mlsanityData to 14 Oct 2026

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

arXiv 2603.27143 ↗First posted 28 Mar 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 28 Mar 2026.

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8 of 21 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
  4. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 781
  5. 2020LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihoodcited by 87
  6. 2021Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learningcited by 21
  7. 2024Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Modelcited by 16
  8. 2019Straight to the point: reinforcement learning for user guidance in ultrasoundcited by 6

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