STTR Phase I: AI-assisted Assessment, Tracking, and Reporting of COVID-19 Severity on Chest CT

这个小企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力是利用人工智能（AI）来减少错误并提高胸部计算机断层扫描（CT）图像上COVID-19肺部疾病严重程度评估的准确性，标准化，一致性和报告。胸部CT程序在COVID-19患者中起着关键作用，但目前用于评估胸部CT图像的方法缺乏准确、定量或一致的信息，导致难以解释的基于文本的报告。拟议的人工智能辅助COVID-19胸部CT工作流程将通过提供标准化的图形报告、重要发现的关键图像和结构化文本，有效捕获肺部受累的部分，并改善与临床医生的沟通。定量数据将标准化个体患者的报告，并为人群水平的分析提供数据，从而有可能在国家层面上显著提高对COVID-19肺病的科学认识。该STTR第一阶段项目建议开发一个人工智能辅助的COVID-19胸部CT工作流程，以快速客观地量化肺部受累的百分比，使用COVID-19报告和数据系统（CO-RADS）对肺部受累进行分类，跟踪常见和不常见的COVID-19肺部发现，并自动生成带有图表、关键图像和结构化文本的摘要报告。用于评估和报告胸部CT图像上的COVID-19肺部疾病严重程度的护理标准涉及口述的基于文本的报告，这些报告主观、高度可变、生成和解释效率低、容易出错、不完整且定性，数据以非标准化格式提供。拟议的人工智能辅助COVID-19胸部CT工作流程将减少解读错误和遗漏，并提高准确性、标准化、观察者间的一致性、效率和报告，以评估COVID-19疾病的严重程度和治疗反应。该项目将与临床专家团队一起验证工作原型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact /commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to leverage artificial intelligence (AI) to reduce errors and improve accuracy, standardization, agreement, and reporting in evaluation of COVID-19 lung disease severity on chest computed tomography (CT) images. Chest CT procedures play a critical role in COVID-19 patients but current methods for evaluating chest CT images lack accurate, quantitative, or consistent information, leading to text-based reports that are difficult to interpret. The proposed AI-assisted COVID-19 chest CT workflow will efficiently capture the fraction of lung involvement and improve communication with clinicians by providing a standardized graphical report, key images of important findings, and structured text. The quantitative data will standardize reporting on an individual patient basis and provide data for population-level analyses, thereby offering the potential to significantly advance scientific knowledge of COVID-19 lung disease on a national level. This STTR Phase I project proposes to develop an AI-assisted COVID-19 chest CT workflow to rapidly and objectively quantify the percentage of lung involvement, classify lung involvement using the COVID-19 Reporting and Data System (CO-RADS), track common and uncommon COVID-19 lung findings, and automatically generate summary reports with a graph, key images, and structured text. The standard-of-care for assessing and reporting COVID-19 lung disease severity on chest CT images involves dictated text-based reports that are subjective, highly variable, inefficient to generate and interpret, prone to errors, incomplete, and qualitative with data provided in an unstandardized format. The proposed AI-assisted COVID-19 chest CT workflow will reduce interpretation errors and omissions and improve accuracy, standardization, inter-observer agreement, efficiency, and reporting in evaluation of COVID-19 disease severity and response to treatment. This project will validate the working prototype with a team of expert clinicians.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.