Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography

arXiv cs.CV / 3/31/2026

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Key Points

  • The paper introduces a multi-task AI network for point-of-care transthoracic echocardiography that gives feedback to help users acquire the apical 4-chamber (A4CH) view and then estimates left ventricular ejection fraction (LVEF) from high-quality images.
  • The system cascades a transducer pose scoring module with an uncertainty-aware left-ventricular (LV) landmark detector, producing both pose status signals (on/near/far target) and visual landmark cues for anatomical orientation.
  • A key practical advantage is that training and inference do not require costly or cumbersome transducer position tracking hardware, relying instead on images alone.
  • Experiments use a spatially dense “sweep” protocol around the optimal A4CH view and show the model can determine transducer pose accuracy and provide landmark guidance while performing automated LVEF estimation.
  • The authors position the approach as a promising strategy for deploying TTE guidance in limited-resource settings by supporting novice users and improving scan quality consistency.

Abstract

Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pose for high-quality image acquisition and subsequent measurement is a challenging task, particularly for novice users. In this work, we present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images. The network cascades a transducer pose scoring module and an uncertainty-aware LV landmark detector with automated LVEF estimation. A strength is that network training and inference do not require cumbersome or costly setups for transducer position tracking. We evaluate performance on point-of-care TTE data acquired with a spatially dense "sweep" protocol around the optimal A4CH view. The results demonstrate the network's ability to determine when the transducer pose is on target, close to target, or far from target based on the images alone, while generating visual landmark cues that guide anatomical interpretation and orientation. In conclusion, we demonstrate a promising strategy to provide guidance for A4CH view acquisition, which may be useful when deploying point-of-care TTE in limited resource settings.