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Purpose <p>To evaluate the performance of an AI algorithm originally developed for rib fracture detection in identifying vertebral fractures on abdominal CT scans.</p> Methods <p>A retrospective study was performed on 58 patients with both CT and MRI scans. An AI model and ER clinicians independently assessed CT images for fractures of the vertebral body, transverse processes, and spinous processes. Two spine radiologists reviewed both CT and MRI data and established the reference standard.</p> Results <p>At the patient level, both the AI algorithm and ER clinicians achieved 100% sensitivity, specificity, accuracy, and F1-score. At the vertebral level, the AI algorithm achieved higher sensitivity than ER clinicians (67% vs. 54%, <i>p</i> &lt; 0.05), with similar accuracy (93% vs. 94%, <i>p</i> = 0.73). At the anatomical location level, the AI algorithm achieved a sensitivity of 62%, accuracy of 98%, and an F1-score of 0.69, compared with 46% sensitivity (<i>p</i> &lt; 0.05), 97% accuracy (<i>p</i> = 0.92), and an F1-score of 0.62 (<i>p</i> = 0.50) for ER clinicians. Among 178 confirmed fracture locations, the AI algorithm detected 110 cases, whereas ER clinicians identified 81. Of the 97 fractures missed by ER clinicians, the AI algorithm correctly detected 51.</p> Conclusions <p>The AI software originally trained for rib fracture detection identified vertebral fractures with higher sensitivity compared to those detected by ER clinicians. Its ability to identify otherwise missed fractures highlights its value as a supplementary tool in urgent care.</p>

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Evaluation of AI developed for rib fracture detection in identifying vertebral fractures on abdominal CT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Hyunjung Kim,
  • Yongfeng Gao,
  • Jin Woo Kim,
  • Jhii-Hyun Ahn,
  • Ga-Young Yoon,
  • Yeon-Jun Kim,
  • Sung Min Ko

摘要

Purpose

To evaluate the performance of an AI algorithm originally developed for rib fracture detection in identifying vertebral fractures on abdominal CT scans.

Methods

A retrospective study was performed on 58 patients with both CT and MRI scans. An AI model and ER clinicians independently assessed CT images for fractures of the vertebral body, transverse processes, and spinous processes. Two spine radiologists reviewed both CT and MRI data and established the reference standard.

Results

At the patient level, both the AI algorithm and ER clinicians achieved 100% sensitivity, specificity, accuracy, and F1-score. At the vertebral level, the AI algorithm achieved higher sensitivity than ER clinicians (67% vs. 54%, p < 0.05), with similar accuracy (93% vs. 94%, p = 0.73). At the anatomical location level, the AI algorithm achieved a sensitivity of 62%, accuracy of 98%, and an F1-score of 0.69, compared with 46% sensitivity (p < 0.05), 97% accuracy (p = 0.92), and an F1-score of 0.62 (p = 0.50) for ER clinicians. Among 178 confirmed fracture locations, the AI algorithm detected 110 cases, whereas ER clinicians identified 81. Of the 97 fractures missed by ER clinicians, the AI algorithm correctly detected 51.

Conclusions

The AI software originally trained for rib fracture detection identified vertebral fractures with higher sensitivity compared to those detected by ER clinicians. Its ability to identify otherwise missed fractures highlights its value as a supplementary tool in urgent care.