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: 10600984
• 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/10600984
• 关键词: Address; Adult; Advisory Committees; Agreement; Algorithms; Assisted Living Facilities; Binding; Biomedical Engineering; Biometry; Blindness; 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; Grant; Human; Image; Imaging technology; Improve Access; Institution; Label; Machine Learning; Manuals; Masks; Master of Science; Measurement; Medical; Medicine; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Nature; Optic Disk; Optical Coherence Tomography; Output; Patients; Perimetry; Public Health; Reference Standards; Reproducibility; Research; Research Priority; 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; high risk population; improved; innovation; interest; learning network; neural network; novel; novel diagnostics; ophthalmic examination; optical disc; population based; primary care clinic; programs; prospective; public health intervention; responsible research conduct; retinal nerve fiber layer; screening; teleophthalmology; tonometry; 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.

项目总结/摘要 候选人：Atalie Carina Thompson，医学博士，公共卫生硕士，目前是青光眼研究员和Heed研究员， 职业目标是成为独立的临床科学家和青光眼和公共卫生领域的领导者。 她长期以来一直致力于解决医疗保健方面的差异，并改善诊断。 青光眼和其他眼科疾病的诊断。虽然获得医学学位， 在斯坦福大学，她获得了一笔奖学金，完成了公共卫生硕士学位，并获得了更高级别的奖学金。 生物统计学和流行病学课程。她在这个提案中的直接目标是完善和验证一个深入的 学习（DL）算法，其能够量化视盘照片上的神经视网膜损伤，然后应用 它在一个试点远程眼科计划。K23指导的以患者为导向的研究职业发展 奖，她将获得额外的教学培训和指导青光眼成像的研究经验， 机器学习、生物统计学、临床研究以及负责任的研究行为。环境：The 顾问委员会的指导和专业知识，杜克眼科中心的广泛资源， 生物统计学和生物医学工程系，以及重要的机构承诺将 为她提供所需的支持，成功地过渡到一个独立的临床医生，科学家。调研： 该提案将测试用SDOCT训练的DL算法检测视盘上的青光眼的假设 照片的准确性比人类的分级者更高。在具体目标1中，量化 视神经视网膜损伤的视盘照片将被细化。主要假设是， 由DL算法提供的将允许在疾病的不同阶段准确地区分眼睛 根据标准自动视野检查，并将产生适用于筛选设置的截止值。在 具体目标2，视盘照片中DL算法的短期重复性和再现性 将确定在几周内获得的。假设重测变异性 来自DL算法的预测结果将类似于由SDOCT获取的原始测量结果。在 具体目标3，DL算法将应用于试点筛查期间获得的视盘照片 初级保健诊所和辅助生活设施中的远程眼科学计划。假设DL 当一个完整的眼科检查被用作黄金时， 标准这项工作将构成R 01赠款的基础，并将促进我们对应用程序的理解 深度学习算法在青光眼和远程眼科学中的应用