{
  "abstract": "Aims and Objectives Chest injuries are a common trauma presentation to emergency departments. Artificial intelligence (AI) has the potential to detect pathologies using medical imaging through pattern recognition. This study aims to review current literature investigating the diagnostic accuracy of AI tools to detect traumatic chest injuries in trauma patients.Method and Design We conducted a systematic review of studies to evaluate the diagnostic performance of chest-injury detection by clinicians with and without the aid of AI. Studies written in English were retrieved from EMBASE, MEDLINE, and IEEE Xplore in March 2024 and included studies evaluating the diagnostic accuracy of detecting pneumothoraces, haemothoraces, lung contusions and rib fractures. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A meta-analysis of the diagnostic performance was summarised using recall (sensitivity) and diagnostic time.Results and Conclusion A total of 20 studies were selected for our final review. Two studies evaluated the AI-assisted detection of pneumothoraces and rib fractures on plain film radiographs. The remaining studies evaluated the detection of rib fractures on CT imaging. Twelve studies were included in the meta-analysis. Unassisted clinicians achieved a recall performance of 0.73 compared to 0.95 in the AI-assisted group, representing a mean difference of 0.12. For diagnostic time to detect rib fractures, the unassisted performance was 3m34s, compared to an AI-assisted performance of 1m55s, representing a mean difference of -99s. Our study is the first to evaluate the performance of AI tools in patients presenting with chest injuries. Our findings demonstrate that AI-assisted reading of CT imaging is associated with an improved identification of rib fractures and reduced diagnostic time. However, the detection of rib fracture injuries highlights the current limitation of AI model use in the acute trauma-care pathway.",
  "authors": [
    {
      "affiliations": [
        "Imperial College Healthcare NHS Trust"
      ],
      "name": "James Lai"
    },
    {
      "affiliations": [
        "Imperial College London"
      ],
      "name": "Ka Jun Cheng"
    },
    {
      "affiliations": [
        "Imperial College Healthcare NHS Trust"
      ],
      "name": "Dan Frith"
    },
    {
      "affiliations": [
        "Imperial College London"
      ],
      "name": "Spyros Masouros"
    },
    {
      "affiliations": [
        "Imperial College Healthcare NHS Trust"
      ],
      "name": "Shehan Hettiaratchy"
    }
  ],
  "title": "4069 The impact of AI tools on the detection of chest injuries from clinical imaging: a systematic review and meta-analysis",
  "uid": "ecd2194a-51a7-54c0-9f14-a33d84010562"
}