Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.

Objectives: Major advancements have been made in applying artificial intelligence and computer vision to analyze videolaryngoscopy data. These models are limited to post hoc analysis and are aimed at research settings. In this work, we assess the feasibility of a real-time solution for automated vocal fold tracking during in-office laryngoscopy.

Predicting ADC map quality from T2-weighted MRI: A deep learning approach for early quality assessment to assist point-of-care.

Purpose: Poor quality prostate MRI images compromise diagnostic accuracy, with diffusion-weighted imaging and the resulting apparent diffusion coefficient (ADC) maps being particularly vulnerable. These maps are critical for prostate cancer diagnosis, yet current methods relying on standardizing technical parameters fail to consistently ensure image quality. We propose a novel deep learning approach to predict low-quality ADC maps using T2-weighted (T2W) images, enabling real-time corrective interventions during imaging.

Predicting ADC Map Quality from T2-Weighted MRI: A Deep Learning Approach for Early Quality Assessment to Assist Point-of-Care.

Purpose: Poor quality prostate MRI images, especially ADC maps, can lead to missed lesions and unnecessary repeat scans. To address this issue, we aimed to develop an automated method to predict ADC map quality from T2 images acquired earlier in the scanning process.

Materials And Methods: A paired multi-site image corpus of T2-weighted images and ADC maps was constructed from 486 patients imaged in-house and at 62 external clinics.

Automated Technical Skill and Performance Assessment in Otology and Neurotology: A Scoping Review.

Objectives/hypothesis: This scoping review aims to provide an overview of existing semi-automated and fully automated methods for technical skill and performance assessment in otologic and neurotologic procedures.

Study Design: Scoping review.

Databases Reviewed: Ovid MEDLINE (PubMed), Ovid EMBASE, Web of Science Core Collection, and IEEE Xplor Digital Library.

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.

Production of a selective antibacterial fatty acid against by strains.

C16 monounsaturated fatty acid (C16:1) show antibacterial activity against , a pathogen associated with various diseases such as atopic dermatitis and bacteremia, while the compound does not exhibit antibacterial activity against , an epidermal commensal that inhibits the growth of . In this study, we aimed to find bifidobacterial strains with the ability to produce C16:1 and to find a practical manner to utilize C16:1-producing strains in industry. Various strains were screened for their content of C16:1.

Active shape model registration of ocular structures in computed tomography images.

. The goal of this work is to create an active shape model segmentation method based on the statistical shape model of five regions of the globe on computed tomography (CT) scans and to use the method to categorize normal globe from globe injury..