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）中康复的患者可能会经历一系列长期- 长期健康后果。由于肺部是病毒感染的主要部位，可能会出现肺部后遗症 在COVID-19幸存者中持续存在。因此，COVID-19幸存者的临床评估与胸部 建议进行X线（CXR）和计算机断层扫描（CT）。CXR更容易获得，而CT提供 更多详细信息。我们的长期目标是开发一个集成的深度学习模型， 肺部图像，以协助管理和治疗COVID-19后受试者的长期后遗症。 该研究的主要目标是推进对比自监督学习模型， 利用CXR扫描仪的可及性和CT图像的准确性， COVID-19后患者，并表征亚型内的临床、成像和机制生物标志物。 我们的中心假设是，存在COVID-19后亚型，并且它们具有不同的进展特征 表型为了验证这一假设并实现主要目标，我们将执行以下四个具体的 目标。在目标1中，我们将推进对比学习方法，以处理低训练的大规模图像 成本，并对大规模CXR图像的分类器和编码器网络进行微调，以检测COVID后 19名受试者。在目标2中，我们将推进对比学习方法，从在以下条件下获取的CT图像中学习： 不同的体积和不同的时间，以区分COVID-19后受试者与其他队列， 亚型。在目标3中，我们将应用计算流体和粒子动力学技术来推导机械 生物标志物，以解释COVID-19后亚型的临床和成像生物标志物之间的关联。在 目标4，我们将进行一项人类受试者研究，检查COVID-19后36-48个月的受试者， 初始随访访视，以评估其临床和影像学生物标志物的进展特征。总的来说， 我们将分别基于CXR和CT图像推进对比自监督学习算法， 可及性（目标1）和准确性（目标2）。我们将生成用于特征可解释性的计算机数据（目标3）， 收集用于模型训练和验证的体内数据（目标4）。来自Aim 2的预训练模型将被微调 通过迁移学习输入CXR图像，这些图像被目标1中的模型分类为后COVID-19。一个 基于Aim 1和Aim 2两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，以及机械和临床意义 解释。如果成功的话，我们的研究将不仅推进对比学习算法， COVID-19后患者的肺部后遗症亚型以及相关的临床、影像学和机制 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潜在的潜在价值。 对COVID-19后患者的管理和治疗产生积极影响。