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，预计影响超过1.1亿人 未来二十年内，全世界的人们。这是一种退行性疾病，对人类的 患者的生活质量和医疗保健系统的成本。青光眼临床管理的迫切需要 并且研究是准确识别可能经历快速疾病进展的患者的能力（即，失去 视觉功能快速）。目前，估计患者的进展速度需要多次随访 在几年的时间里。这种识别进展的延迟导致视力丧失并增加成本 护理。它还影响了青光眼的临床试验，增加了研究新药物所需的时间和成本。 该疾病的治疗方法。第一阶段STTR提案的目标是使用人工智能技术， 提高了青光眼raid进展的准确性，缩短了识别时间。的主要结局 我们的第一阶段提案将使基于人工智能的工具能够识别快速的昏迷进展， 立即准备用于1/2a期临床试验，因为不需要FDA批准。具体来说，我们将（1） 使用纵向光学相干断层扫描（OCT）成像和视野（VF）测试数据集来训练AI 识别快速进展的青光眼患者的模型和（2）结合患者数据，临床测量， 和治疗历史输入到AI模型中，以进一步提高性能。AI模型将被训练和评估 结合研究和实际临床数据。这些数据集包括数万张图像， VF测试和临床记录收集了超过9,000名青光眼患者的不同队列， 十多年的历程。这些数据集为我们提供了一个独特的机会，不仅可以训练AI模型， 而且还将模型性能表征为患者人口统计学、临床协变量、疾病 严重程度和随访时间-提供关键背景，帮助临床医生更好地理解模型预测。 准确和早期的预测将对临床管理和临床试验都有很大好处， 青光眼改善结果，降低患者护理和药物开发成本，加快药物开发 青光眼治疗使快速识别进展者的工具成为对我们目标客户有吸引力的产品， 制药公司和眼部护理专家。