# Predicting Post-Covid Pulmonary Fibrosis with Explainable Deep Learning and Optimal Biomarker Discovery

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2024 · $772,290

## Abstract

PROJECT SUMMARY
Since the beginning of COVID-19 (SARS-CoV-2) pandemic, we have seen a number of prominent variants, and
two of them are very recent. COVID-19 continues to affect millions of people: over 771 million people were
affected and over 6.9 million deaths occurred (as of October 2023). A significant portion of patients who suffer
from severe COVID-19 disease are at risk for developing long-term Post-Acute Sequelae (PASC) with pulmonary
fibrosis. Identifying patients who are most at risk for post COVID-19 pulmonary fibrosis could allow early
intervention with anti-fibrotic drugs and other potential treatment strategies. Given the extent of people infected
with COVID-19, post COVID-19 pulmonary fibrosis is a significant cause of mortality and morbidity, yet there is
no strategy developed to address this unmet critical clinical need. Hence, the overall goal of this proposal is to
address this need for predicting post COVID-19 pulmonary fibrosis at an early stage by developing novel
explainable deep learning (DL) algorithms on multimodal data (combined imaging and electronic health record
(EHR) data) from multiple centers. Main Hypothesis: The proposed explainable DL algorithms will identify
patients at risk for development of post COVID-19 related pulmonary fibrosis at early phases by multimodal data
with high accuracy. In Aim 1, we will identify imaging and EHR characteristics associated with post COVID-19
fibrosis from patient data gathered from Northwestern, Columbia, NIH, and UPenn. We will automatically extract
imaging (CT scan) features in two ways: (i) pulmonary analysis (PA) and (ii) body composition analysis (BCA).
For each patient, we will collect EHR data consisting of demographic, clinical (including vital, medication,
vaccination, and comorbidity) and laboratory information. We expect to retrospectively collect a balanced dataset
of 1,150 initial CT scans and associated EHR data. In Aim 2, we will develop Transformer embedded explainable
capsule networks (X-TCaps) for prediction of post COVID-19 pulmonary fibrosis. We will integrate our newly
established visual explanation algorithm (called IBA) into the machine-generated results in addition to
radiographical explanations, and PA and BCA features captured by X-TCaps. In Aim 3, we will employ our
established optimal biomarker (OBM) method to determine the most potent features (biomarkers) from EHR and
imaging data to predict post COVID-19 pulmonary fibrosis at the highest accuracy possible. PASC is a massive
emergency and very little is known about it. Once accomplished, our proposed framework will provide early
prediction of post COVID-19 pulmonary fibrosis and determine biomarkers to understand pulmonary fibrosis
better. Our study is innovative as no previous study has investigated post COVID-19 pulmonary fibrosis, which
is paramount to developing a robust knowledge database and informing clinical practice in this area. With this
project, we will provide mechanistic understandi...

## Key facts

- **NIH application ID:** 10977818
- **Project number:** 1R01HL171376-01A1
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Ulas Bagci
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $772,290
- **Award type:** 1
- **Project period:** 2024-09-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10977818

## Citation

> US National Institutes of Health, RePORTER application 10977818, Predicting Post-Covid Pulmonary Fibrosis with Explainable Deep Learning and Optimal Biomarker Discovery (1R01HL171376-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10977818. Licensed CC0.

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