Radiologist-Centered Artificial Intelligence (RCAI) for Lung Cancer Screening and Diagnosis

以放射科医生为中心的人工智能（RCAI）用于肺癌筛查和诊断

• 批准号: 10640048
• 负责人: Ulas Bagci
• 金额: $ 44.75万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640048
• 关键词: Address; Adopted; Artificial Intelligence; Benign; Cancer Etiology; Cessation of life; Classification; Clinical; Cognitive; Collaborations; Communication; Computer-Assisted Diagnosis; Cues; Data; Detection; Devices; Diagnosis; Diagnostic; Diagnostic Procedure; Event; Feedback; Glass; Goals; Human; Image; Intuition; Label; Learning; Learning Module; Length; Location; Lung Neoplasms; Lung nodule; Mainstreaming; Malignant - descriptor; Malignant neoplasm of lung; Measurement; Measures; Methodology; Modeling; Nodule; Outcomes Research; Pattern; Pennsylvania; Performance; Phase; Probability; Process; Radiology Specialty; Reading; Reproducibility; Research; Scanning; Screening for cancer; Screening procedure; Second Opinions; Shapes; System; Techniques; Technology; Testing; Texture; Three-Dimensional Image; Time; Training; United States; United States National Institutes of Health; Universities; Update; Visual attention; Visualization; Woman; Work; X-Ray Computed Tomography; artificial intelligence algorithm; attenuation; calcification; career; clinical decision-making; clinical diagnostics; computer human interaction; cone-beam computed tomography; deep learning; deep learning algorithm; deep reinforcement learning; design; diagnostic accuracy; educational atmosphere; efficacy evaluation; efficacy validation; experience; experimental study; flexibility; gaze; improved; inattention; insight; learning algorithm; learning network; low dose computed tomography; lung cancer screening; men; mortality; novel; radiologist; sample fixation; satisfaction; screening; statistics; synergism; technological innovation; tool; tumor; virtual; visual search; visual tracking

PROJECT SUMMARY Lung cancer is the most common cause of cancer death in both men and women in the United States. Lung cancer screening with low-dose computed tomography (CT) has been shown to reduce lung cancer mortality. However, current radiology practice still suffers from (1) high rates of missed tumors and (2) imprecise lung nodule characterization (malignant vs. benign). Artificial intelligence (AI) based computer aided diagnosis (CAD) systems have helped radiologists to reduce the missed-tumor rates moderately, but have not been widely adopted for three key reasons: lack of efficiency, lack of real-time collaboration, and lack of interpretability. The overall goal of this proposal is to create radiologist-centered artificial intelligence algorithms that are both interpretable and collaborative and to demonstrate their improved efficacy via lung cancer screening experiments. The central hypothesis of this effort is that the creation of an AI based virtual cognitive assistant (VCA) will provide a better understanding of cognitive biases while offering interpretable feedback to radiologists for an improved screening experience with higher diagnostic accuracy, reproducibility, and efficiency. Specific aims of the proposal are three-fold. Aim 1: To develop an eye-tracking platform that offers a realistic radiology reading room experience while extracting gaze patterns from radiologists. This will facilitate addressing the problem of true collaboration between radiologists and CAD. Radiologists will perform their screening without any constraints (e.g., wearing glasses) while their gaze patterns and other human-computer interaction events are tracked, processed, and stored in real time. Aim 2: To develop an automated real-time collaborative system involving a developed VCA and the radiologist to synergistically improve detection and diagnostic performances. Using deep learning (DL) algorithms, the VCA will embody a powerful visual attention model to represent radiologists’ gaze, visual search, and fixation patterns, and will be composed of a detection component and a diagnostic component. A deep reinforcement learning algorithm will enable communication between the VCA and the radiologist. Lastly, a DL-based segmentation component will, on the fly, enable the VCA to derive and visualize quantitative measures (HU statistics, volume, long/short axes lengths, etc.) and overlay them along with the tumor classification label (benign/malignant) and its probability in real time. Aim 3: To evaluate the efficacy of the proposed VCA via lung cancer screening experiments involving six radiologists from two institutes (University of Pennsylvania and NIH) at different expertise levels. The proposed VCA is a first-of-a-kind-system to exploit the synergy between powerful DL technology and experts (humans) to attempt boost clinical diagnostic performance of radiologists, unlike passive DL techniques that learn from labeled data. The outcome of this research are expected to be transformative by providing deep insights for re-designing current CAD systems to truly collaborate with radiologists, instead of acting as second opinion tools for them or replacing them, and by ultimately further reducing lung cancer-related deaths.

项目概要 肺癌是美国男性和女性癌症死亡的最常见原因。 低剂量计算机断层扫描 (CT) 癌症筛查已被证明可以降低肺癌死亡率。 然而，当前的放射学实践仍然存在以下问题：(1) 肿瘤漏检率高；(2) 肺部不精确 基于人工智能 (AI) 的计算机辅助诊断 (CAD) 结节特征（恶性与良性）。 系统已帮助放射科医生适度降低肿瘤漏诊率，但尚未广泛推广 采用这种方式的主要原因有三个：缺乏效率、缺乏实时协作以及缺乏可解释性。 该提案的总体目标是创建以放射科医生为中心的人工智能算法 可解释和协作，并通过肺癌筛查证明其疗效的改善 这项工作的中心假设是创建一个基于人工智能的虚拟认知助手。 (VCA) 将更好地理解认知偏差，同时向放射科医生提供可解释的反馈 改善筛查体验，提高诊断准确性、可重复性和效率。 该提案的具体目标有三个：目标 1：开发一个提供眼动追踪功能的平台。 真实的放射学阅览室体验，同时提取放射科医生的注视模式，这将有利于。 解决放射科医生和 CAD 之间的真正合作问题将发挥作用。 不受任何限制（例如戴眼镜）的筛选，而他们的注视模式和其他人机 实时跟踪、处理和存储交互事件 目标 2：开发自动化的实时系统。 涉及开发的 VCA 和放射科医生的协作系统，以协同改进检测和 使用深度学习 (DL) 算法，VCA 将体现出强大的视觉注意力。 模型来表示放射科医生的注视、视觉搜索和注视模式，并将由检测组成 深度强化学习算法将实现通信。 最后，基于深度学习的分割组件将在运行中启用 VCA 导出并定量可视化测量值（HU 统计数据、体积、长/短轴长度等）和 将它们与肿瘤分类标签（良性/恶性）及其概率实时叠加。目标 3： 通过六名放射科医生参与的肺癌筛查实验来评估拟议 VCA 的功效 来自两个不同专业水平的机构（宾夕法尼亚大学和美国国立卫生研究院）。 所提出的 VCA 是第一个利用强大的深度学习技术和 与被动深度学习技术不同，专家（人类）试图提高放射科医生的临床诊断性能 从标记数据中学习，这项研究的结果预计将通过提供深入的内容而具有变革性。 重新设计当前 CAD 系统以真正与放射科医生合作，而不是充当第二个的见解 为他们提供意见工具或取代他们，并最终进一步减少与肺癌相关的死亡。

项目成果

The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs.

CO-RADS 评分系统在诊断 COVID-19 感染中的作用及其与临床体征的相关性。

• 发表时间: 2022
• 影响因子: 1.4
• 作者: Çomoğlu, Şenol;Öztürk, Sinan;Topçu, Ahmet;Kulalı, Fatma;Kant, Aydın;Sobay, Resul;Arslan, Mustafa;Ülgür, Hanife Şeyda;Kostakoğlu, Uğur;Küçük, Eyüp Veli;Karakoç, Hanife Nur;Çağlar, Merve;Uzuğ, Gülsüm;Bağcı, Ulaş;Özkan, Ömer Faruk;Yılmaz, Gürd
• 通讯作者: Yılmaz, Gürd

Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.

基于机器学习的预测，通过简化的深部脑刺激引线模型，预测患者组织中 MRI 引起的功率吸收。

• 发表时间: 2021-10
• 影响因子: 2.1
• 作者: Vu, Jasmine;Nguyen, Bach T;Bhusal, Bhumi;Baraboo, Justin;Rosenow, Joshua;Bagci, Ulas;Bright, Molly G;Golestanirad, Laleh
• 通讯作者: Golestanirad, Laleh

Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network.

使用聚焦调制引导卷积神经网络进行视频胶囊内窥镜分类。

• 发表时间: 2022-07
• 作者: Srivastava, Abhishek;Tomar, Nikhil Kumar;Bagci, Ulas;Jha, Debesh
• 通讯作者: Jha, Debesh

Quality assurance of computer-aided detection and diagnosis in colonoscopy.

结肠镜检查计算机辅助检测和诊断的质量保证。

• 发表时间: 2019-07
• 影响因子: 7.7
• 作者: Vinsard, Daniela Guerrero;Mori, Yuichi;Misawa, Masashi;Kudo, Shin;Rastogi, Amit;Bagci, Ulas;Rex, Douglas K;Wallace, Michael B
• 通讯作者: Wallace, Michael B

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images.

用于 MRI 图像中导管内乳头状粘膜肿瘤 (IPMN) 分类的神经变压器。

• 发表时间: 2022-07
• 作者: Salanitri, F Proietto;Bellitto, G;Palazzo, S;Irmakci, I;Wallace, M;Bolan, C;Engels, M;Hoogenboom, S;Aldinucci, M;Bagci, U;Giordano, D;Spampinato, C
• 通讯作者: Spampinato, C