SCH: AI-DOCTOR COLLABORATIVE MEDICAL DIAGNOSIS

SCH：AI-医生协同医疗诊断

• 批准号: 10688087
• 负责人: Hien Van Nguyen
• 金额: $ 21.87万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-22 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10688087
• 关键词: Address; Algorithms; Artificial Intelligence; Cardiovascular system; Cessation of life; Classification; Clinical; Cognitive; Collaborations; Computer software; Computer-Assisted Diagnosis; Creativeness; Detection; Diagnosis; Diagnostic; Diagnostic Errors; Diagnostic radiologic examination; Error Sources; Evaluation; Expert Systems; Fatigue; Goals; Hospitals; Human; Institution; Instruction; Intention; Joints; Knowledge; Learning; Lung nodule; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Medical; Methodology; Methods; Mission; Modeling; Monitor; Outcome; Pathologic; Performance; Principal Investigator; Process; Psychological reinforcement; Public Health; Pulmonary Embolism; Radiology Specialty; Research Design; Retrospective Studies; Reverse engineering; Scanning; System; Systems Integration; Thinking; Thoracic Radiography; Time; User-Computer Interface; Visual; Work; Workload; artificial intelligence algorithm; cancer diagnosis; computer framework; design; diagnostic accuracy; distraction; gaze; imaging modality; improved; innovation; iterative design; multidisciplinary; multimodality; neglect; next generation; novel; radiologist; success; theories; usability; user-friendly; visual information

Recent retrospective studies show that radiology's diagnostic error rates did not decrease significantly over the years. For example, missed lung cancer rates remain at 20-60% on chest radiography dependent on study design. This error contributes to 40,000-80,000 deaths annually in U.S. hospitals. This project aims to develop a computational framework for Al to collaborate with human radiologists on medical diagnosis tasks. To achieve this goal, we divide the project into three Aims, where the first two focus on fundamental theories, and the last one evaluates the proposed approaches on targeted applications. Aim 1: Develop computational principles for optimal Al-radiologist interaction. This Aim will develop a computational framework for guiding the interaction between radiologists and Al to achieve the best possible diagnostic performance while minimizing the time burden. Our framework consists of the first method for reverse-engineering radiologists' intention from the joint gaze and visual information based on reinforcement learning. This Aim is the first to provide an integrated system with gaze sensing, deep networks, and human radiologists. The knowledge from this Aim will fundamentally transform how one would build collaborative medical diagnosis systems. Aim 2: Design a user-friendly and minimally-interfering interface for radiologist-Al interaction. This Aim addresses an essential question of designing a minimally interfering interface that allows human radiologists to interact with Al models efficiently. Our proposed system combines an innovative "multimodal thinking with audio and gaze" (MTAG) methodology with user-centered iterative design. The process will result in a novel radiologist-Al collaborative interface that maximizes time efficiency while minimizing the amount of distraction. The outcome of this Aim will shed light on design principles for systems involving radiologists. Aim 3: Evaluation Plan. This Aim evaluates the proposed approaches in Aim 1-2 on two clinically important applications: i) Lung nodule detection and ii) pulmonary embolism. Lung cancer is the second most common cancer, and pulmonary embolism is the third most common cause of cardiovascular death. Studying how radiologists collaborate with Al to reduce diagnostic errors will lead to significant clinical impacts. RELEVANCE (See instructions): Diagnostic errors contribute to 40,000-80,000 deaths annually in U.S. hospitals. This project combines novel artificial intelligence (Al) algorithms, gaze monitoring software, and design principles to help doctors minimize diagnostic errors due to cognitive and perceptual biases. The project's success will fundamentally change how we design Al medical systems to increase diagnostic accuracy, save lives, reduce missed cancer diagnoses, improve public health, and advance NCl's mission.

最近的回顾性研究表明，放射学的诊断错误率并未显着降低 年。例如，在胸部X线照相术上遗漏的肺癌率保持在20-60％ 研究设计。该错误每年在美国医院每年造成40,000-80,000人死亡。这个项目的目标 为了开发一个计算框架，供AL与人类放射科医生合作就医学诊断 任务。为了实现这一目标，我们将项目划分为三个目标，前两个目标关注基本 理论，最后一个评估了目标应用程序所提出的方法。 目标1：开发最佳AL-放射科医生相互作用的计算原理。这个目标将发展 一个计算框架，用于指导放射科医生与AL之间的相互作用，以达到最佳 可能的诊断性能，同时减少时间负担。我们的框架包括第一个 基于关节目光和视觉信息的反向工程放射科医生反向工程的方法 强化学习。这个目标是第一个提供一个具有凝视感的集成系统的目标 网络和人类放射科医生。这个目标的知识将从根本上改变一个人 将建立协作医学诊断系统。 AIM 2：设计一个用户友好且微小的界面接口，以进行放射科医生 - 互动。这 目的解决了设计最小干扰界面的基本问题，该界面允许人类 放射学家有效与Al模型相互作用。我们提出的系统结合了创新的“多模式” 用音频和凝视思考”（MTAG）方法，并以用户为中心的迭代设计。该过程将 导致一种新型的放射科医生-AL协作界面，可最大程度地提高时间效率 分心的程度。此目标的结果将阐明涉及的系统的设计原理 放射科医生。 目标3：评估计划。这个目的评估了AIM 1-2对两个临床的AIM 1-2的拟议方法 重要应用：i）肺结节检测和ii）肺栓塞。肺癌是第二个 最常见的癌症和肺栓塞是心血管死亡的第三大最常见原因。 研究放射科医生如何与AL合作以减少诊断错误将导致重大临床 影响。 相关性（请参阅说明）： 诊断错误每年在美国医院每年造成40,000-80,000人死亡。这个项目结合了 新颖的人工智能（AL）算法，凝视监控软件和设计原理，以帮助医生 最小化由于认知和感知偏见而导致的诊断错误。该项目的成功将从根本上 更改我们设计医疗系统以提高诊断准确性，挽救生命，减少错过的方式 癌症诊断，改善公共卫生并提高NCL的任务。