# Automated Detection and Classification of Laryngeal Diseases Using Deep Neural Networks

> **NIH NIH R03** · UNIVERSITY OF KANSAS MEDICAL CENTER · 2020 · $154,375

## Abstract

PROJECT SUMMARY
The long-term goal of this project is to improve the care of patients with laryngeal disorders through
development of automated diagnostic support for in-office flexible laryngoscopy. To accomplish this goal, we
propose developing neural network-based algorithms to detect and classify structural laryngeal lesions in
laryngoscopy images. An automated diagnostic tool for in-office laryngoscopy such as we propose will have
several benefits: (1) It will improve access to care for patients with symptoms of laryngeal dysfunction living in
communities with limited otolaryngology resources, (2) It will improve early detection of laryngeal cancers
potentially reducing the morbidity of treatment, and (3) It will prove a valuable teaching tool for students and
residents first learning to interpret laryngoscopic exams.
Flexible laryngoscopy is a common in-office procedure performed by otolaryngologists to evaluate the upper
aerodigestive tract in patients with symptoms of laryngeal dysfunction. Subtle differences in the appearance of
laryngeal lesions enable otolaryngologists to differentiate benign lesions from suspected malignant ones. The
expertise and clinical acumen to correctly interpret laryngoscopic findings requires years of training and
therefore laryngoscopy is largely only performed in subspecialty otolaryngology clinics. The primary objective
of this project is to develop neural network-based algorithms to detect and classify structural laryngeal lesions.
Our hypothesis is that these algorithms can be trained using a large dataset of laryngeal images to accurately
detect and classify structural laryngeal lesions on flexible laryngoscopic exam. To test this hypothesis, we
propose the following aims: (1) Generate a dataset of high-quality, labeled endoscopic laryngeal images
corresponding to normal and structural lesions of the larynx, (2) Develop a location-aware anchor-based
reasoning neural network for accurate detection of laryngeal lesions, and (3) Develop an adaptive network
model for classification of structural laryngeal pathologies including papilloma, polyp, leukoplakia and
suspected malignancy. With expertise in the diagnosis and treatment of laryngeal disorders and computer
vision, including object detection and classification, our multidisciplinary team is uniquely qualified to complete
this project.

## Key facts

- **NIH application ID:** 10043172
- **Project number:** 1R03CA253212-01
- **Recipient organization:** UNIVERSITY OF KANSAS MEDICAL CENTER
- **Principal Investigator:** Andres Martin Bur
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $154,375
- **Award type:** 1
- **Project period:** 2020-07-10 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10043172

## Citation

> US National Institutes of Health, RePORTER application 10043172, Automated Detection and Classification of Laryngeal Diseases Using Deep Neural Networks (1R03CA253212-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10043172. Licensed CC0.

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