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

基本信息

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
项目类别：
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

项目摘要

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)，其中计算患者的数值/统计特征图像，正在成为基于人工智能 (AI) 的 QI 诊断和治疗规划的重要工具。工具在这一领域显示出巨大的前景，但是，这些工具所测量的定量价值。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、不准确的地面事实、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，这对于伦理是必要的。这些工具的应用，这一推论也是从我们对患者进行的一项调查中得出的倡导者（Birch 等人，Nature Medicine 2022）为了实现这一目标，在本提案中，我们首先提出。开发一种新颖的非金标准方法，使用患者数据来量化基于人工智能的 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，并且通常相比之下，所提出的技术将针对量化任务而开发。并且不需要任何黄金标准定量值接下来，考虑到基于人工智能的 QI 工具的不确定性，我们建议制定一份调查问卷，以了解患者的风险价值概况例如，如果基于人工智能的 QI 工具输出表明积极治疗的定量值，但由于不确定性较高，一些患者可能会规避风险，更愿意对不确定性赋予较高的权重值，而其他患者可能会重视治疗的益处，因此对不确定性的权重较小。为了纳入这些患者的偏好，我们建议开发一份调查问卷，以了解患者的风险- 该项目将推进我们当前关于非金标准的 R01 奖项的持续活动。 QI 方法的评估，在不确定性量化的背景下扩展该项目，从而使使用我们的工具不仅可以进行评估，还可以为每位患者提供个性化的建议。将在指导治疗反应的高度重要的临床问题的背景下开发方法回答这个问题将有助于解决 III 期非小细胞肺癌 (NSCLC) 患者的一个关键问题。对非小细胞肺癌（NSCLC）这种发病率高且发病率高的疾病的个体化治疗策略的迫切且未得到满足的需求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、本研究直接召集了肿瘤学家和核医学医师。该项目将响应 NOT-OD-22-065 制定人工智能临床伦理使用框架。还加强了我们在母体 R01 中开发的工具的影响，使用它们来指导临床决策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，该项目将对 QI 治疗 NSCLC 的伦理临床应用产生重大影响，因为以及其他癌症、心脏病和神经退行性疾病，QI 都发挥着作用。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Projection-Domain Low-Count Quantitative SPECT Method for α-Particle-Emitting Radiopharmaceutical Therapy.

用于 α 粒子发射放射性药物治疗的投影域低计数定量 SPECT 方法。

DOI：
发表时间：
2023-01
期刊：
影响因子：
0
作者：
Li, Zekun;Benabdallah, Nadia;Abou, Diane S;Baumann, Brian C;Dehdashti, Farrokh;Ballard, David H;Liu, Jonathan;Jammalamadaka, Uday;Laforest, Richard;Wahl, Richard L;Thorek, Daniel L J;Jha, Abhinav K
通讯作者：
Jha, Abhinav K

An estimation-based method to segment PET images

一种基于估计的 PET 图像分割方法

DOI：
发表时间：
2020-02-29
期刊：
ArXiv
影响因子：
0
作者：
Ziping Liu;R. Laforest;J. Mhlanga;Hae Sol Moon;Tyler J Fraum;M. Itani;A. Mintz;F. Dehdashti;B. Siegel;Abhinav K. Jha
通讯作者：
Abhinav K. Jha

Clinical decisions using AI must consider patient values.

使用人工智能的临床决策必须考虑患者的价值。