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: 10650861
• 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/10650861
• 关键词: Appearance; Artificial Intelligence; Biological; Biometry; Blindness; Cicatrix; Clinical; Clinical Data; Clinical Management; Clinical Trials; Collaborations; Computer Vision Systems; Cornea; Corneal Ulcer; Data; Data Collection; Data Science; Data Set; Databases; Development; Diagnosis; Diagnostic; Discipline; Disease; Early treatment; Education; Epidemiology; Equipment; Etiology; Evaluation; Expert Systems; Eye; Faculty; Foundations; Funding; Future; Goals; Healthcare; Hospitals; Human; Image; India; Infection; International; Investigation; Keratitis; Knowledge; Lead; Machine Learning; Medical; Medical Informatics; Medically Underserved Area; Mentored Clinical Scientist Development Program; 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; Sampling; Scientist; Sensitivity and Specificity; Specialist; Surgical Management; System; Techniques; Telemedicine; Time; Training; Ulcer; United States National Institutes of Health; Visual; antimicrobial; 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.

项目总结/摘要 该K23提案旨在开发和评估人工智能（AI）在诊断中的应用 传染性角膜炎是世界范围内致盲的主要原因。这将通过以下方式实现： 三个具体目标：1）开发和评估人工智能模型，以确定培养证实的传染性疾病的病因， 2）在真实的中外部验证模型性能， 世界范围内基于人群的角膜溃疡样本;以及3）开发和评估额外的AI模型， 真菌性角膜炎的自动显微镜诊断。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）整理自几个NIH资助的临床 过去几十年进行的感染性角膜炎试验（SCUT、MUTT I & II、CLAIR和MALIN） 作为弗朗西斯一世国际合作的一部分，普罗克特基金会和阿拉文眼科医院 印度该模型的性能将与人类专家在培养证实的传染性病例上进行比较。 角膜炎。第二个角膜溃疡成像和临床数据库（马杜赖数据库） 目前正在开发的AI模型将用于外部验证SA#1中开发的AI模型（通过估计其 真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，我们已经建立了一个国际合作之间的凯西眼科研究所， 普罗克特基金会和亚拉文。这提供了一个前所未有的机会，利用我的专业知识， 凯西在人工智能和计算机视觉支持的眼科疾病诊断方面的导师， 普罗克特在流行病学、生物统计学和感染性角膜炎方面的世界一流教师的专业知识， 在Aravind，传染性角膜炎和基础设施的数据收集量是无与伦比的。这种合作 将促进精心设计和验证的人工智能模型的发展，这些模型将指导早期的指导， 抗微生物治疗和改善感染性角膜炎的视力结果。 我的主要职业目标是建立自己作为一个独立的临床科学家进行研究， 技术创新和国际公共卫生的接口。我的公共卫生硕士，医学训练， 研究经验使我在公共卫生、临床和外科方面打下了坚实的基础。 角膜感染管理和医学信息学。在过去九个月的K12支持中， 我开始发展机器学习和数据科学方面的专业知识，建立了一个基础， 在此期间，K23奖。成功地将AI应用于医疗保健问题需要一个 涉及临床医生，人工智能方法学家，信息学家和公共卫生专家的多学科方法。这 K23将使我能够在这些学科中建立技能和专业知识，并成为领导者 这一运动在未来几年。

项目成果

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks.

• DOI: 10.1016/j.xops.2022.100119
• 发表时间: 2022-06
• 期刊: Ophthalmology science
• 作者: Redd TK;Prajna NV;Srinivasan M;Lalitha P;Krishnan T;Rajaraman R;Venugopal A;Acharya N;Seitzman GD;Lietman TM;Keenan JD;Campbell JP;Song X
• 通讯作者: Song X