AUTHOR=Kusunose Kenya , Hirata Yukina , Yamaguchi Natsumi , Kosaka Yoshitaka , Tsuji Takumasa , Kotoku Jun’ichi , Sata Masataka TITLE=Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.891703 DOI=10.3389/fcvm.2022.891703 ISSN=2297-055X ABSTRACT=Background

Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment.

Objective

We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography.

Methods

The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort.

Results

EIPH defined as ΔmPAP/ΔCO >3.3 and exercise mPAP >25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; p = 0.046).

Conclusion

Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting.