Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective - Takeaways - MDSpire

Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective

  • By

  • Bernard V. Delvaux

  • Yoann Elmaleh

  • Alwin Chuan

  • Alex T. Sia

  • Rajnish K. Gupta

  • Kristopher M. Schroeder

  • Karim Guessous

  • James S. Bowness

  • May 29, 2026

  • 0 min

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  • 1

    AI-based anatomy recognition in ultrasound-guided regional anesthesia (UGRA) has been primarily evaluated through technical metrics.

  • 2

    Data quality is crucial for robust AI model development, requiring images from expert operators and standardized protocols.

  • 3

    Segmentation involves marking anatomical features as ground truth, with strategies including semantic segmentation and object detection.

  • 4

    Robust annotation criteria must be consistently applied to reduce variability in model training and evaluation.

  • 5

    Combining multiple expert annotations can enhance accuracy and reduce subjective bias in the segmentation process.

Original Source(s)

Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective

  • by Bernard V. Delvaux, Yoann Elmaleh, Alwin Chuan, Alex T. Sia, Rajnish K. Gupta, Kristopher M. Schroeder, Karim Guessous, James S. Bowness

  • May 29, 2026

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