Deep Learning and Subtyping of Post-COVID-19 Lung Progression Phenotypes

COVID-19 后肺部进展表型的深度学习和亚型分析

• 批准号: 10634998
• 负责人: CHING-LONG LIN
• 金额: $ 75.67万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10634998
• 关键词: Air; Alveolus; Biological Markers; COVID-19; COVID-19 patient; COVID-19 survivors; Characteristics; Chronic Obstructive Pulmonary Disease; Classification; Clinic; Clinical; Clinical Data; Clinical assessments; Data; Data Analyses; Dedications; Development; Dimensions; Disease; Exhibits; Exposure to; Goals; Health; Healthcare; Image; Iowa; Knowledge; Learning; Liquid substance; Long-Term Effects; Lung; Modeling; Outcome; Patient Care; Patients; Phenotype; Public Health; Pulmonary Emphysema; Radiation Dose Unit; Recommendation; Research; Resolution; SARS-CoV-2 infection; Site; Structure of parenchyma of lung; Study Subject; Techniques; Testing; Thoracic Radiography; Time; Training; Universities; Validation; Virus Diseases; Visit; X-Ray Computed Tomography; X-Ray Medical Imaging; clinical biomarkers; cohort; cost; deep learning; deep learning algorithm; deep learning model; experience; fibrotic lung; follow-up; human subject; imaging biomarker; improved; in silico; in vivo; learning algorithm; learning strategy; long-term sequelae; lung imaging; novel coronavirus; particle; phenotypic biomarker; post-COVID-19; prototype; pulmonary function; small airways disease; supervised learning; transfer learning

PROJECT SUMMARY Patients who recover from the novel coronavirus disease 2019 (COVID-19) may experience a range of long- term health consequences. Since the lung is the primary site of viral infection, pulmonary sequelae may present persistently in COVID-19 survivors. Thus, clinical assessment of COVID-19 survivors in conjunction with chest X-ray (CXR) and computed tomography (CT) is recommended. CXR is more accessible, whereas CT provides more detailed information. Our long-term goal is to develop an integrated deep learning model that can assess lung images to assist with the management and treatment of long-term sequelae of post-COVID-19 subjects. The primary objective of the proposed research is to advance contrastive self-supervised learning models that take advantage of the accessibility of CXR scanners and the accuracy of CT images, identify the subtypes in patients with post-COVID-19, and characterize clinical, imaging and mechanistic biomarkers within subtypes. Our central hypothesis is that post-COVID-19 subtypes exist and they are characterized by distinct progression phenotypes. To test this hypothesis and achieve the primary objective, we will perform the following four specific aims. In Aim 1, we will advance contrastive learning methods to handle large-scale images with low training costs, and fine-tune the classifier and the encoder network on large-scale CXR images to detect post-COVID- 19 subjects. In Aim 2, we will advance contrastive learning methods that learn from CT images acquired at different volumes and different times to differentiate post-COVID-19 subjects from other cohorts and identify subtypes. In Aim 3, we will apply computational fluid and particle dynamics techniques to derive mechanistic biomarkers to explain the associations between clinical and imaging biomarkers in post-COVID-19 subtypes. In Aim 4, we will conduct a human subject study that examines post-COVID-19 subjects at 36-48 months after initial follow-up visits to assess the progression features of their clinical and imaging biomarkers. In summary, we will advance contrastive self-supervised learning algorithms based on CXR and CT images, respectively, for accessibility (Aim 1) and accuracy (Aim 2). We will generate in silico data for feature interpretability (Aim 3) and gather in vivo data for model training and validation (Aim 4). The pre-trained model from Aim 2 will be fine-tuned via transfer learning to input CXR images that are classified as post-COVID-19 by the model from Aim 1. An integrated deep learning model based on the two models from Aim 1 and 2 will take CXR images as inputs to provide CT-based detailed phenotypic information together with mechanistically and clinically meaningful interpretation. If successful, our study will not only advance contrastive learning algorithms, but also elucidate the pulmonary sequelae of post-COVID-19 patients in subtypes and associated clinical, imaging and mechanistic biomarkers. The ability to identify progression subtypes and associated phenotypic biomarkers will have a positive impact on the management and treatment of patients with post-COVID-19.

项目摘要 从2019年新型冠状病毒病（COVID-19）中恢复过的患者可能会经历一系列长期的 任期健康后果。由于肺是病毒感染的主要部位，因此可能存在肺后遗症 持续在Covid-19幸存者中。因此，COVID-19幸存者与胸部结合的临床评估 建议使用X射线（CXR）和计算机断层扫描（CT）。 CXR更容易访问，而CT提供 更详细的信息。我们的长期目标是开发一个可以评估的综合深度学习模型 肺部图像有助于管理和治疗长期后续后期19受试者的后遗症。 拟议研究的主要目的是提高对比的自我观察的学习模型 利用CXR扫描仪的可访问性和CT图像的准确性，确定子类型 Covid-19的患者以及亚型内的临床，成像和机械生物标志物的表征。 我们的中心假设是，Covid-19的亚型存在，它们的特征是不同的进展 表型。为了检验这一假设并实现主要目标，我们将执行以下四个特定 目标。在AIM 1中，我们将推进对比性学习方法，以处理低训练的大规模图像 成本，并在大规模CXR图像上微调分类器和编码器网络，以检测后旋转后 - 19个主题。在AIM 2中，我们将推进从获得的CT图像中学习的对比度学习方法 不同的体积和不同的时间，以区分Covid-19的受试者与其他同类的受试者并确定 亚型。在AIM 3中，我们将应用计算流体和粒子动力学技术来得出机械 生物标志物可以解释Covid-19-19子类型中临床和成像生物标志物之间的关联。在 AIM 4，我们将进行一项人类主题研究，该研究在36-48个月后检查Covid-19的受试者 最初的随访访问以评估其临床和成像生物标志物的进展特征。总之， 我们将分别基于CXR和CT图像来推进对比度的自我监督学习算法 可访问性（AIM 1）和准确性（AIM 2）。我们将在计算机数据中生成特征可解释性（AIM 3）和 收集体内数据进行模型训练和验证（AIM 4）。 AIM 2的预训练模型将进行微调 通过转移学习到输入CXR图像，该图像由AIM 1中的模型分类为COVID-19。 基于AIM 1和2的两个模型的集成深度学习模型将以CXR图像作为输入 提供基于CT的详细表型信息以及机械和临床上有意义的 解释。如果成功，我们的研究不仅会推进对比度学习算法，而且还将阐明 亚型和相关的临床，成像和机械性的肺后19例患者的肺后遗症 生物标志物。鉴定进展亚型和相关表型生物标志物的能力将具有 对199年后患者的管理和治疗的积极影响。