A framework to quantify and incorporate uncertainty for ethical application of AI-based quantitative imaging in clinical decision making

量化和纳入基于人工智能的定量成像在临床决策中的伦理应用的不确定性的框架

• 批准号: 10599754
• 负责人: Abhinav K Jha
• 金额: $ 31.48万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10599754
• 关键词: Accounting; Address; Advocate; American College of Radiology Imaging Network; Area; Artificial Intelligence; Attitude; Award; Betula Genus; Clinical; Collaborations; Data; Detection; Diagnosis; Discipline of Nuclear Medicine; Disease; Ethicists; Ethics; Evaluation; Goals; Gold; Heart Diseases; Imaging Device; Interdisciplinary Study; Malignant Neoplasms; Measurement; Measures; Medicine; Metabolic; Methods; Morbidity - disease rate; Multi-Institutional Clinical Trial; Nature; Neurodegenerative Disorders; Non-Small-Cell Lung Carcinoma; Oncologist; Outcomes Research; Output; Parents; Patient Preferences; Patient imaging; Patient risk; Patients; Physicians; Positron-Emission Tomography; Prediction of Response to Therapy; Process; Prospective Studies; Quantitative Evaluations; Questionnaires; Recommendation; Risk; Role; Surveys; Techniques; Testing; Training; Tumor Volume; Uncertainty; Validation; Weight; aggressive therapy; artificial intelligence algorithm; base; clinical application; clinical decision-making; clinical translation; design; fluorodeoxyglucose positron emission tomography; imaging modality; imaging scientist; improved; industry partner; mortality; multidisciplinary; novel; patient advocacy group; personalized medicine; predictive marker; quantitative imaging; segmentation algorithm; simulation; tool; treatment response; tumor

Project Summary: Quantitative imaging (QI), where a numerical/statistical feature is computed from a patient image, is emerging as an important tool for diagnosis and therapy planning. Artificial intelligence (AI)-based QI tools are showing significant promise in this area. However, the measured quantitative value from these tools may also suffer from uncertainty due to various reasons such as limited training data, inaccurate ground truth, mismatch between training and test sets. For ethical application of AI-based QI tools, this uncertainty should be quantified and then incorporated in the clinical decision-making process. This is necessary for the ethical application of these tools, an inference that also emerged from a survey conducted by us across patient advocates (Birch et al, Nature Medicine 2022). Towards addressing this goal, in this proposal, we first propose to develop a novel no-gold-standard method to quantify uncertainty of AI-based QI tools using patient data. Existing uncertainty quantification techniques have mainly been developed for detection tasks, and typically require availability of gold standard. In contrast, the proposed technique will be developed for quantification tasks and not require any gold standard quantitative value. Next, to incorporate the uncertainty of the AI-based QI tool, we propose to propose to develop a questionnaire that will elicit the patient’s risk-value profiles towards treatments. For example, if an AI-based QI tool outputs a quantitative value that indicates aggressive therapy, but with high uncertainty, some patients may be risk averse and prefer to assign high weight to the uncertainty value, while other patients may value the benefits of the treatment and thus assign less weight to that uncertainty. To incorporate these patient preferences, we propose to develop a questionnaire that will elicit the patient’s risk- value profiles. This project will advance on the ongoing activities of our current R01 award on no-gold-standard evaluation of QI methods, extending that project in the context of uncertainty quantification, and thus enabling the use of our tools for not just evaluation, but generating personalized recommendations for each patient. The methods will be developed in the context of the highly significant clinical question of guiding therapy response in patients with stage III non-small cell lung cancer (NSCLC). Answering this question will help address a critical, urgent, and unmet need for strategies to personalize the treatment of NSCLC, a disease with high morbidity and mortality rates. A highly multi-disciplinary team consisting of imaging scientist with expertise in AI, AI ethicists, oncologist, and nuclear-medicine physician have been assembled for this study. This supplement is directly responsive to NOT-OD-22-065 in terms of developing a framework for ethical clinical use of AI. The project will also strengthen the impact of tools we are developing in the parent R01 by using them to guide clinical decision making. Impact will also be strengthened by collaboration with patient advocacy groups and industry partners. Overall, this project is poised to strongly impact the ethical clinical application of QI for treatment of NSCLC, as well as other cancers, cardiac and neurodegenerative diseases where QI has a role.

项目摘要：定量成像（QI），其中计算出患者的数值/统计功能 图像正在成为诊断和治疗计划的重要工具。基于人工智能（AI）的气 工具在这一领域表现出巨大的希望。但是，从这些工具中测得的定量价值 由于培训数据有限，地面真相不准确，可能会遭受不确定性的困扰， 训练和测试集之间的不匹配。对于基于AI的Qi工具的道德应用，这种不确定性应该是 量化然后纳入临床决策过程中。这对于道德是必要的 这些工具的应用，这种推论也来自我们对患者进行的调查 倡导者（Birch等，自然医学2022）。为了解决这个目标，在本提案中，我们首先提出 开发一种新型的无金标准方法来使用患者数据量化基于AI的QI工具的不确定性。 现有的不确定性量化技术主要是用于检测任务的，通常是 需要使用黄金标准。相反，将开发提出的技术来量化任务 并且不需要任何黄金标准定量值。接下来，要结合基于AI的Qi工具的不确定性， 我们建议提出一份调查表，该问卷将使患者的风险价值概况引起 治疗。例如，如果基于AI的Qi工具输出的定量值表示侵略性治疗，则 但是，由于不确定性高，有些患者可能会冒着风险，并且宁愿为不确定性分配高体重 价值，而其他患者可能会重视治疗的益处，从而为这种不确定性分配较小的体重。 为了纳入这些患者的偏好，我们建议开发一份问卷，以引起患者的风险 - 价值配置文件。该项目将推进我们目前的R01奖的持续活动， 评估Qi方法，在不确定性量化的背景下扩展该项目，从而使 使用我们的工具不仅用于评估，还可以为每个患者产生个性化建议。 将在指导治疗反应的高度重要临床问题的背景下开发方法 III期非小细胞肺癌（NSCLC）患者。回答这个问题将有助于解决关键 紧急且未满足的策略是个性化NSCLC治疗的策略，NSCLC是一种发病率高的疾病 死亡率。一个高度多学科的团队，包括成像科学家，具有AI，AI伦理学家的专业知识， 肿瘤科医生和核药物物理学已经为这项研究组装。这种补充是直接的 在开发AI道德临床使用框架方面，对NOT-OD-22-065的反应。该项目将 同样强大的工具在父母R01中开发的工具的影响通过使用它们来指导临床决定 制作。与患者倡导团体和行业合作伙伴的合作也将增强影响力。 总体而言，该项目被中毒以强烈影响QI在治疗NSCLC中的道德临床应用，因为 以及其他癌症的癌症和神经退行性疾病的作用。