Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images

Kenya Kusunose; Yukina Hirata; Natsumi Yamaguchi; Yoshitaka Kosaka; Takumasa Tsuji; Jun'ichi Kotoku; Masataka Sata

doi:10.3389/fcvm.2022.891703

Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images

Front Cardiovasc Med. 2022 Jun 15:9:891703. doi: 10.3389/fcvm.2022.891703. eCollection 2022.

Authors

Kenya Kusunose¹, Yukina Hirata², Natsumi Yamaguchi², Yoshitaka Kosaka¹, Takumasa Tsuji³, Jun'ichi Kotoku³, Masataka Sata¹

Affiliations

¹ Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
² Ultrasound Examination Center, Tokushima University Hospital, Tokushima, Japan.
³ Department of Radiological Technology, Graduate School of Medical Care and Technology, Teikyo University, Tokyo, Japan.

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.

Keywords: artificial intelligence; connective tissue disease; echocardiography; exercise pulmonary hypertension; scleroderma (SSc).