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Automated Detection and Classification of Laryngeal Diseases Using Deep Neural Networks

使用深度神经网络自动检测和分类喉部疾病

基本信息

批准号：
10043172
负责人：
Andres Martin Bur
金额：
$ 15.44万
依托单位：
UNIVERSITY OF KANSAS MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-10 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10043172
关键词：
Aerodigestive Tract Algorithms Anesthesia procedures Appearance Architecture Awareness Benign Caring Categories Cessation of life Classification Clinic Clinical Collaborations Colonic Polyps Colonoscopy Communities Computer Vision Systems Custom Data Set Detection Development Diagnosis Diagnostic Disease Disease Progression Distal Drops Early Diagnosis Educational process of instructing Ensure Fellowship Functional disorder Gastroesophageal reflux disease Goals Health Services Accessibility Hoarseness Image Improve Access Infection Label Laryngeal Diseases Laryngoscopes Laryngoscopy Larynx Learning Lesion Leukoplakia Location Malignant - descriptor Malignant Neoplasms Malignant neoplasm of larynx Manuals Modeling Modernization Morbidity - disease rate Network-based Normal Range Otolaryngologist Otolaryngology Papilloma Pathology Patient Care Patients Performance Pilot Projects Plug-in Polyps Positioning Attribute Procedures Recurrence Resources Sampling Semantics Structure Students Symptoms System Technical Expertise Testing Training Vision research Work base classification algorithm cost deep neural network detector digital experience feature extraction flexibility improved large datasets learning algorithm multidisciplinary network models neural network tool

项目摘要

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.

项目概要该项目的长期目标是通过以下方式改善喉部疾病患者的护理开发办公室内灵活喉镜的自动诊断支持。为了实现这一目标，我们建议开发基于神经网络的算法来检测和分类喉部结构性病变喉镜检查图像。我们建议的用于办公室喉镜检查的自动诊断工具将具有几个好处：（1）它将改善生活在有喉功能障碍症状的患者获得护理的机会耳鼻喉科资源有限的社区，（2）它将改善喉癌的早期发现潜在地降低治疗的发病率，并且（3）它将被证明是对学生和学生来说是一个有价值的教学工具住院医师首先学习解释喉镜检查。柔性喉镜检查是耳鼻喉科医师进行的一种常见的诊室手术，用于评估上颌骨有喉功能障碍症状的呼吸消化道患者。外观上的细微差别喉部病变使耳鼻喉科医师能够区分良性病变和疑似恶性病变。这正确解释喉镜检查结果的专业知识和临床敏锐度需要多年的培训和因此，喉镜检查主要只在耳鼻喉专科诊所进行。主要目标该项目的目的是开发基于神经网络的算法来检测和分类结构性喉部病变。我们的假设是，这些算法可以使用大型喉部图像数据集进行训练，以准确地通过灵活的喉镜检查检测和分类喉部结构性病变。为了检验这个假设，我们提出以下目标：（1）生成高质量、标记的内窥镜喉图像数据集对应于喉部的正常和结构性病变，（2）开发基于位置感知的锚点推理神经网络用于准确检测喉部病变，以及（3）开发自适应网络喉部结构性病变分类模型，包括乳头状瘤、息肉、白斑和疑似恶性肿瘤。拥有喉疾病诊断和治疗以及计算机方面的专业知识视觉，包括物体检测和分类，我们的多学科团队具有独特的资格来完成这个项目。