SCH: Intelligent Radiology Through Human-Machine Cooperation

随着先进的医学成像工具的发展取得了前所未有的进步，医学图像的准确解释在广泛的医学学科中对几种疾病的诊断和治疗至关重要。然而，研究表明，不同放射科医生的解释之间存在很大差异，特别是使用较新开发的定量量表。人工智能（AI）的最新计算和算法进展有望成为学习医学图像和临床数据之间关系的有效工具，从而根据每位患者的过去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而开发的，而没有考虑到人与人之间的差异。反之亦然，关于人工智能如何影响放射科医生的读数并为他们的日常工作提供额外价值的数据非常有限。该项目的重点是利用多名放射科医生的解释，为人工智能创建一个值得信赖的参考标准，同时研究人工智能如何减少解释中的可变性，并改善临床工作流程，以最佳地造福患者护理。通过证明人工智能评估，它将大大减少放射科医生读数之间的差异，同时使用放射科医生的阅读策略改进人工智能算法。这将有助于培训放射科医生，并减轻身体、心理和环境条件（例如噪音、疲劳）对放射科医生阅读医学图像的不利影响。该项目的总体目标是通过使用最先进的人工智能算法开发一种准确的医学图像标记工具，以最大限度地提高准确性并最大限度地减少阅读器之间和阅读器内部的差异，在医学成像数据的智能计算分析中开发一种基于人类-人工智能合作的新范式。该工具还通过一系列过滤后的图像为人工智能算法提供决策依据，以帮助放射科医生进行解释。与此同时，一个眼球追踪系统被用来学习专家放射科医生和实习生在了解和不了解人工智能评估的情况下的决策模式。这些知识被反馈给人工智能算法，以提高其性能。这将产生最可靠的标记工具，与传统的人工智能工具或单个放射科医生相比，其性能优越。利用该平台，开发了一种人工智能工具，将医学成像数据与所有相关的临床、社会和人口统计数据相结合，以准确诊断和预测每个患者的病程，无论是否接受治疗。该奖项反映了美国国家科学基金会的法定使命，并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。

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.