Deep learning to quantify glaucomatous damage on fundus photographs for teleophthalmology

深度学习量化眼底照片上的青光眼损伤，用于远程眼科

基本信息

批准号：
10348705
负责人：
Atalie C Thompson
金额：
$ 19.48万
依托单位：
WAKE FOREST UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-02 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10348705
关键词：
Address Adult Advisory Committees Agreement Algorithms Assisted Living Facilities Biomedical Engineering Biometry Blindness Clinic Clinical Clinical Research Consumption Data Dependence Detection Development Diabetic Retinopathy Diagnosis Discrimination Disease Early Diagnosis Effectiveness Environment Epidemiology Evaluation Eye Eye diseases Fellowship Frequencies Fundus Fundus photography Glaucoma Goals Gold Grant Human Image Imaging technology Improve Access Individual Label Machine Learning Manuals Masks Master&apos s Degree Measurement Medical Medicine Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Nature Optic Disk Optical Coherence Tomography Output Patients Perimetry Primary Health Care Public Health Reference Standards Reproducibility Research Research Priority Research Proposals Resource-limited setting Resources Scientist Screening procedure Sensitivity and Specificity Severity of illness Specialist Suspect Glaucomas Technology Testing Thick Time Training Validation Visual Fields Visual impairment Width Work algorithm training artificial intelligence algorithm career carina cohort cost cost effective deep learning deep learning algorithm deep neural network demographics diagnostic tool experience eye center health care disparity high risk improved innovation interest learning network neural network novel novel diagnostics population based programs prospective public health intervention responsible research conduct retinal nerve fiber layer screening teleophthalmology tool

项目摘要

PROJECT SUMMARY/ABSTRACT Candidate: Atalie Carina Thompson, MD, MPH is a current glaucoma fellow and Heed fellow with a long-term career goal of becoming an independent clinician-scientist and leader in the field of glaucoma and public health. She has a long-standing interest in addressing healthcare disparities in medicine, and in improving the diagnosis of glaucoma and other ophthalmic diseases through imaging technology. While obtaining a medical degree at Stanford, she received a fellowship to complete a master’s degree in public health with additional higher-level coursework in biostatistics and epidemiology. Her immediate goal in this proposal is to refine and validate a deep learning (DL) algorithm capable of quantifying neuroretinal damage on optic disc photographs and then to apply it in a pilot teleophthalmology program. With a K23 Mentored Patient-Oriented Research Career Development Award, she will acquire additional didactic training and mentored research experience in glaucoma imaging, machine learning, biostatistics, clinical research, and the responsible conduct of research. Environment: The mentorship and expertise of the advisory committee, the extensive resources at the Duke Eye Center and Departments of Biostatistics and Biomedical Engineering, and the significant institutional commitment will provide her with the support needed to transition successfully into an independent clinician-scientist. Research: This proposal will test the hypothesis that a DL algorithm trained with SDOCT detects glaucoma on optic disc photographs with greater accuracy than human graders. In Specific Aim 1, a DL algorithm that quantifies neuroretinal damage on optic disc photographs will be refined. The main hypothesis is that the quantitative output provided by the DL algorithm will allow accurate discrimination of eyes at different stages of the disease according to standard automated perimetry, and will generate cut-offs suitable for use in a screening setting. In Specific Aim 2, the short-term repeatability and reproducibility of the DL algorithm in optic disc photographs acquired over a time period of several weeks will be determined. The hypothesis is that the test-retest variability of the predictions from the DL algorithm will be similar to the original measurements acquired by SDOCT. In Specific Aim 3, the DL algorithm will be applied to optic disc photographs obtained during a pilot screening teleophthalmology program in primary care clinics and assisted living facilities. The hypothesis is that the DL algorithm will be more accurate than human graders when a full ophthalmic examination is used as the gold standard. This work will constitute the basis of an R01 grant and will advance our understanding of the application of deep learning algorithms in glaucoma and teleophthalmology.

项目概要/摘要候选人： Atalie Carina Thompson，医学博士、公共卫生硕士，现任青光眼研究员和 Heed 研究员，长期从事青光眼研究职业目标是成为一名独立的临床科学家和青光眼和公共卫生领域的领导者。她长期以来对解决医学领域的医疗保健差异和改善诊断有着浓厚的兴趣通过成像技术治疗青光眼和其他眼科疾病，同时获得医学学位。斯坦福大学，她获得了奖学金，以完成公共卫生硕士学位和其他更高级别的学位她在该提案中的直接目标是完善和验证深入的生物统计学和流行病学课程。学习（DL）算法能够量化视盘照片上的神经视网膜损伤，然后应用通过 K23 指导的以患者为导向的研究职业发展。获奖后，她将获得青光眼成像方面的额外教学培训和指导研究经验，机器学习、生物统计学、临床研究和负责任的研究行为：环境。咨询委员会的指导和专业知识、杜克眼科中心的广泛资源以及生物统计学和生物医学工程系以及重大的机构承诺将为她提供成功转型为独立临床科学家研究所需的支持：该提案将测试以下假设：使用 SDOCT 训练的深度学习算法可检测视神经盘上的青光眼在特定目标 1 中，深度学习算法能够比人类评分者更准确地进行量化。视盘照片上的神经视网膜损伤将被细化，主要假设是定量输出。 DL算法提供的功能将能够准确区分眼睛在疾病的不同阶段根据标准自动视野检查，并将生成适合在筛查环境中使用的截止值。具体目标 2，DL 算法在视盘照片中的短期重复性和再现性将确定几周时间段内获得的数据。假设是重测变异性。 DL 算法的预测结果将与 SDOCT 获取的原始测量结果相似。具体目标 3，深度学习算法将应用于试点筛选过程中获得的视盘照片初级保健诊所和辅助生活设施中的远程眼科项目的假设是 DL。当使用全面的眼科检查作为黄金时，算法将比人类评分者更准确这项工作将构成 R01 资助的基础，并将增进我们对申请的理解。青光眼和远程眼科深度学习算法的研究。