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），其中从患者计算数值/统计特征 成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QI 工具在这一领域显示出巨大的前景。然而，从这些工具中测得的定量值 也可能由于各种原因而遭受不确定性，例如有限的训练数据，不准确的地面实况， 训练集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。这是必要的道德 这些工具的应用，这一推断也出现在我们对患者进行的一项调查中， 倡导者（Birch等人，Nature Medicine 2022）。为了实现这一目标，在本提案中，我们首先建议 开发一种新的非金标准方法，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针对检测任务而开发的，并且通常 需要金本位的可用性。相比之下，所提出的技术将被开发用于量化任务 而不需要任何金标准量化值。接下来，为了纳入基于AI的QI工具的不确定性， 我们建议制定一份问卷，以了解患者的风险价值概况， 治疗。例如，如果基于AI的QI工具输出指示积极治疗的定量值， 但在高度不确定性的情况下，一些患者可能不愿承担风险，并且更愿意为不确定性分配较高的权重 价值，而其他患者可能重视治疗的好处，因此对这种不确定性给予较少的权重。 为了将这些患者的偏好纳入其中，我们建议开发一份问卷，以了解患者的风险- 值配置文件。这个项目将推进我们目前的R 01奖的非金标准正在进行的活动 QI方法的评估，在不确定性量化的背景下扩展该项目，从而使 我们的工具不仅用于评估，而且还为每位患者提供个性化建议。的 方法将在指导治疗反应的高度重要的临床问题的背景下开发， III期非小细胞肺癌（NSCLC）患者。探讨这个问题将有助于解决一个关键的， 对NSCLC个性化治疗策略的迫切和未满足的需求，NSCLC是一种高发病率和 死亡率。一个高度多学科的团队，由具有人工智能专业知识的成像科学家，人工智能伦理学家， 肿瘤学家和核医学医生已经聚集在这项研究。该补充直接 根据NOT-OD-22-065，制定了AI临床伦理使用框架。该项目将 还通过使用我们在父R 01中开发的工具来指导临床决策，加强这些工具的影响 制作。还将通过与患者倡导团体和行业合作伙伴的合作来加强影响。 总的来说，该项目有望对QI治疗NSCLC的伦理临床应用产生强烈影响， 以及其他癌症、心脏病和神经退行性疾病，其中QI起作用。