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A Comprehensive Strategy to Detect Glaucoma Worsening Earlier and With Fewer Tests

通过更少的测试及早发现青光眼恶化的综合策略

基本信息

批准号：
10331842
负责人：
Jithin Yohannan
金额：
$ 19.12万
依托单位：
JOHNS HOPKINS UNIVERSITY
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：
2021-03-01 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10331842
关键词：
Accounting Affect Area Bayesian Analysis Blindness Calendar Clinical Clinical Data Coupled Data Data Science Data Set Detection Diagnostic tests Disease Educational workshop Effectiveness Ethics Eye Future Glaucoma Goals Healthcare Healthcare Systems Image Knowledge Learning Machine Learning Measures Mentored Patient-Oriented Research Career Development Award Mentors Methods Modeling Monitor Optical Coherence Tomography Outcome Patients Pattern Performance Physiologic Intraocular Pressure Population Public Health Pythons Research Research Activity Research Personnel Research Proposals Resources Risk Signal Transduction Stream Structure Techniques Testing Thick Time Training Visit Visual Acuity Visual Fields Work base clinical decision-making compare effectiveness convolutional neural network deep learning deep learning model demographics field study generative adversarial network high risk improved longitudinal database machine learning method machine learning model meetings model design multilevel analysis neural network architecture predictive modeling preservation prevent programs responsible research conduct retinal nerve fiber layer skills tool wasting

项目摘要

ABSTRACT This is an application for a K23 Mentored Patient-Oriented Research Career Development Award. The goal of this proposal is to provide the candidate with the advanced skills needed to establish an independent research program in the area of glaucoma diagnostic testing with special expertise in test error correction and predictive modeling of future glaucoma outcomes. To facilitate this long-term goal, in the current proposal, the candidate’s main research goal is to reduce the time and number of tests necessary to detect glaucoma worsening by (1) correcting for errors in previously obtained visual field (VF) and peripapillary optical coherence tomography (OCT) tests by using multilevel models with Bayesian analysis (MLB) and generative adversarial networks (GAN) (2) stratifying eyes at high and low risk for rapid glaucoma worsening at the baseline clinical visit using deep convolutional neural networks (DCNN). These aims are based on high quality preliminary data which show that: (1) the effect of VF reliability metrics and OCT signal strength on test error can be quantified and thus corrected for and (2) machine learning methods can predict risk of future VF progression with fair accuracy with baseline visit VF data alone and therefore adding structural (OCT) and clinical information from the baseline visit is likely to improve model accuracy. The main hypotheses of the proposed research aims are (1) correcting for test errors with MLB and GAN will reduce the time needed to detect worsening by 10 and 20% respectively (2) combining baseline visit structural (OCT), functional (VF) and clinical data as inputs into DCNNs will allow us to achieve an area under the receiver operating curve of at least 0.8 at predicting the risk of future rapid glaucoma worsening. The candidate proposes a comprehensive training plan, combining formal coursework, meetings, seminars and workshops overseen by his diverse group of mentors. Specific training goals include: (1) Receiving training in multi-level regression modeling and Bayesian analysis techniques. (2) Becoming adept at data science with a special emphasis on learning Python for data extraction, manipulation and analysis. (3) Furthering knowledge of machine learning techniques with a specific emphasis on deep learning including DCNNs and GANs. (4) Continuing training in the ethical and responsible conduct of research. The training plan will be executed in coordination with the set of research activities mentioned above. Results from this research proposal will be used to develop a subsequent R01 research proposal that will facilitate the candidate’s transition to an independent researcher.

摘要