Optimizing Point-of-Care Ultrasound Video Acquisition for Probabilistic Multi-Task Heart Failure Detection

TL;DR

A personalized data acquisition strategy in which an RL agent, given a partially observed multi-view study, selects the next view to acquire or terminates acquisition to support heart-failure (HF) assessment is introduced.

Abstract

Purpose: Echocardiography with point-of-care ultrasound (POCUS) must support clinical decision-making under tight bedside time and operator-effort constraints. We introduce a personalized data acquisition strategy in which an RL agent, given a partially observed multi-view study, selects the next view to acquire or terminates acquisition to support heart-failure (HF) assessment. Upon termination, a diagnostic model jointly predicts aortic stenosis (AS) severity and left ventricular ejection fraction (LVEF), two key HF biomarkers, and outputs uncertainty, enabling an explicit trade-off between diagnostic performance and acquisition cost. Methods: We model POCUS as a sequential acquisition problem: at each step, a video selector (RL agent) chooses the next view to acquire or terminates acquisition. Upon termination, a shared multi-view transformer performs multi-task inference with two heads, ordinal AS classification, and LVEF regression, and outputs Gaussian predictive distributions yielding ordinal probabilities over AS classes and EF thresholds. These probabilities drive a reward that balances expected diagnostic benefit against acquisition cost, producing patient-specific acquisition pathways. Results: The dataset comprises 12,180 patient-level studies, split into training/validation/test sets (75/15/15). On the 1,820 test studies, our method matches full-study performance while using 32% fewer videos, achieving 77.2% mean balanced accuracy (bACC) across AS severity classification and LVEF estimation, demonstrating robust multi-task performance under acquisition budgets. Conclusion: Patient-tailored, cost-aware acquisition can streamline POCUS workflows while preserving decision quality, producing interpretable scan pathways suited to bedside use. The framework is extensible to additional cardiac endpoints and merits prospective evaluation for clinical integration.

Figures (3)

• Figure 1: High-level overview of Double-Precise as an RL framework. The agent sequentially selects videos until a stop action; the environment, comprising a video encoder and a diagnostic model, returns sparse and dense rewards and next states. The diagnostic model outputs a joint distribution over AS severity and LVEF categories; the blue contour depicts this distribution at the initial state, which shifts as additional videos are acquired. As acquisition proceeds, the marginal variances contract, indicating increasing diagnostic certainty.
• Figure 2: Comparison of RL and random selection. Each circle point corresponds to a $\lambda$ from Double-Precise. For random selection, the dashed lines represent the mean performance over 5 runs, while the shaded regions indicate one standard deviation.
• Figure 3: Personalized diagnostic pathways for joint AS and LVEF prediction. Each node corresponds to a state defined by the set of acquired videos, and reports the number of patients reaching that state, the number whose acquisition terminates there, and the bACC for AS and LVEF among patients whose acquisition terminates at that node. Directed edges represent the sequential view acquisitions between states, annotated by the number of patients transitioning along each edge.