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Automated MRI protocoling shows promise for reducing radiologist workload. However, clinical indications lack sufficient detail, and standardizing sequence names is crucial for improving cross-scanner consistency and enabling transfer learning.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Automated protocoling for Magnetic Resonance Imaging (MRI) examinations presents a significant opportunity for workflow automation using artificial intelligence (AI).
  • Current AI approaches face challenges in robustness and successful implementation, necessitating a thorough analysis of existing limitations.
  • Understanding these limitations is key to developing effective automated MRI protocoling systems.

Approach:

  • Conducted a literature review to identify limitations in published automated MRI protocoling methods.
  • Quantitatively assessed limitations using data from a private radiology practice.
  • Evaluated the information content of clinical indications (ICD-10 codes) and the heterogeneity of MRI protocol trees across different scanners.

Key Points:

  • The overlap coefficient for ICD-10 coded admitting diagnoses indicates insufficient information for automated protocoling (0.14-0.56 for brain/head, 0.04-0.57 for spine).
  • Sequence name standardization significantly increased the overlap coefficient across different MRI scanners (from 0.81/0.86 to 0.93), reducing protocol tree heterogeneity.
  • Standardization facilitates transfer learning by improving consistency in MRI sequence naming conventions.

Conclusions:

  • Automated MRI protocoling can reduce radiologist workload but cannot rely solely on admitting diagnoses due to their limited information content.
  • Sequence name standardization is essential for harmonizing MRI protocols across different scanners, thereby enhancing the feasibility of automated systems.
  • Standardization paves the way for more robust and transferable AI solutions in medical imaging protocoling.