SCH: Intelligent Radiology Through Human-Machine Cooperation

SCH：通过人机协作实现智能放射学

• 批准号: 2205152
• 负责人: Ali Adibi
• 金额: $ 80万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205152&HistoricalAwards=false
• 关键词: SCH; Intelligent; Radiology; Through; Human

With unprecedented progress in the development of advanced imaging tools for medicine, accurate interpretation of medical images has become essential in diagnosis and treatment of several diseases in a wide range of medical disciplines. Nevertheless, studies show large variability between interpretations of different radiologists, especially using newer developed quantitative scales. Recent computational and algorithmic advances in Artificial Intelligence (AI) promise effective tools to learn the relationship between medical images and clinical data to diagnose diseases and predict progression and outcomes based on the past history of each patient. However, the AI tools for medical analysis are usually developed based on radiologist interpretations, not considering the between-person variations. Vice versa, there is very limited data on how AI can affect radiologists’ readings and offer additional value to their daily workflow. This project is focused on the creation of a trustworthy reference standard for AI using the interpretation of multiple radiologists while investigating how AI can reduce the variability in interpretation and improve the clinical workflow to optimally benefit patient care. It will considerably reduce the variations among radiologist readings by proving the AI assessment while improving the AI algorithms using radiologists’ reading strategy. It will be helpful in training radiologists as well as mitigating the adverse effects of physical, psychological, and environmental conditions (e.g., noise, fatigue) on the radiologists’ readings of medical images. The overarching goal of this project is to develop a novel paradigm based on human-AI cooperation in intelligent computational analysis of medical imaging data by using state-of-the-art AI algorithms to develop an accurate labeling tool for medical images to maximize the accuracy and minimize the inter- and intra-reader variability. The tool also provides the decision-making rationale in the AI algorithm through a series of filtered images to help radiologists in their interpretations. In parallel, an eye-tracking system is used to learn the decision-making patterns of expert radiologists and trainees with and without knowing the AI assessment. This knowledge is fed back to the AI algorithm to improve its performance. This will result in the most reliable labeling tool with superior performance compared to conventional AI tools or individual radiologists. Using this platform, an AI tool is developed for combining the medical imaging data with all relevant clinical, social, and demographic data for accurate diagnosis and prediction of the course of a disease with and without treatment for each patient.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.

随着医学先进成像工具的发展取得前所未有的进步，医学图像的准确解释在广泛的医学学科中的几种疾病的诊断和治疗中变得至关重要。然而，研究表明，不同的放射科医生的解释之间存在很大的差异，特别是使用新开发的定量尺度。人工智能（AI）的最新计算和算法进展有望提供有效的工具来学习医学图像和临床数据之间的关系，以根据每个患者的既往病史诊断疾病并预测进展和结果。然而，用于医学分析的人工智能工具通常是基于放射科医生的解释开发的，而不考虑人与人之间的差异。反之亦然，关于人工智能如何影响放射科医生的读数并为其日常工作流程提供额外价值的数据非常有限。该项目的重点是使用多名放射科医生的解释为AI创建一个值得信赖的参考标准，同时研究AI如何减少解释的可变性并改善临床工作流程，以最佳地使患者护理受益。它将大大减少放射科医生的读数之间的变化，通过证明人工智能评估，同时改善人工智能算法使用放射科医生的阅读策略。它将有助于培训放射科医生以及减轻身体、心理和环境条件（例如，噪声、疲劳）对放射科医师的医学图像读数的影响。该项目的总体目标是开发一种基于人类-人工智能合作的新型范例，通过使用最先进的人工智能算法对医学成像数据进行智能计算分析，为医学图像开发准确的标记工具，以最大限度地提高准确性并最大限度地减少阅读者之间和阅读者内部的差异。该工具还通过一系列过滤图像提供AI算法中的决策依据，以帮助放射科医生进行解释。与此同时，眼动跟踪系统用于学习放射科专家和学员的决策模式，无论是否知道AI评估。这些知识被反馈给AI算法以提高其性能。这将产生最可靠的标记工具，与传统AI工具或个体放射科医生相比，具有上级性能。利用该平台开发的AI工具，将医学影像数据与所有相关的临床、社会和人口统计数据相结合，对每个患者进行准确的诊断和预测，无论是否接受治疗。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。