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Related Experiment Video

Updated: Feb 4, 2026

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using Deep Learning.

Sanjana Kaza¹, Abhinita S Mohanty², Aisha Serpedin³

¹Cornell Tech, New York, New York, USA.

The Laryngoscope

|February 2, 2026

Summary

This summary is machine-generated.

A deep learning system using YOLO12 with temporal tracking effectively identifies vocal fold polyps in videos. This AI-assisted approach achieves near real-time performance for detecting vocal fold lesions.

Keywords:

artificial intelligence object detection stroboscopy vocal fold polyp

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Related Experiment Videos

Last Updated: Feb 4, 2026

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Preparation of the Rat Vocal Fold for Neuromuscular Analyses

Preparation of the Rat Vocal Fold for Neuromuscular Analyses

Published on: May 15, 2020

Construction and Characterization of a Novel Vocal Fold Bioreactor

Construction and Characterization of a Novel Vocal Fold Bioreactor

Published on: August 1, 2014

Area of Science:

Medical imaging
Artificial intelligence
Otolaryngology

Background:

Vocal fold polyps are lesions that can affect voice quality.
Accurate and timely detection of vocal fold polyps is crucial for diagnosis and treatment.
Current detection methods may benefit from AI-driven enhancements.

Purpose of the Study:

To develop and evaluate a deep learning object detection system for identifying vocal fold polyps in stroboscopic video frames using You Only Look Once (YOLO).
To assess the added benefit of temporal tracking on the detection performance of vocal fold polyps.
To explore the potential of real-time AI-assisted detection of vocal fold lesions.

Main Methods:

A dataset of 12,742 annotated frames from 55 laryngoscopy videos was used.
Pretrained YOLO11 and YOLO12 models were fine-tuned for polyp detection.
A temporal tracking algorithm was developed to improve detection consistency across frames.

Main Results:

YOLO12 significantly outperformed YOLO11 in polyp detection metrics.
YOLO12 achieved a precision of 83.1% and an F1 score of 67.6% on the test set.
Temporal tracking with YOLO12 increased mean average precision (mAP@0.5) to 70.4% at near real-time speeds (21.4 fps).

Conclusions:

YOLO12 combined with temporal tracking offers enhanced performance for vocal fold polyp detection.
The system demonstrates near real-time capabilities, facilitating potential clinical applications.
AI-assisted detection holds promise for improving the identification of vocal fold lesions.