Translational application of a self-organized deep feature engineering pipeline for non-invasive pulmonary hypertension classification from routine chest radiographs

Background Pulmonary hypertension (PH) causes high mortality and poses diagnostic challenges. Current guide- lines require invasive right heart catheterization (RHC) to confirm mean pulmonary artery pressure ≥25 mmHg. Delayed diagnosis impairs timely treatment. It is unknown whether standard chest X-rays can stratify PH sever- ity. We aimed to develop and validate Exemplar MobileNet (ExMobileNet), an explainable artificial intelligence (AI) model that classifies PH into hemodynamic categories from routine chest X-ray images and thus supports non-invasive severity assessment. Methods We collected 1,293 de-identified chest X-rays obtained from 2018 to 2023. The cohort comprised 135 patients with PH confirmed using RHC and 551 healthy controls. We defined seven multi-class tasks for key hemodynamic parameters (such as mean pulmonary artery pressure, pulmonary vascular resistance, and cardiac index). The ExMobileNet workflow consists of: (1) Feature extraction via MobileNetV2, (2) feature selection by neighborhood component analysis and chi-square feature selectors, (3) classification with k-nearest neighbors and support vector machines and (4) decision fusion by majority vote and greedy optimization. Results Task-level accuracy ranged from 90.3% to 93.2%. Geometric mean scores ranged from 78.9% to 85.1%. Overall sensitivity and specificity were 88.5% and 91.3%, respectively. Mean accuracy across all tasks was 92.0% (±1.2%). Average inference time was 2.3 ± 0.4 second per image on CPU-only hardware. Conclusions ExMobileNet achieved high agreement with RHC-based assessments using routine chest X-rays. This AI tool may enable earlier, non-invasive PH screening in clinical practice.