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An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading

Nathan Sarkar1, Mitsuo Kumagai2, Samantha Meyr2

  • 1University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA.

Emergency Radiology
|February 1, 2024
PubMed
Summary
This summary is machine-generated.

SpleenPro, an AI tool, improved splenic injury grading objectivity and reduced radiologist interpretation time. User feedback highlighted the need for integrated clinical workflow features for enhanced diagnostic utility.

Keywords:
AAST gradeArtificial intelligenceHemorrhageSpleenTraumaUser acceptance

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • The American Association for the Surgery of Trauma (AAST) Organ Injury Scale is standard for splenic injury grading.
  • Current inter-rater agreement for AAST splenic injury grading is only moderate.
  • There is a need for tools to improve the objectivity and consistency of splenic injury assessment.

Purpose of the Study:

  • To evaluate SpleenPro, an interactive explainable AI/ML diagnostic aid.
  • To assess the impact of SpleenPro on radiologist dwell time, agreement, clinical utility, and user acceptance for AAST splenic injury grading.
  • To determine how explainability in AI impacts user perception and adoption.

Main Methods:

  • Two expert trauma radiologists independently graded 76 CT studies of blunt splenic injury, with and without AI/ML assistance, after a washout period.
  • Three versions of the SpleenPro interface with varying explainability were presented to four expert panelists.
  • User acceptance was assessed via structured interviews using Likert scales and free responses, focusing on diagnostic utility, mental support, workload, trust, and future use.

Main Results:

  • SpleenPro significantly decreased interpretation times for both radiologists.
  • Weighted Cohen's kappa for AAST grading improved from 0.53 to 0.70 with AI/ML assistance.
  • Increased AI explainability correlated with higher scores for mental support, effort/workload/frustration, trust/reliability, and likelihood of future use.

Conclusions:

  • SpleenPro demonstrated utility in enhancing the objectivity of AAST splenic injury grading and providing mental support to radiologists.
  • User research indicated a need for combined early notification and grading functionality, PACS integration, and report autopopulation.
  • Formative research identified key concepts for integrating AI into clinical workflows for improved splenic injury assessment.