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Deep Learning Approaches for Personalized Modeling and Forecasting of Glaucomatous Changes

用于青光眼变化个性化建模和预测的深度学习方法

基本信息

批准号：
10089451
负责人：
HIROSHI ISHIKAWA
金额：
$ 12.47万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
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：
2020-02-01 至 2021-08-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10089451
关键词：
3-Dimensional Area Atlases Blindness Brain Caring Clinic Clinic Visits Clinical Clinical Management Collaborations Color Complex Cross-Sectional Studies Custom Data Data Set Decision Making Detection Development Disease Disease Progression Disease model Early Diagnosis Eye Future Glaucoma Image Image Analysis Imaging technology Individual Intervention Investments Knowledge Learning Location Machine Learning Magnetic Resonance Imaging Maps Measurable Measurement Medical Imaging Methods Modeling Monitor Morbidity - disease rate Ophthalmology Optical Coherence Tomography Outcome Patients Performance Research Research Proposals Retina Sampling Series Speed Structure System Techniques Technology Testing Thick Thinness Time Tissues Training Vision Visit Visual Fields analytical method base case-by-case basis clinical application clinical practice cohort computerized cost deep learning falls feature selection follow-up image processing imaging modality improved in vivo individual patient innovation insight knowledge base longitudinal analysis longitudinal dataset machine learning method novel ocular imaging personalized approach personalized medicine personalized predictions predictive modeling preservation prevent programs retinal nerve fiber layer theories tool treatment planning trend

项目摘要

Project Summary Glaucoma is a leading cause of vision morbidity and blindness worldwide. Early disease detection and sensitive monitoring of progression are crucial to allow timely treatment for preservation of vision. The introduction of ocular imaging technologies significantly improves these capabilities, but in clinical practice there are still substantial challenges at managing the optimal care for individual cases due to difficulties of accurately assessing the potential progression and its speed and magnitude. These difficulties are due to a variety of causes that change over the course of the disease, including large inter-subject variability, inherent measurement variability, image quality, varying dynamic ranges of measurements, minimal measurable level of tissues, etc. In this proposal, we propose novel agnostic data-driven deep learning approaches to detect glaucoma and accurately forecast its progression that are optimized to each individual case. We will use state- of-the-art automated computerized machine learning methods, namely the deep learning approach, to identify structural features embedded within OCT images that are associated with glaucoma and its progression without any a priori assumptions. This will provide novel insight into structural information, and has shown very encouraging preliminary results. Instead of relying on the conventional knowledge-based approaches (e.g. quantifying tissues known to be significantly associated with glaucoma such as retinal nerve fiber layer), the proposed cutting-edge agnostic deep learning approaches determine the features responsible for future structural and functional changes out of thousands of features autonomously by learning from the provided large longitudinal dataset. This program will advance the use of structural and functional information obtained in the clinics with a substantial impact on the clinical management of subjects with glaucoma. Furthermore, the developed methods have potentials to be applied to various clinical applications beyond glaucoma and ophthalmology.

项目总结