Application of physical examination information annotation combined with artificial intelligence in CT diagnosis of rib fracture

AO Ping; ZHANG Yu-lin; ZHU Li; XIU Zhi-gang

doi:10.11659/jjssx.01E024035

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Application of physical examination information annotation combined with artificial intelligence in CT diagnosis of rib fracture

Clinical Research | 更新时间：2025-01-16

- Application of physical examination information annotation combined with artificial intelligence in CT diagnosis of rib fracture
- JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY Vol. 34, Issue 1, Pages: 41-44(2025)
- 作者机构：
  
  1.四川大学华西医院龙泉医院放射科，四川成都 610100
  2.四川大学华西医院龙泉医院超声科，四川成都 610100
- 作者简介：
- 基金信息：
- DOI：10.11659/jjssx.01E024035
  CLC： R445.3;R683.1
- Published：25 January 2025，
  
  Received：08 January 2024，
- 稿件说明：
移动端阅览
AO PING, ZHANG YU-LIN, ZHU LI, et al. Application of physical examination information annotation combined with artificial intelligence in CT diagnosis of rib fracture. [J]. Journal of regional anatomy and operative surgery, 2025, 34(1): 41-44.
DOI：

AO PING, ZHANG YU-LIN, ZHU LI, et al. Application of physical examination information annotation combined with artificial intelligence in CT diagnosis of rib fracture. [J]. Journal of regional anatomy and operative surgery, 2025, 34(1): 41-44. DOI： 10.11659/jjssx.01E024035.

摘要

目的

探讨查体信息备注联合人工智能（AI）辅助诊断在肋骨骨折CT诊断中的应用价值。

方法

收集100例备注有查体信息的胸部外伤行肋骨CT检查患者的临床资料，由放射科2名不同年资医师分别用4种方法[医师独立诊断（A组）、医师结合查体信息备注诊断（B组）、医师在AI辅助下诊断（C组）、医师在AI辅助下结合查体信息备注诊断（D组）]对图像进行分析，比较2名医师使用不同方法对肋骨骨折的诊断效能及诊断用时。

结果

2名不同年资放射科医师对肋骨骨折诊断敏感度A组低于B、C、D组（

0.05），但B、C、D组肋骨骨折诊断敏感度组间两两比较差异均无统计学意义（

0.05）；住院医师A组诊断敏感度低于主治医师（

0.05），2名医师其余组间肋骨骨折诊断敏感度两两比较差异均无统计学意义（

0.05）。2名医师对肋骨骨折假阳性率组间两两比较差异均无统计学意义（

0.05）。2名医师各组间诊断用时比较差异具有统计学意义（

0.05），其中A组诊断用时最长，C组用时最短。

结论

医师在AI辅助下结合查体信息备注能明显提高肋骨骨折的诊断敏感度，缩短诊断时间，从而提高工作效率。

Abstract

Objective

To explore the application value of physical examination information annotation combined with artificial intelligence （AI） in CT diagnosis of rib fractures.

Methods

The clinical data of 100 patients with chest trauma who underwent rib CT examination with physical examination information annotation were collected. The images were analyzed by two physicians in the department of radiology with different seniorities using four methods ［diagnosed by physicians independently （group A）， diagnosed by physicians combined with physical examination information annotation （group B）， diagnosed by physicians under the assistance of AI （group C）， and diagnosed by physicians combined with physical examination information annotation under the assistance of AI （group D）］. The diagnostic efficacy and diagnostic time of two radiologists using different methods for rib fractures were compared.

Results

The sensitivities of two radiologists with different seniorities in the diagnosis of rib fracture in the group A were lower than those in the groups B， C and D （

0.05）， but there was no significant difference in the sensitivity of rib fracture among groups B， C and D （

0.05）. The diagnostic sensitivity of resident physician in the group A was lower than that of the attending physicians （

0.05）， and there was no significant difference in the diagnostic sensitivity of rib fracture in the other groups between the two physicians （

0.05）. There was no statistically significant difference in the false-positive rate of rib fractures among groups between two physicians （

0.05）.There was statistically significant difference in the diagnostic time among groups between two physicians （

0.05）， among which group A took the longest diagnosis time and group C took the shortest.

Conclusion

The assistance of AI and conbinatin of physical examination information annotation can increase the sensitivity of the physician in the diagnosis of rib fractures， shorten the diagnostic time and improve the work efficiency.

关键词

肋骨骨折多层螺旋CT人工智能查体信息备注诊断

Keywords

rib fracturesmulti-slice spiral CTartificial intelligencephysical examination information annotationdiagnosis

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