Applications of artificial intelligence to the diagnostic evaluation of infectious keratitis

人工智能在感染性角膜炎诊断评估中的应用

• 批准号: 10449624
• 负责人: Travis Kenneth Redd
• 金额: $ 26.71万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10449624
• 关键词: Appearance; Artificial Intelligence; Biometry; Blindness; Cicatrix; Clinical; Clinical Data; Clinical Management; Clinical Microbiology; Clinical Trials; Collaborations; Computer Vision Systems; Cornea; Corneal Ulcer; Data; Data Collection; Data Science; Data Set; Databases; Development; Diagnosis; Diagnostic; Discipline; Disease; Early treatment; Epidemiology; Equipment; Etiology; Evaluation; Expert Systems; Eye; Faculty; Foundations; Funding; Future; Goals; Gold; Healthcare; Hospitals; Human; Image; India; Infection; Institutes; International; Investigation; Keratitis; Knowledge; Lead; Machine Learning; Medical; Medical Informatics; Medically Underserved Area; Mentored Patient-Oriented Research Career Development Award; Mentors; Microbiology; Microscopic; Modeling; Movement; Multimodal Imaging; Neural Network Simulation; Ophthalmology; Outcome; Performance; Photography; Population; Positioning Attribute; Public Health; Research; Retinopathy of Prematurity; SECTM1 gene; Sampling; Scientist; Sensitivity and Specificity; Specialist; Surgical Management; System; Techniques; Telemedicine; Time; Training; Ulcer; United States National Institutes of Health; Visual; antimicrobial; base; burden of illness; career; clinical database; clinical imaging; clinically significant; convolutional neural network; data infrastructure; deep learning; design; experience; image archival system; imaging modality; impression; improved; interdisciplinary approach; large datasets; ocular imaging; pathogen; population based; programs; rapid diagnosis; recruit; routine Bacterial stain; skills; technological innovation; tool; whole slide imaging

PROJECT SUMMARY/ABSTRACT This K23 proposal aims to develop and evaluate applications of artificial intelligence (AI) to the diagnostic investigation of infectious keratitis, a major cause of blindness worldwide. This will be accomplished through three specific aims: 1) Develop and evaluate an AI model to identify the etiology of culture-proven infectious keratitis from an existing database of clinical photographs; 2) Externally validate model performance in a real- world, population-based sample of corneal ulcers; and 3) Develop and evaluate an additional AI model for automated microscopic diagnosis of fungal keratitis. The AI model developed in SA#1 will be trained using a clinical photography database (the Culture Positive Ulcer Database) collated from several NIH funded clinical trials for infectious keratitis (SCUT, MUTT I & II, CLAIR, and MALIN) conducted over the past several decades as part of the international collaboration between the Francis I. Proctor Foundation and Aravind Eye Hospital in India. This model's performance will be compared against human experts on culture-proven cases of infectious keratitis. A second repository of imaging and clinical data from corneal ulcers (the MADURAI database) currently in development will be used to externally validate the AI model developed in SA#1 (by estimating its sensitivity and specificity in a real-world sample) and to train the AI model in SA#3. To accomplish these research goals, we have established an international collaboration between the Casey Eye Institute, the Proctor Foundation, and Aravind. This provides an unprecedented opportunity to leverage the expertise of my mentors at Casey in artificial intelligence and computer vision-enabled diagnosis of ophthalmic diseases, the expertise of the world-class faculty at Proctor in epidemiology, biostatistics, and infectious keratitis, and the unparalleled volume of infectious keratitis and infrastructure for data collection at Aravind. This collaboration will facilitate the development of carefully designed and validated AI models which will guide earlier directed antimicrobial therapy and improve visual outcomes in infectious keratitis. My primary career goals are to establish myself as an independent clinician scientist performing research at the interface of technological innovation and international public health. My MPH, medical training, and research experience have allowed me to develop a strong foundation in public health, the clinical and surgical management of corneal infections, and medical informatics. Over the past nine months of K12 support I have begun developing expertise in machine learning and data science, establishing a foundation which I will build upon during this K23 award period. The successful application of AI to health care problems requires a multidisciplinary approach involving clinicians, AI methodologists, informaticists, and public health experts. This K23 will allow me to build skills and expertise in each of these disciplines and become well positioned to lead this movement in the coming years.

项目摘要/摘要 该K23提案旨在开发和评估人工智能（AI）的应用 调查感染性角膜炎，这是全球失明的主要原因。这将通过 三个具体目的：1）开发和评估AI模型，以识别培养性传染性的病因 现有临床照片数据库的角膜炎； 2）外部验证模型性能 世界，基于人群的角膜溃疡样本； 3）开发和评估一个其他AI模型 真菌性角膜炎的自动微观诊断。 SA＃1中开发的AI模型将使用 临床摄影数据库（培养阳性溃疡数据库）从几个NIH资助的临床 在过去的几十年中进行的传染性角膜炎试验（Scut，Mutt I＆II，Clair和Malin） 作为弗朗西斯·I·普罗克特基金会和阿拉维德眼科医院之间国际合作的一部分 印度。该模型的表现将与人类有关传染性案例的人类专家进行比较 角膜炎。来自角膜溃疡的成像和临床数据的第二个存储库（Madurai数据库） 当前正在开发的开发将用于外部验证SA＃1中开发的AI模型（通过估算其 现实世界样本中的灵敏度和特异性）并在SA＃3中训练AI模型。完成这些 研究目标，我们已经建立了Casey Eye Institute之间的国际合作 Proctor Foundation和Aravind。这为利用我的专业知识提供了前所未有的机会 凯西（Casey）在人工智能和基本视觉诊断眼科疾病的导师， 流行病学，生物统计学和感染性角膜炎的世界一流教师的专业知识以及 Aravind的数据收集的无与伦比的传染性角膜炎和基础设施。这项合作 将有助于开发经过精心设计和验证的AI模型，该模型将引导早期指导 抗菌治疗并改善感染性角膜炎的视觉结局。 我的主要职业目标是确立自己是一名独立临床医生，从事 技术创新和国际公共卫生的界面。我的MPH，医学培训，以及 研究经验使我能够在公共卫生，临床和外科手术方面奠定坚实的基础 角膜感染和医学信息学的管理。在过去的9个月的K12支持中 开始发展机器学习和数据科学方面的专业知识，建立了我将建立的基础 在此K23奖励期间。 AI成功地应用于医疗保健问题需要 涉及临床医生，AI方法学家，信息主义者和公共卫生专家的多学科方法。这 K23将使我能够在每个学科中建立技能和专业知识，并在领导 未来几年的这一运动。