基于深度学习的人工智能软件在肋骨新鲜骨折CT诊断中的应用价值

doi:10.3969/j.issn.1671-7171.2024.10.033

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摘要【目的】探讨基于深度学习的人工智能(AI)软件在肋骨新鲜骨折CT诊断中的应用价值。【方法】选择2022年6月至2023年9月本院收治的符合标准的259例新鲜骨折患者,将其影像学资料导入AI软件工作站,以放射科一位工作8年的主治医师及AI软件诊断肋骨骨折情况进行分组,A组为AI软件单独诊断,B组为医师单独诊断,AB组为医师联合AI软件诊断。分别记录三组的骨折病灶数量、部位、分型,将三组结果与参考标准进行分析,比较三组骨折检出的敏感度、误诊率、不完全性骨折检出率,比较B组、AB组平均诊断时间。【结果】259例患者共563处骨折,完全性骨折385处,不完全性骨折178处。A组、B组、AB组骨折检出的敏感度分别为91.47%、83.13%、92.54%,三组敏感度比较,差异有统计学意义(P<0.05)。A组、AB组骨折检出的敏感度均高于B组,差异有统计学意义(P<0.05)。A组、B组、AB组的误诊率分别为20.03%、13.17%、12.58%,三组误诊率比较,差异有统计学意义(P<0.05)。A组误诊率最高,显著高于B组、AB组,差异有统计学意义(均P<0.05)。A组、B组、AB组的不完全性骨折的检出率分别为75.28%、50.56%、76.40%,三组比较,差异有统计学意义(P<0.05)。AB组平均诊断时间短于B组,差异有统计学意义(P<0.05)。【结论】医师与AI共同诊断提高了肋骨新鲜骨折检出的敏感度,主要体现在不完全性骨折的检出率上,还可明显缩短诊断时间,提高工作效率。

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关键词 ：肋骨骨折/影像诊断, 体层摄影术, 螺旋计算机, 人工智能

Abstract：【Objective】 To explore the application value of deep learning-based artificial intelligence (AI) software in the diagnosis of CT of fresh rib fractures.【Methods】A total of 259 qualified fresh fracture patients admitted to our hospital from June 2022 to September 2023 were selected and their imaging data were introduced to the AI workstation; Based on a 8-year attending physician in the department of radiology and AI software to diagnose rib fractures, the group A was diagnosed separately by AI software, and the group B and the group AB were diagnosed by physician combined with AI software. The number, site, and classification of fracture lesions were recorded in the three groups; The results of the three groups were analyzed with the reference standard to compare the sensitivity, misdiagnosis rate and detection rate of incomplete fractures in the three groups; Mean time of diagnosis was compared between the groups B and the group AB. 【Results】There were 563 fractures in 259 patients; There were 385 complete fractures and 178 incomplete fractures. The sensitivity of fracture detection in the group A, the group B and the group AB was 91.47%, 83.13%, and 92.54%, respectively. The sensitivity comparison of the three groups showed statistically significant differences (P<0.05).The sensitivity of fractures in the group A and the group AB was higher than that in the group B, significant (P<0.05). The misdiagnosis rates in the group A, the group B and the group AB were 20.03%, 13.17% and 12.58%, respectively, which was statistically significant (P<0.05).The group A had the highest misdiagnosis rate, significantly higher than that of the group AB and the group B, with a statistically significant difference (all P<0.05). The detection rate of incomplete fractures in the group A, the group B and the group AB was 75.28%, 50.56% and 76.40%, respectively, and the difference was statistically significant (P<0.05).The mean diagnosis time in the group AB was shorter than that of the group B , with significant difference(P<0.05).【Conclusion】The joint diagnosis between doctors and AI improves the sensitivity of the detection of fresh rib fractures, which is mainly reflected in the detection rate of incomplete fractures, and can also significantly shorten the diagnosis time and improve the work efficiency.

Key words： Rib Fractures/DG Tomography, Spiral Computed Artificial Intelligence

收稿日期: 2024-01-10

中图分类号:

R655

通讯作者: *E-mail:178078311@qq.com

引用本文:

吕丹, 高凯波, 邓士杰, 孙倩, 杨建宝, 李雪健, 谭海涛. 基于深度学习的人工智能软件在肋骨新鲜骨折CT诊断中的应用价值[J]. 医学临床研究, 2024, 41(10): 1571-1574.
LYU Dan, GAO Kaibo, DENG Shijie, et al. Application Value of Deep Learning-based AI Software in CT Diagnosis of Fresh Rib Fractures. JOURNAL OF CLINICAL RESEARCH, 2024, 41(10): 1571-1574.

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http://journal07.magtech.org.cn/yxlcyj/CN/10.3969/j.issn.1671-7171.2024.10.033 或 http://journal07.magtech.org.cn/yxlcyj/CN/Y2024/V41/I10/1571

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