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References (25)

[1]
Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy
2025Tobias Schraut, A. Schützenberger et al.
[2]
Comparative Evaluation of High-Speed Videoendoscopy and Laryngovideostroboscopy for Functional Laryngeal Assessment in Clinical Practice
2025Joanna Hoffman, M. Barańska et al.
[3]
GIRAFE: Glottal Imaging Dataset for Advanced Segmentation, Analysis, and Facilitative Playbacks Evaluation
2024G. Andrade-Miranda, K. Chatzipapas et al.
[4]
A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method
2024S. M. N. Nobel, S. R. Swapno et al.
[5]
S3AR U-Net: A separable squeezed similarity attention-gated residual U-Net for glottis segmentation
2024Francis Jesmar Montalbo
[6]
Segment Anything
2023A. Kirillov, Eric Mintun et al.
[7]
Segment Anything in Medical Images
2023Jun Ma, Bo Wang
[8]
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
2022Junde Wu, Huihui Fang et al.
[9]
Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos
2022M. Döllinger, Tobias Schraut et al.
[10]
A single latent channel is sufficient for biomedical glottis segmentation
2022A. Kist, Katharina Breininger et al.
[11]
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
2021Hu Cao, Yueyue Wang et al.
[12]
Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network
2020M. Fehling, Fabian Grosch et al.
[13]
Decoupled Weight Decay Regularization
2017I. Loshchilov, F. Hutter
[14]
Mask R-CNN
2017Kaiming He, Georgia Gkioxari et al.
[15]
SGDR: Stochastic Gradient Descent with Warm Restarts
2016I. Loshchilov, F. Hutter
[16]
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
2016Fausto Milletarì, N. Navab et al.
[17]
Effects of Vocal Fold Nodules on Glottal Cycle Measurements Derived from High-Speed Videoendoscopy in Children
2016Rita R. Patel, H. Unnikrishnan et al.
[18]
An automatic method to detect and track the glottal gap from high speed videoendoscopic images
2015G. Andrade-Miranda, Juan Ignacio Godino-Llorente et al.
[19]
U-Net: Convolutional Networks for Biomedical Image Segmentation
2015O. Ronneberger, P. Fischer et al.
[20]
Kinematic measurements of the vocal-fold displacement waveform in typical children and adult populations: quantification of high-speed endoscopic videos.
2015Rita R. Patel, K. D. Donohue et al.

Showing 20 of 25 references

Founder's Pitch

"A zero-shot glottal segmentation AI for real-time clinical voice assessment using videoendoscopy."

Healthcare AIScore: 8View PDF ↗

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0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

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Why It Matters

This research offers a significant advancement in real-time clinical voice assessment by enabling accurate glottal segmentation across varied clinical settings without requiring dataset-specific tuning, thus potentially standardizing and expanding diagnostic capabilities.

Product Angle

This can be turned into a software tool or service for clinics specializing in voice disorders, allowing seamless integration with existing endoscopic equipment, facilitating real-time diagnosis and patient monitoring.

Disruption

This approach could replace current manual or less accurate segmentation methods, potentially setting a new standard in laryngoscopy-assisted diagnosis by offering automated and reliable assessments.

Product Opportunity

The market potential spans ENT specialists and hospitals with significant demand for non-invasive diagnostic tools. The use of AI in healthcare diagnostics is a rapidly growing field with substantial investment opportunity.

Use Case Idea

Develop an application for ENT specialists that uses this AI to evaluate vocal fold function in real-time during videoendoscopy, providing immediate diagnostic insights and extracting clinical biomarkers.

Science

The paper introduces a detection-gated pipeline integrating YOLOv8 and U-Net for glottal segmentation in high-speed videoendoscopy. It uses a detection gate to suppress false positives from non-glottal frames and enables zero-shot cross-dataset functionality, achieving state-of-the-art results without fine-tuning.

Method & Eval

The method was tested on the GIRAFE and BAGLS datasets, achieving DSC scores of 0.81 and 0.85, respectively. It demonstrated significant cross-dataset transfer capability without needing fine-tuning, verified by a clinical cohort study.

Caveats

The approach depends on the quality and consistency of videoendoscopic images, and there may be limitations with extremely varied conditions or hardware not covered by the datasets used.

Author Intelligence

Harikrishnan Unnikrishnan

LEAD
Orchard Robotics, San Francisco, California, USA
hari@orchard-robotics.com