AI-Based Identification of Rapid Glaucoma Progression to Guide Clinical Management and Accelerate Clinical Trials

基于人工智能的青光眼快速进展识别，指导临床管理并加速临床试验

• 批准号: 10553060
• 负责人: Ali Tafreshi
• 金额: $ 23.98万
• 依托单位: AISIGHT HEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10553060
• 关键词: Address; Affect; Age; Area; Artificial Intelligence; Blindness; Caring; Clinic; Clinical; Clinical Data; Clinical Management; Clinical Research; Clinical Trials; Consumption; Data; Data Set; Degenerative Disorder; Detection; Development; Disease; Disease Progression; Early Intervention; Early identification; Engineering; Evaluation; Expert Systems; Eye; Fundus; Glaucoma; Goals; Health Care Costs; Healthcare Systems; Image; Lead; Length; Letters; Longitudinal cohort; Measurement; Measures; Medical; Methods; Modeling; Operative Surgical Procedures; Ophthalmology; Optical Coherence Tomography; Participant; Patient Care; Patients; Performance; Persons; Pharmacologic Substance; Phase; Quality of life; Recording of previous events; Records; Research; Sample Size; Sampling Studies; Severity of illness; Small Business Technology Transfer Research; Specialist; Speed; Structure; System; Techniques; Testing; Therapeutic; Time; Training; Vision; Visit; Visual Fields; Work; base; care costs; clinical care; clinical decision support; cohort; commercial application; cost; dashboard; deep learning; deep learning model; demographics; design; drug development; effective intervention; field study; follow-up; improved; improved outcome; longitudinal dataset; multimodality; multiple data types; neuroprotection; novel; novel therapeutics; predictive modeling; predictive tools; primary outcome; recruit; research study; sex; support tools; tool; treatment response

Project Summary Glaucoma is the leading cause of irreversible blindness worldwide and is expected to affect more than 110 million people worldwide within the next two decades. It is a degenerative disease that has a large impact both in terms of patient quality of life and in costs to the healthcare system. A critical need in glaucoma clinical management and research is the ability to accurately identify patients likely to undergo rapid disease progression (i.e., lose visual function quickly). Currently, estimating the rate of progression for a patient requires several follow-up visits over the course of multiple years. This delay in identifying progression leads to lost vision and increases the cost of care. It also impacts clinical trials in glaucoma, increasing the time and cost needed to investigate novel therapies for the disease. The goal of this Phase I STTR proposal is to use artificial intelligence techniques to improve the accuracy and shorten the time for identifying raid progression in glaucoma. The primary outcome of our Phase I proposal will enable an AI-based tool to identify rapid glaucomatous progression and will be immediately ready for use in Phase 1/2a clinical trials as FDA approval is not required. Specifically, we will (1) use longitudinal optical coherence tomography (OCT) imaging and visual field (VF) testing dataset to train AI models to identify rapidly progressing glaucoma patients and (2) incorporate patient data, clinical measurements, and treatment history into the AI models to further improve performance. AI models will be trained and evaluated on a combination of research and real-world clinical data. These datasets include tens of thousands of images, VF tests, and clinical records collected from a diverse cohort of more than 9,000 glaucoma patients over the course of more than a decade. These datasets provide us with a unique opportunity to not only train AI models, but also to characterize model performance as a function of patient demographics, clinical covariates, disease severity, and follow-up length – providing critical context to help clinicians better understand model predictions. Accurate and early predictions would be of great benefit to both clinical management and clinical trials in glaucoma. Improved outcomes, reduced patient care and drug development costs, and faster development of glaucoma therapeutics make tools that quickly identify progressors an attractive product for our target customers, pharmaceutical companies and eye care specialists.

项目摘要 青光眼是全球不可逆失明的主要原因，预计会影响超过1.1亿 在接下来的二十年中，全球人是一种退化性疾病 患者的生活质量和医疗保健系统的成本。 研究是能够准确识别可能造成快速疾病进展的患者的能力（即失去，失败 目前的视觉功能很快，估计患者的进度率 在多年的过程中 护理也影响了青光眼的临床试验，增加了研究的时间和成本 疾病的目标是使用人工智能技术 改善识别青光眼进展的时间。 我们的I阶段建议使用启用基于AI的工具来识别快速的青光眼进展，并将是 立即准备在第1阶段/2A临床试验中使用，因为不需要FDA批准。 明天使用纵向光学连贯性（OCT）成像和视野（VF）测试数据集训练AI 识别快速发展的青光眼患者的模型，（2）结合患者数据，临床测量， 和治疗历史记录到Amodels，以进一步提高性能。 关于研究和现实世界数据的组合。 VF测试以及从多种多样的9,000名青光眼患者中收集的临床记录。 十多年的过程。这些数据集为我们提供了一个独特的机会 但也将模型性能描述为患者人口统计学，临床协变量，疾病的函数 严重性和随访长度的关键环境，以帮助临床医生更好地了解模型预测。 准确和早期的预测将对临床管理和临床方面有很大的好处 青光眼。 青光眼治疗剂制造工具，可以迅速识别出对我们目标客户的有吸引力的产品， 制药公司和眼保健专家。