{
  "abstract": "Introduction Accurate and timely diagnosis of pelvic, hip and spinal fractures in the Emergency Department (ED) is critical to reducing morbidity and mortality. Gleamer BoneView, an AI-based fracture detection tool, was implemented in our hospital in 2024. This study aims to evaluate the impact that the use of Gleamer BoneView, had on fracture detection in a real-world ED setting. We hypothesise that AI assistance would improve detection rates without increasing downstream imaging.Methods We retrospectively analysed cases of adult ED patients undergoing pelvic/hip or spinal radiographs for suspected fracture from June–August 2024 (AI-assisted reporting) and for the same cohort in June-August 2023 (radiologist-only reported). Fracture detection was determined by the final radiologist’s report. Detection rates were compared using chi-squared tests (p<0.05). CT and MRI utilisation, along with AI diagnostic performance (sensitivity, specificity, PPV, NPV), were also assessed.Results Pelvic/hip fracture detection increased from 12.7% (49/386) in 2023 to 19.2% (70/365) in 2024 (p=0.02). Spinal fracture detection increased from 14.7% (67/457) in 2023 to 21.3% (64/301) in 2024 (p=0.03). Downstream CT use remained stable for both pelvic/hip (11.7% vs. 10.4%) and spinal radiographs (7.8% vs. 7.6%). Downstream MRI use for indeterminate spinal radiographs decreased from 5.3% in 2023 to 3.3% in 2024. AI diagnostic performance was as follows: Spine radiographs: Sensitivity 90%, Specificity 93%, PPV 79%, NPV 97%. Pelvis/hip radiographs: Sensitivity 85%, Specificity 92%, PPV 71%, NPV 96%.Conclusion AI-assisted radiograph interpretation significantly improved fracture detection rates in the ED, without increased CT usage and with reduced MRI demand. These results support the integration of AI tools into acute imaging pathways to enhance diagnostic accuracy and streamline patient management. *presenting author",
  "authors": [
    {
      "affiliations": [
        "Department of Radiology, Mater Misericordiae University Hospital"
      ],
      "name": "Molly Godson Treacy"
    },
    {
      "affiliations": [
        "Department of Radiology, Mater Misericordiae University Hospital"
      ],
      "name": "Sadhbh Doherty"
    },
    {
      "affiliations": [
        "Department of Radiology, Mater Misericordiae University Hospital"
      ],
      "name": "Oliver O’Brien"
    },
    {
      "affiliations": [
        "Department of Radiology, Mater Misericordiae University Hospital"
      ],
      "name": "Fiona Desmond"
    },
    {
      "affiliations": [
        "School of Medicine, University College Dublin"
      ],
      "name": "Francois Husson"
    },
    {
      "affiliations": [
        "Department of Radiology, Mater Misericordiae University Hospital"
      ],
      "name": "Peter MacMahon"
    }
  ],
  "title": "#195 AI-enhance fracture detection in the ED: a real-world assessment of diagnostic accuracy and imaging utilisation",
  "uid": "dc3be3b3-5cd9-52a0-84bb-d88e6f61f278"
}