Publication

Deploying artificial intelligence in the detection of adult appendicular and pelvic fractures in the Singapore emergency department after hours: efficacy, cost savings and non-monetary benefits

Authors:

Quek, John Jian Xian MBBS; Nickalls, Oliver James MBBS, MMed; Wong, Bak Siew Steven MBChB, MMed; and Tan, Min On MBBS, MMed

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Abstract

This study evaluated how well RBfracture™ could detect appendicular and pelvic fractures in adult patients from after-hours radiographs in a general hospital ED in Singapore, while also estimating its potential clinical and financial benefits.

Key findings

  • RBfracture exhibited a sensitivity of 98.9%, an accuracy of 85.9% and an almost perfect agreement with the reference standard
  • RBfracture’s implementation offers significant potential measurable cost, manpower and time savings
  • The promise of reducing missed diagnoses avoids the inconvenience and delayed treatment for patients and minimises the surplus workload in performing callbacks for ED physicians

Open access

This research is open access

This original research can be read in the Singapore Medical Journal (SMJ) in July 2024.

Funding and conflicts of interests

No external funding was received for this study, and the authors declare no conflicts of interest.

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