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

项目摘要：定量成像(QI)，其中数字/统计特征是从患者计算出来的 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QI 在这一领域，工具显示出了巨大的希望。然而，从这些工具测得的量化值 也可能由于训练数据有限、地面事实不准确等各种原因而存在不确定性， 训练集和测试集不匹配。对于基于人工智能的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。这对于伦理道德来说是必要的 这些工具的应用，这也是我们对患者进行的调查得出的结论 倡导者(Birch等人，《自然医学2022》)。为了解决这一目标，在本提案中，我们首先提出 开发一种新的非金标准方法来量化基于人工智能的QI工具的不确定性，使用患者数据。 现有的不确定性量化技术主要是为检测任务开发的，并且通常 要求提供黄金标准。相反，提议的技术将被开发用于量化任务 并且不需要任何金本位的量化值。接下来，为了融入基于人工智能的QI工具的不确定性， 我们建议开发一份调查问卷，以得出患者的风险-价值概况 治疗。例如，如果基于人工智能的QI工具输出指示积极治疗的量化值， 但由于不确定性很高，一些患者可能会厌恶风险，并倾向于将不确定性赋予更高的权重 而其他患者可能会重视治疗的益处，因此对这种不确定性的权重较小。 为了纳入这些患者的偏好，我们建议开发一份问卷，以了解患者的风险- 价值配置文件。该项目将推进我们目前正在进行的非金标R01奖的活动 评价质量改进方法，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可以进行评估，还可以为每个患者生成个性化的建议。这个 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。回答这个问题将有助于解决一个关键的， 对非小细胞肺癌个性化治疗战略的迫切和未得到满足的需求，非小细胞肺癌是一种高发病率和高发病率的疾病 死亡率。一支高度多学科的团队，由拥有人工智能专业知识的成像科学家、人工智能伦理学家、 肿瘤学家和核医学内科医生已经为这项研究召集了起来。这份增刊是直接 响应NOT-OD-22-065，制定人工智能临床合乎道德的使用框架。该项目将 还通过使用我们正在母公司R01中开发的工具来指导临床决策，从而加强这些工具的影响 制作。通过与患者倡导团体和行业合作伙伴的合作，也将加强影响。 总体而言，该项目将有力地影响QI在非小细胞肺癌(AS)治疗中的伦理临床应用。 以及其他癌症、心脏和神经退行性疾病，QI在这些疾病中都有作用。

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.