人工智能软件辅助诊断新鲜肋骨骨折的效能评估
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南京医科大学第一附属医院放射科

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Effectiveness evaluation of artificial intelligence system in the diagnosis of fresh rib fractures
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Department of radiology, the First Affiliated Hospital of Nanjing Medical University

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    目的:评估人工智能软件(artificial intelligence system, AI)辅助放射科医师在胸部计算机断层扫描(computed tomography, CT)新鲜肋骨骨折诊断中的应用效能。方法:收集共508例行胸部CT检查的新鲜肋骨骨折病例。6名影像医师分为低年资组(≤5年)和高年资组(> 5年),使用PACS系统对CT图像进行独立阅片;在间隔为期4周的洗脱期后,影像医师在结合AI辅助诊断结果的前提下对胸部CT图像进行第二轮阅片;记录骨折类型、部位及诊断时间。采用配对卡方检验比较人工智能辅助前后诊断骨折灵敏度、特异度有无统计学差异。绘制接收者操作特征曲线(receiver operating characteristic curve,ROC曲线),并计算曲线下面积(Area Under Curve,AUC),应用Medcalc软件对曲线下面积进行显著性检验。采用Cohen’s kappa系数分析低年资医师和高年资医师诊断肋骨骨折的一致性。应用配对样本t检验比较人工智能辅助前后诊断医师诊断时间是否具有差异。结果:508例胸部CT共包含骨折2883处骨折,AI辅助后低年资医师和高年资医师的诊断灵敏度由77.95%、83.96%提升至88.52%、90.98%,P<0.001;平均AUC从0.902增加到0.948,P<0.001;独立阅片和AI辅助诊断后低年资医师与高年资医师之间的Cohen’s kappa系数由0.832提升至0.900;诊断每个肋骨骨折病例的时间平均减少了28.43s,P<0.001。结论:人工智能软件辅助诊断医师诊断新鲜肋骨骨折能够提高肋骨骨折的检出效能,并减少诊断时间。

    Abstract:

    Objective: To evaluate the effectiveness of using an artificial intelligence system to assist radiologists in the diagnosis of fresh rib fractures with computed tomography (CT). Methods: A dataset of 508 cases with fresh rib fracture CT images were collected and analyzed by 6 radiologists independently with the PACS system, the radiologists were divided into two groups: low seniority group (≤5 years) and high seniority group (>5 years). After a washout period of four weeks, the CT images were evaluated again by the 6 radiologists with the assistance of AI system. The fracture type, site, and time of diagnosis were recorded for further analysis. The paired chi-square test was used to compare the sensitivity and specificity in diagnosis of fresh fracture with and without AI assistance. The receiver operating characteristic curve (ROC) and the Area Under curve (AUC) was calculated. Cohen's Kappa coefficient was used to analyze the consistency between low and high seniority radiologists in diagnosing rib fractures. Paired sample t test was used to compare the difference in diagnosis time. Results: A total of 2883 fresh rib fractures were found in the 508 CT images. With the help of AI-assisted diagnosis, the diagnostic sensitivity of low and high seniority radiologists increased significantly from 77.95%, 83.96% to 88.52%, 90.98%, P<0.001; The average AUC increased from 0.902 to 0.948, P<0.001; The Cohen's Kappa coefficient between low and high seniority radiologists increased from 0.832 to 0.900. The average diagnosis time of each case decreased 28.43s, P<0.001. Conclusion: With the AI assistance in the diagnosis of fresh rib fracture, the detection efficiency of rib fracture was improved and the diagnosis time was reduced.

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  • 收稿日期:2021-04-25
  • 最后修改日期:2021-06-30
  • 录用日期:2022-02-25
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