Artificial intelligence enhanced contemporary pulmonary hypertension care

Area of Science:

• Cardiology and Medical Imaging
• Artificial Intelligence in Medicine
• Pulmonary Medicine

Background:

• Pulmonary hypertension (PH) is a significant public health concern linked to increased mortality.
• Diagnostic challenges and complex etiologies often lead to delayed or missed PH diagnoses.
• Early identification of PH is crucial for timely intervention and improved patient prognosis.

Purpose of the Study:

• To review the role of Artificial Intelligence (AI) in improving the early identification and management of pulmonary hypertension (PH).
• To explore how AI-assisted tools can enhance diagnostic accuracy and patient triage for PH.
• To discuss the integration of AI with clinical and echocardiographic data for personalized PH assessment.

Main Methods:

• Review of current literature on AI applications in PH diagnosis and management.
• Discussion of AI-driven echocardiography techniques, including image guidance, auto-measurement, deep phenotyping, and smart reporting.
• Exploration of multi-level AI integration combining clinical and imaging data for risk stratification.

Main Results:

• AI demonstrates potential for a simplified, personalized approach to early PH identification.
• AI-assisted triage can effectively highlight patients needing further investigation for PH.
• AI integrated with echocardiography can improve the detection of PH, particularly PH due to left heart disease.

Conclusions:

• AI technologies offer a promising avenue for enhancing the diagnosis and management of pulmonary hypertension.
• AI can democratize access to medical care by assisting in the selection of high-risk individuals for expert evaluation.
• The integration of AI into clinical workflows has the potential to significantly improve outcomes for patients with PH.