{
  "abstract": "Aims and Objectives Incorrect fracture diagnosis is the most frequent diagnostic error in UK emergency departments, causing significant patient morbidity and cost to the NHS. This study aimed to test the impact of an artificial intelligence (AI)-assisted fracture detection tool on NHS clinicians’ diagnostic performance.Method and Design A dataset of 500 plain radiographs from Oxford University Hospitals was curated based on the frequency of fracture detection claims reported by NHS Resolution, with 250 images containing one or more fractures and 250 without. Ground truth was established through independent reporting by two senior musculoskeletal radiologists, with a third arbitrating disagreement. Images were subsequently inferenced by fracture detection software (Gleamer BoneView). A multicase multireader study was conducted including 18 clinicians of varying seniority from six clinical specialties. Readers interpreted all images, then repeated the reads after a four-week washout period with AI assistance. Changes in diagnostic performance, confidence, and reporting speed were compared, along with the diagnostic performance of the algorithm against ground truth.Abstract 4128 Table 1Results and Conclusion Pooled analyses for per-case reader performance demonstrated an increase in the area under the receiver operating curve from 0.883 (95% CI 0.858–0.907) without AI to 0.921 (95% CI 0.901–0.942) with AI (p < 0.001). Sensitivity improved from 0.828 (95% CI 0.788–0.868) to 0.867 (95% CI 0.827–0.906) with AI (p < 0.001), and specificity increased from 0.829 (95% CI 0.784–0.875) to 0.904 (95% CI 0.877–0.931) with AI (p < 0.001).AI-assisted fracture detection showed potential to improve clinician and radiologist accuracy on plain X-rays. Further real-world prospective studies are needed to demonstrate clinical efficacy in urgent care settings.",
  "authors": [
    {
      "affiliations": [
        "John Radcliffe Hospital"
      ],
      "name": "James Vaz"
    },
    {
      "affiliations": [
        "Oxford University Hospitals NHS Foundation Trust"
      ],
      "name": "Abdala Espinosa"
    },
    {
      "affiliations": [
        "University College London Hospitals NHS Foundation Trust"
      ],
      "name": "Nicholas Woznitza"
    },
    {
      "affiliations": [
        "Grest Ormond Street Hospital for Children"
      ],
      "name": "Susan Shelmerdine"
    },
    {
      "affiliations": [
        "Oxford University Hospitals NHS Foundation Trust"
      ],
      "name": "Sally Beer"
    },
    {
      "affiliations": [
        "Liverpool University Hospitals NHS Foundation trust"
      ],
      "name": "Alpesh Mistry"
    },
    {
      "affiliations": [
        "Oxford University Hospitals NHS Foundation Trust"
      ],
      "name": "Natasa Devic"
    },
    {
      "affiliations": [
        "Oxford University Hospitals NHS Foundation Trust"
      ],
      "name": "James The"
    },
    {
      "affiliations": [
        "Oxford University Hospitals NHS Foundation Trust"
      ],
      "name": "Sarim Ather"
    }
  ],
  "title": "4128 Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI)",
  "uid": "db9b60c3-4b10-5672-a5f4-68ef67eaaad5"
}