NSF/FDA SIR: A Modeling Tool for Assessment of Radiological Workflow Prioritization Based on Computer-assisted Diagnosis

NSF/FDA SIR：基于计算机辅助诊断的放射工作流程优先级评估建模工具

• 批准号: 1935809
• 负责人: Itai Gurvich
• 金额: $ 15万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935809&HistoricalAwards=false
• 关键词: NSF; FDA; SIR; Modeling; Tool

Advances in machine learning (ML) algorithms support the diagnosis and detection of various disease conditions within minutes of medical image acquisition. One emerging application of these algorithms is as an aid in determining the priority for (human) image review of a patient with an abnormal condition. Since 2018, the FDA has approved several of these ML-aided triage devices. The patient/case-level output of these algorithms does not replace the interpretation by a radiologist but it can inform the radiologist's prioritization of case reviews and hence the radiologist's workflow. On one hand, such ML algorithms can lead to better patient outcomes by increasing the likelihood for earlier diagnosis and treatment of severe and time-sensitive conditions. On the other hand, the radiologist’s re-prioritization of work can delay the review of cases that are incorrectly analyzed and missed or those not in the scope of the ML algorithm. While the algorithms typically process the images in minutes, the overall, risk-adjusted, patient waiting time-saving benefits are difficult to discern due to the complexity of the clinician's workflow. To improve the understanding of the interplay between the ML-aided triage and the workflow into which it feeds, this project develops flexible analytical and simulation models which accommodate various ML-aided triage algorithms and workflow management rules in clinical reading. The aim of the research is to provide regulators with a quantitative principled approach to evaluate, in a workflow-relevant way, the prioritization performance of these triage algorithms. The results of this project will enable developers to identify optimal prioritization strategies, and let users have science-based information about these devices so that they can make informed health care decisions.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.

机器学习 (ML) 算法的进步支持在医学图像采集后几分钟内诊断和检测各种疾病状况。这些算法的一个新兴应用是帮助确定异常状况患者的（人类）图像审查的优先级。自 2018 年以来，FDA 已批准了多种机器学习辅助分诊设备。这些算法的患者/病例级输出不会取代放射科医生的解释，但它可以告知放射科医生病例审查的优先顺序，从而告知放射科医生的工作流程。一方面，此类机器学习算法可以通过增加早期诊断和治疗严重且时间敏感的疾病的可能性，从而带来更好的患者治疗结果。另一方面，放射科医生重新调整工作优先顺序可能会延迟对分析错误和遗漏的病例或不在机器学习算法范围内的病例的审查。虽然算法通常可以在几分钟内处理图像，但由于临床医生工作流程的复杂性，总体而言，经过风险调整、节省患者等待时间的好处很难辨别。为了加深对机器学习辅助分诊与其所输入的工作流程之间相互作用的理解，该项目开发了灵活的分析和模拟模型，可适应临床阅片中的各种机器学习辅助分诊算法和工作流程管理规则。该研究的目的是为监管机构提供一种定量原理方法，以与工作流程相关的方式评估这些分类算法的优先级性能。该项目的结果将使开发人员能够确定最佳优先策略，并让用户获得有关这些设备的基于科学的信息，以便他们能够做出明智的医疗保健决策。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SPT optimality (mostly) via linear programming

SPT 最优性（大部分）通过线性规划

• DOI: 10.1016/j.orl.2022.12.007
• 发表时间: 2023
• 期刊: Operations Research Letters
• 影响因子: 1.1
• 作者: Cho, Woo-Hyung;Shmoys, David;Henderson, Shane
• 通讯作者: Henderson, Shane