Exploratory Analysis of Deep Learning Model for Non Invasive Classification of Pulmonary Hypertension Based On Chest X-Ray Images - Ribeiro, Estela; Carvalho, Gabriella G.; Cardenas, Diego A. C.; Souza, Rogério de; Gutierrez, Marco A. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Pulmonary Hypertension (PH) is a progressive condition in which early detection is essential for improving outcomes. Chest X-rays (CXR) may contain patterns associated with PH. This study evaluated automated PH detection from CXRs. A retrospective private dataset of 1,354 exams (1,138 PH) was analyzed. Multiple CNNs and Transformer-based architectures were trained and tested. CNNs achieved AUROC values from 0.71 to 0.76, outperforming Transformers (AUROC ≤ 0.60). Performance was mainly driven by PH cases with mPAP > 25 mmHg, while sensitivity decreased in normal and mildly elevated ranges. These results support the feasibility of non-invasive PH screening, although larger and more balanced datasets are needed for clinical validation.