mlsanityData to 14 Oct 2026

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

arXiv 2509.02898 ↗First posted 2 Sep 2025cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 2 Sep 2025.

Builds on

10 of 26 references.

References · 10

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2023Learning to Maximize Mutual Information for Dynamic Feature Selectioncited by 56
  3. 2020Reinforcement Learning with Efficient Active Feature Acquisitioncited by 21
  4. 2023Deep Reinforcement Learning for Cost-Effective Medical Diagnosiscited by 21
  5. 2019On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessmentcited by 12
  6. 2023ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiographycited by 11
  7. 2019Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learningcited by 5
  8. 2021Towards Robust Active Feature Acquisitioncited by 5
  9. 2024Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosuscited by 3
  10. 2018Improving Skin Condition Classification with a Question Answering Modelcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Predictioncited by 1
  2. 2026Optimizing Point-of-Care Ultrasound Video Acquisition for Probabilistic Multi-Task Heart Failure Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
  2. 2024Goal-conditioned reinforcement learning for ultrasound navigation guidance
  3. 2024HeartBeat: Towards Controllable Echocardiography Video Synthesis with Multimodal Conditions-Guided Diffusion Models
  4. 2026Incentivizing Cardiologist-Like Reasoning in MLLMs for Interpretable Echocardiographic Diagnosis
  5. 2025Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
  6. 2025EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance
  7. 2026EchoSonar-R: A Multi-View Reasoning-Enabled Model for Disease Classification and Report Generation in Echocardiography
  8. 2026EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesis