RAPID: Explainable Machine Learning for Analysis of COVID-19 Chest CT

RAPID：用于分析 COVID-19 胸部 CT 的可解释机器学习

• 批准号: 2026809
• 负责人: Michael Pazzani
• 金额: $ 10.16万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026809&HistoricalAwards=false
• 关键词: RAPID; Explainable; Machine; Learning; Analysis

In December 2019, it was discovered that a widely contagious pneumonia was caused by a new coronavirus infection now named COVID-19. The primary test for detection of the virus is real-time polymerase chain reaction (RT-PCR) with sensitivity of approximately 71% in some studies. However, this test may require several days to provide a result. Perhaps more importantly, imaging with x-ray or computed tomography (CT) are required to confirm pneumonia, which is the principal cause of death, as it leads to acute respiratory distress syndrome (ARDS). Recent studies have shown sensitivity of chest CT for approximately 98% for COVID-19 pneumonia and could provide immediate results but currently require human interpretation. Given the need for rapid, more accurate diagnosis, this project will use, adapt, and evaluate explainable machine learning techniques to diagnosis of COVID-19 pneumonia. This project will improve the understanding of mechanisms of COVID-19 and will help mitigate its impacts.Viral nucleic acid detection using real-time polymerase chain reaction (RT-PCR) is the primary method for diagnosis of COVID-19 infection, which has rapidly spread worldwide as a global pandemic. Sensitivity of this test for COVID-19 infection has been estimated at approximately 71% in some studies and may require several days for a result. X-ray and CT imaging are complementary technologies that allow diagnosis of COVID-19 pneumonia, which can evolve to acute respiratory distress syndrome (ARDS) -- the principal cause of death in patients with COVID-19 infection. Especially early in the course of the disease, chest CT has multiple advantages over RT-PCR yielding results more quickly and is already widely deployed, but requires expert radiologist interpretation. The number of chest CTs may rapidly exceed the speed and capacity of already strained radiologists. An explainable machine learning algorithm may address this disadvantage to expedite the interpretation of chest CT and assist rapid triage of patients to the ICU, inpatient ward, monitoring unit, or home self-quarantine. Machine learning algorithms, specifically those leveraging deep convolutional neural networks (deep learning), have the potential for facilitating even more rapid diagnosis within minutes. This project seeks to validate the use of explainable deep learning methods to adjust diagnostic operating points for multiple applications, including (a) disease screening, (b) disease staging and prognostication, and (c) evaluation of treatment response.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.

2019年12月，人们发现一种广泛传染性的肺炎是由一种现在被命名为COVID-19的新型冠状病毒感染引起的。检测该病毒的主要方法是实时聚合酶链反应（RT-PCR），在一些研究中灵敏度约为71%。但是，该测试可能需要几天才能提供结果。也许更重要的是，需要x射线或计算机断层扫描（CT）成像来确认肺炎，这是死亡的主要原因，因为它会导致急性呼吸窘迫综合征（ARDS）。最近的研究表明，胸部CT对COVID-19肺炎的敏感性约为98%，可以立即提供结果，但目前需要人工解释。鉴于对快速、更准确诊断的需求，该项目将使用、调整和评估可解释的机器学习技术来诊断COVID-19肺炎。该项目将增进对COVID-19机制的理解，并有助于减轻其影响。实时聚合酶链反应（RT-PCR）病毒核酸检测是新型冠状病毒感染诊断的主要方法，目前新型冠状病毒感染已成为全球大流行，在全球范围内迅速蔓延。在一些研究中，该测试对COVID-19感染的敏感性估计约为71%，可能需要几天才能得出结果。x射线和CT成像是可用于诊断COVID-19肺炎的互补技术，COVID-19肺炎可发展为急性呼吸窘迫综合征（ARDS），这是COVID-19感染患者的主要死亡原因。特别是在疾病的早期，胸部CT比RT-PCR有很多优势，可以更快地得出结果，并且已经被广泛应用，但需要放射科专家的解释。胸部ct的数量可能会迅速超过已经紧张的放射科医生的速度和能力。一种可解释的机器学习算法可以解决这一缺点，以加快胸部CT的解释，并帮助患者快速分类到ICU、住院病房、监护单元或家庭自我隔离。机器学习算法，特别是那些利用深度卷积神经网络（深度学习）的算法，有可能在几分钟内促进更快的诊断。该项目旨在验证可解释的深度学习方法的使用，以调整多种应用的诊断操作点，包括(a)疾病筛查，(b)疾病分期和预后，以及(c)治疗反应评估。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。

项目成果

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.

• DOI: 10.1002/emp2.12297
• 发表时间: 2020-12
• 期刊: Journal of the American College of Emergency Physicians open
• 影响因子: 2.3
• 作者: Carlile M;Hurt B;Hsiao A;Hogarth M;Longhurst CA;Dameff C
• 通讯作者: Dameff C

User-Centric Enhancements to Explainable AI Algorithms for Image Classification

以用户为中心的可解释人工智能图像分类算法的增强

• 发表时间: 2022
• 期刊: Proceedings of the Annual Conference of the Cognitive Science Society
• 作者: Severine Soltani, Robert Kaufman

Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers

肺水肿的深度学习放射学评估：优化临床表现，使用血清生物标志物进行训练

• DOI: 10.1109/access.2022.3172706
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Huynh, Justin;Masoudi, Samira;Noorbakhsh, Abraham;Mahmoodi, Amin;Kligerman, Seth;Yen, Andrew;Jacobs, Kathleen;Hahn, Lewis;Hasenstab, Kyle;Pazzani, Michael
• 通讯作者: Pazzani, Michael

Expert-Informed, User-Centric Explanations for Machine Learning

由专家提供信息、以用户为中心的机器学习解释

• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Michael Pazzani;Severine Soltani, Robert Kaufman
• 通讯作者: Severine Soltani, Robert Kaufman

Generating Explanations for Chest Medical Scan Pneumonia Predictions

生成胸部医学扫描肺炎预测的解释

• 发表时间: 2021
• 期刊: COVID Information Commons
• 作者: Samson Qian