Scholarlink

Introduction <p>Fractures pose a critical challenge in emergency settings, necessitating rapid and accurate diagnosis to prevent complications. Recent advances in artificial intelligence (AI) have opened new possibilities for fracture detection using X-ray imaging. This study aimed to evaluate the performance of SmartUrgence® by Milvue, an AI tool designed to identify fractures in emergency cases, comparing its results to computed tomography (CT) scans, the gold standard in fracture diagnosis.</p> Methods <p>Patients referred from the Orthopedic Department after clinical suspicion of fractures underwent both AI-assisted X-ray imaging and CT scans. The study compared AI-generated X-ray assessments with CT scan findings to measure the AI tool’s accuracy, sensitivity, and specificity.</p> Results <p>SmartUrgence® demonstrated strong diagnostic metrics: specificity of 95.45%, sensitivity of 91.13%, positive predictive value (PPV) of 93.39%, and negative predictive value (NPV) of 93.85%. Overall accuracy reached 93.67%, with a balanced accuracy of 93.25%. Precision (0.934) and recall (0.911) were also high, reflecting the AI’s ability to minimize false positives and negatives. However, the AI tool’s performance remained significantly different from that of CT scans (<i>P</i> &lt; 0.001), particularly in detecting certain fracture types.</p> Conclusion <p>The findings suggest that AI has strong potential as an effective tool for fracture detection in emergency care, offering high sensitivity and specificity. Nonetheless, AI should complement, not replace, CT imaging. Variability in detection rates across fracture types indicates the need for further refinement.</p> <p>Practical implication: Future research should focus on improving AI performance in complex cases and ensuring safe integration into clinical workflows. Enhancing collaboration between AI systems and medical professionals will be key to maximizing the benefits of AI-assisted diagnostics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of artificial intelligence-aided X-ray in diagnosis of bone fractures in emergency setting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月6日

查看全文
  • Nesma Abdellatif,
  • Aya El-Rawy,
  • Ahmed Abdellatif,
  • Mohammad Al-Shatouri

摘要

Introduction

Fractures pose a critical challenge in emergency settings, necessitating rapid and accurate diagnosis to prevent complications. Recent advances in artificial intelligence (AI) have opened new possibilities for fracture detection using X-ray imaging. This study aimed to evaluate the performance of SmartUrgence® by Milvue, an AI tool designed to identify fractures in emergency cases, comparing its results to computed tomography (CT) scans, the gold standard in fracture diagnosis.

Methods

Patients referred from the Orthopedic Department after clinical suspicion of fractures underwent both AI-assisted X-ray imaging and CT scans. The study compared AI-generated X-ray assessments with CT scan findings to measure the AI tool’s accuracy, sensitivity, and specificity.

Results

SmartUrgence® demonstrated strong diagnostic metrics: specificity of 95.45%, sensitivity of 91.13%, positive predictive value (PPV) of 93.39%, and negative predictive value (NPV) of 93.85%. Overall accuracy reached 93.67%, with a balanced accuracy of 93.25%. Precision (0.934) and recall (0.911) were also high, reflecting the AI’s ability to minimize false positives and negatives. However, the AI tool’s performance remained significantly different from that of CT scans (P < 0.001), particularly in detecting certain fracture types.

Conclusion

The findings suggest that AI has strong potential as an effective tool for fracture detection in emergency care, offering high sensitivity and specificity. Nonetheless, AI should complement, not replace, CT imaging. Variability in detection rates across fracture types indicates the need for further refinement.

Practical implication: Future research should focus on improving AI performance in complex cases and ensuring safe integration into clinical workflows. Enhancing collaboration between AI systems and medical professionals will be key to maximizing the benefits of AI-assisted diagnostics.