PRECISE-AS: Personalized Reinforcement Learning for Efficient Point-of-Care Echocardiography in Aortic Stenosis Diagnosis

2025/09/02 by Armin Saadat, Saadat, Armin, Hashemi, Nima +12
Engineering · Medicine · #Cardiac Valve Diseases and Treatments #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Soft Robotics and Applications #Ultrasound Imaging and Elastography

paper · pdf · doi:10.48550/arxiv.2509.02898

openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Aortic stenosis (AS) is a life-threatening condition caused by a narrowing of the aortic valve, leading to impaired blood flow. Despite its high prevalence, access to echocardiography (echo), the gold-standard diagnostic tool, is often limited due to resource constraints, particularly in rural and underserved areas. Point-of-care ultrasound (POCUS) offers a more accessible alternative but is restricted by operator expertise and the challenge of selecting the most relevant imaging views. To address this, we propose a reinforcement learning (RL)-driven active video acquisition framework that dynamically selects each patient's most informative echo videos. Unlike traditional methods that rely on a fixed set of videos, our approach continuously evaluates whether additional imaging is needed, optimizing both accuracy and efficiency. Tested on data from 2,572 patients, our method achieves 80.6% classification accuracy while using only 47% of the echo videos compared to a full acquisition. These results demonstrate the potential of active feature acquisition to enhance AS diagnosis, making echocardiographic assessments more efficient, scalable, and personalized. Our source code is available at: https://github.com/Armin-Saadat/PRECISE-AS.

Citations

• Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
• ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography
• Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
• Learning to Maximize Mutual Information for Dynamic Feature Selection
• Towards Robust Active Feature Acquisition
• Reinforcement Learning with Efficient Active Feature Acquisition
• On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessment
• Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning
• Deep Reinforcement Learning with Double Q-learning

Related

• GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis
• Severe aortic stenosis detection by deep learning applied to echocardiography
• Straight to the point: reinforcement learning for user guidance in ultrasound
• Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis
• EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation