes were precision, recall, and F1-score for all classes. The processing rate for frames between the model and a human annotator were compared. Results:The automated classifier achieved an informative frame precision, recall, and F1-score of 94.4%, 90.2%, and 92.3%, respectively, when evaluated on a hold-out test set of 4438 frames. The model processed frames 16 times faster than a human annotator. Conclusion:The CNN-based classifier demonstrates high precision for classifying informative frames in flexible laryngostroboscopic videos. This model has the

Background This scoping review aims to provide a broad overview of the research on the unassisted virtual physical exam performed over synchronous audio-video telemedicine to identify gaps in knowledge and guide future research. Methods Searches for studies on the unassisted virtual physical exam were conducted in 3 databases. We included primary research studies in English on the virtual physical exam conducted via patient-to-provider synchronous, audio-video telemedicine in the absence of assistive technology or personnel. Screening and data extraction were performed by 2 independent reviewers. Results Seventy-four studies met inclusion criteria. The most common components of the physical exam performed over telemedicine were neurologic (38/74, 51%), musculoskeletal (10/74, 14%), multi-system (6/74, 8%), neuropsychologic (5/74, 7%), and skin (5/74, 7%). The majority of the literature focuses on the telemedicine physical exam in the adult population, with only 5% of studies conducted specifically in a pediatric population. During the telemedicine exam, the patients were most commonly located in outpatient offices (28/74, 38%) and homes and other non-clinical settings (25/74, 34%). Both patients and providers in the included studies most frequently used computers for the telemedicine encounter. Conclusions Research evaluating the unassisted virtual physical exam is at an early stage of maturity and is skewed toward the neurologic, musculoskeletal, neuropsychologic, and skin exam components. Future research should focus on expanding the range of telemedicine exam maneuvers studied and evaluating the exam in the most relevant settings, which for telemedicine is trending toward exams conducted through mobile devices and in patients’ homes.