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Principal Investigator: CHING-LONG LIN
Organization: UNIVERSITY OF IOWA
Fiscal Year: 2024
Award: $731,422
Funding agency: National Heart Lung and Blood Institute
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.
Terms: <Active Follow-up><Air><Alveolus><Biological Markers><Bronchial Alveolus><CAT scan><COPD><COVID infected patient><COVID patient><COVID positive patient><COVID survivors><COVID-19><COVID-19 infected patient><COVID-19 infection><COVID-19 infection survivors><COVID-19 patient><COVID-19 positive patient><COVID-19 survivors><COVID-19 virus infection><COVID19 infection><COVID19 patient><COVID19 positive patient><CT X Ray><CT Xray><CT imaging><CT scan><CV-19><CXR><Characteristics><Chronic Obstruction Pulmonary Disease><Chronic Obstructive Lung Disease><Chronic Obstructive Pulmonary Disease><Classification><Clinic><Clinical><Clinical Data><Clinical assessments><CoV emergence><Computed Tomography><Conventional X-Ray><Coronavirus Infectious Disease 2019><Data><Data Analyses><Data Analysis><Dedications><Development><Dimensions><Disease><Disorder><Emphysema><Exhibits><Exposure to><Fibrotic lesions in lung><Goals><Health><Healthcare><Image><Iowa><Knowledge><Learning><Liquid substance><Long-Term Effects><Longterm Effects><Lung><Lung Parenchyma><Lung Respiratory System><Lung Tissue><Lung scar><Lung tissue scar><Modeling><Outcome><PASC><Patient Care><Patient Care Delivery><Patients><Phenotype><Post Acute Sequelae of COVID19><Post Acute Sequelae of SARS-CoV-2><Post Acute Sequelae of SARS-CoV2><Post Acute Sequelae of severe acute respiratory syndrome coronavirus 2><Post-Acute Sequelae of SARS-CoV-2 Infection><Public Health><Pulmonary Emphysema><Pulmonary Scar><Pulmonary Tissue fibrosis><Pulmonary imaging><Radiation Dose><Radiation Dose Unit><Recommendation><Research><Resolution><SARS-CoV-2 infected patient><SARS-CoV-2 infection><SARS-CoV-2 patient><SARS-CoV-2 positive patient><SARS-CoV-2 survivors><SARS-CoV2 infection><Scarring at the lung><Scarring in the lung><Severe acute respiratory syndrome coronavirus 2 infection><Site><Structure of parenchyma of lung><Study Subject><Systematics><Techniques><Testing><Thoracic Radiography><Time><Tomodensitometry><Training><Universities><Validation><Viral Diseases><Virus Diseases><Visit><X-Ray CAT Scan><X-Ray Computed Tomography><X-Ray Computerized Tomography><X-Ray Imaging><X-Ray Medical Imaging><Xray CAT scan><Xray Computed Tomography><Xray computerized tomography><Xray imaging><Xray medical imaging><active followup><adverse sequelae of COVID><adverse sequelae of COVID-19><adverse sequelae of coronavirus disease><adverse sequelae of coronavirus disease 2019><bio-markers><biologic marker><biomarker><care for patients><care of patients><caring for patients><catscan><chest X ray><chest Xray><chest radiography><chronic COVID-19 sequelae><chronic obstructive pulmonary disorder><clinical biomarkers><clinically useful biomarkers><cohort><computed axial tomography><computer tomography><computerized axial tomography><computerized tomography><conventional Xray><corona virus emergence><coronavirus disease 2019><coronavirus disease 2019 infected patient><coronavirus disease 2019 infection><coronavirus disease 2019 patient><coronavirus disease 2019 positive patient><coronavirus disease infected patient><coronavirus disease patient><coronavirus disease positive patient><coronavirus disease-19><coronavirus disease-19 patient><coronavirus emergence><coronavirus infectious disease-19><coronavirus patient><cost><data interpretation><deep learning><deep learning algorithm><deep learning based model><deep learning method><deep learning model><deep learning strategy><developmental><emergent CoV><emergent corona virus><emergent coronavirus><emerging CoV><emerging corona virus><emerging coronavirus><emphysematous><experience><fibrotic lung><fluid><follow up><follow-up><followed up><followup><health care><human subject><imaging><imaging biomarker><imaging marker><imaging-based biological marker><imaging-based biomarker><imaging-based marker><improved><in silico><in vivo><infected with COVID-19><infected with COVID19><infected with SARS-CoV-2><infected with SARS-CoV2><infected with coronavirus disease 2019><infected with severe acute respiratory syndrome coronavirus 2><learning activity><learning algorithm><learning method><learning strategies><learning strategy><liquid><long haul sequelae of COVID-19><long haul sequelae of coronavirus disease 2019><long-term sequelae><long-term sequelae of COVID-19><long-term sequelae of SARS-CoV-2><long-term sequelae of coronavirus disease 2019><long-term sequelae of severe acute respiratory syndrome coronavirus 2><lung function><lung imaging><lung radiography><lung scanning><nCoV><new CoV><new corona virus><new coronavirus><non-contrast CT><noncontrast CT><noncontrast computed tomography><novel CoV><novel corona virus><novel coronavirus><particle><patient infected with COVID><patient infected with COVID-19><patient infected with SARS-CoV-2><patient infected with coronavirus disease><patient infected with coronavirus disease 2019><patient infected with severe acute respiratory syndrome coronavirus 2><patient with COVID><patient with COVID-19><patient with COVID19><patient with SARS-CoV-2><patient with coronavirus disease><patient with coronavirus disease 2019><patient with severe acute respiratory distress syndrome coronavirus 2><phenotypic biomarker><phenotypic marker><post COVID-19 sequelae><post acute sequelae following COVID-19><post-COVID><post-COVID-19><post-acute sequelae following SARS-CoV-2 infection><post-acute sequelae of COVID-19><post-acute sequelae of acute COVID infection><post-acute sequelae of coronavirus disease 2019><post-coronavirus disease 2019><pre-trained model><prototype><pulmonary><pulmonary function><radiographic chest image><radiographic lung image><resolutions><self supervised><self supervised learning><self supervision><severe acute respiratory syndrome coronavirus 2 infected patient><severe acute respiratory syndrome coronavirus 2 patient><severe acute respiratory syndrome coronavirus 2 positive patient><small airways disease><survive COVID-19><survive SARS-CoV-2><thoracic radiogram><thorax radiography><transfer learning><validations><viral infection><virus infection><virus-induced disease>