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The integration of artificial intelligence into cardiac imaging is transforming the landscape of clinical diagnostics by enabling precise, efficient, and scalable solutions. This study presents an advanced deep learning approach leveraging the YOLOv8x model for real-time segmentation of heart chambers from a single horizontal long-axis cardiac magnetic resonance image (MRI). Left ventricular and left atrial volumes, and right atrial areas, were measured and validated against industry-standard software. To ensure scalability and accessibility, an end-to-end AWS pipeline was developed, seamlessly integrating image processing, model inference, and result visualization. This research underscores the potential of combining deep learning with cloud technologies to deliver robust and adaptable solutions for functional cardiac image analysis.

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Single Image Multi-endpoint Analysis Using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Rahul Potnuru,
  • Namratha Sudunagunta,
  • Jennifer Nguyen,
  • Shames Alaesa,
  • Michael D. Nelson

摘要

The integration of artificial intelligence into cardiac imaging is transforming the landscape of clinical diagnostics by enabling precise, efficient, and scalable solutions. This study presents an advanced deep learning approach leveraging the YOLOv8x model for real-time segmentation of heart chambers from a single horizontal long-axis cardiac magnetic resonance image (MRI). Left ventricular and left atrial volumes, and right atrial areas, were measured and validated against industry-standard software. To ensure scalability and accessibility, an end-to-end AWS pipeline was developed, seamlessly integrating image processing, model inference, and result visualization. This research underscores the potential of combining deep learning with cloud technologies to deliver robust and adaptable solutions for functional cardiac image analysis.