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

• 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
• 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' 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

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 通过影像技术诊断青光眼和其他眼科疾病。在获得医学学位的同时 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 生物统计学和流行病学课程。她在此提案中的直接目标是完善和验证深层的 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 它在远程眼科试点计划中。通过 K23 指导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医生科学家所需的支持。研究： 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 比人类评分者更准确的照片。在具体目标 1 中，深度学习算法量化了 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。在 具体目标 2，DL 算法在视神经盘照片中的短期重复性和再现性 将确定在几周的时间内获得的。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。在 具体目标 3，DL 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的远程眼科计划。假设 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 标准。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。