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

Award Number: R01HL168116
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 06/01/2023
PERIOD OF PERFORMANCE END DATE: 02/29/2028

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $688,845 )
2026	2026	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Lung Diseases Research	001	4	3/25/2026	NON-COMPETING CONTINUATION	$21,086
2026	2026	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Lung Diseases Research	000	4	3/3/2026	NON-COMPETING CONTINUATION	$667,759
														Subtotal = $688,845

Issue Date FY: 2025 ( Subtotal = $733,586 )
2025	2025	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Lung Diseases Research	000	3	7/16/2025	NON-COMPETING CONTINUATION	$733,586
														Subtotal = $733,586

Issue Date FY: 2024 ( Subtotal = $731,422 )
2024	2024	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Lung Diseases Research	001	2	6/20/2024	NON-COMPETING CONTINUATION	$59,708
2024	2024	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Lung Diseases Research	000	2	3/19/2024	NON-COMPETING CONTINUATION	$671,714
														Subtotal = $731,422

Issue Date FY: 2023 ( Subtotal = $756,675 )
2023	2023	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Lung Diseases Research	000	1	5/22/2023	NEW	$756,675
														Subtotal = $756,675

Grand Total All Awards = $2,910,528

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

Award Number: R01HL168116

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 06/01/2023

PERIOD OF PERFORMANCE END DATE: 02/29/2028

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