Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm

• Published in: Diagnostics (Basel) Vol. 14; no. 9; p. 897
• Main Authors: Nemati, Navid, Burton, Timothy, Fathieh, Farhad, Gillins, Horace R, Shadforth, Ian, Ramchandani, Shyam, Bridges, Charles R
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Algorithms; Artificial intelligence; Cardiac catheterization; Cardiovascular disease; Chronic obstructive pulmonary disease; Coronary vessels; Data mining; Deep learning; digital health; Females; front line; Heart failure; Intubation; Machine learning; Males; Medical imaging; Medical research; Medicine, Experimental; Mortality; Patients; Point of care testing; point-of-care; Pulmonary hypertension; Tomography; Vein & artery diseases; Taiwan; digital health; point-of-care; pulmonary hypertension; artificial intelligence; front line
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14090897

Artificial intelligence, particularly machine learning, has gained prominence in medical research due to its potential to develop non-invasive diagnostics. Pulmonary hypertension presents a diagnostic challenge due to its heterogeneous nature and similarity in symptoms to other cardiovascular conditions. Here, we describe the development of a supervised machine learning model using non-invasive signals (orthogonal voltage gradient and photoplethysmographic) and a hand-crafted library of 3298 features. The developed model achieved a sensitivity of 87% and a specificity of 83%, with an overall Area Under the Receiver Operator Characteristic Curve (AUC-ROC) of 0.93. Subgroup analysis showed consistent performance across genders, age groups and classes of PH. Feature importance analysis revealed changes in metrics that measure conduction, repolarization and respiration as significant contributors to the model. The model demonstrates promising performance in identifying pulmonary hypertension, offering potential for early detection and intervention when embedded in a point-of-care diagnostic system.