Machine Learning Advances Revolutionize Pulmonary Arterial Hypertension Diagnosis

Machine learning techniques such as combining machine learning with echocardiography and electrocardiography (ECG), show promising results in diagnosing pulmonary arterial hypertension (PAH), offering a potential noninvasive alternative to traditional invasive methods, according to a study published in Frontiers in Cardiovascular Medicine.

“This paper aims to shed light on the path toward a new era in diagnosing pulmonary arterial hypertension (PAH) by exploring the intersection of machine learning and cardiovascular medicine,” explained Akbar Fadilah, Brawijaya Cardiovascular Research Center, Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia, and coauthors.

In a meta-analysis of 26 clinical trials conducted following PRISMA guidelines, the researchers analyzed various noninvasive diagnostic methods, including ECG, echocardiography, blood biomarkers, and microRNA analysis. The results revealed that machine learning algorithms accurately detect PAH, with an overall sensitivity of 81% (95% CI, 0.76–0.85; P <.001), specificity of 84% (95% CI, 0.77–0.88; P <.001), and an area under the receiver operating characteristic curve (AUC) of 89% (95% CI, 0.85–0.91).

Echocardiography emerged as the most reliable ML diagnostic tool, achieving the highest specificity (93%) and diagnostic odds ratio (70). Blood biomarkers and microRNA also showed promise, with sensitivity and specificity values supporting their use as supplementary tools. The analysis revealed a need for standardizing ML methodologies to reduce heterogeneity, which was noted across studies.

One significant advantage of ML approaches is their ability to integrate complex datasets noninvasively, reducing procedural risks while maintaining diagnostic precision. These advancements align with the growing trend of personalized medicine, ensuring patients receive tailored care based on precise, data-driven insights. However, challenges remain, including addressing variability in diagnostic outcomes across different ML applications and ensuring equitable access to advanced diagnostic technologies.

“This innovative approach demonstrates considerable potential by yielding outstanding diagnostic outcomes, thereby fostering the development of more accessible and less invasive diagnostic modalities,” concluded the study authors.

Reference

Fadilah A, Putri VYS, Puling IMDR, Willyanto SE. Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies. Front Cardiovasc Med. 2024;11:1422327. doi:10.3389/fcvm.2024.1422327