根据2018年AO/OTA分类系统,使用人工智能对成年人肘部周围骨折进行分类
Annelie Pettersson1, Michael Axenhus2, Teo Stukan1
1Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
BMC musculoskeletal disorders
|September 9, 2025
概括
使用卷积神经网络 (CNN) 的人工智能 (AI) 系统根据2018年AO/OTA系统准确分类肘部骨折. 这种AI工具有助于临床医生提高对肘部骨折的诊断准确性和治疗决策.
科学领域:
- 整形外科放射学 整形外科放射学
- 医学中的人工智能.
- 用于医学成像的深度学习
背景情况:
- 评估AI用于肘部骨折的诊断准确性.
- 使用2018年AO/OTA骨折分类系统.使用2018年AO/OTA骨折分类系统.
- 评估卷积神经网络 (CNN) 用于骨折分类.
研究的目的:
- 评估人工智能系统在分类肘部骨折中的准确性.
- 为了确定AI在2018年AO/OTA分类中的表现.
- 探索AI在改善骨折诊断方面的潜力.
主要方法:
- 对5,367张肘部放射图 (成年患者,2002-2016年) 的回顾性分析.
- 由骨科外科医生编写的手册AO/OTA分类.
- 微调一个预训练的Efficientnet B4 CNN模型.
- 在一组208张X射线图的测试中进行性能评估.
主要成果:
- 人工智能系统的整体骨折检测AUC达到0.88 (95%CI为0.83-0.93).
- 权重平均AUCs:近端半径为0.80,近端肘部为0.86,远端腰部为0.85.
- 人工智能在54种不同的骨折类型中展示了准确的分类.
结论:
- 人工智能系统 (CNN) 可以增强对肘部骨折的临床决策.
- 人工智能提供了一个补充工具,以提高诊断准确度.
- 人工智能可以减少未被诊断的骨折,改善患者的治疗结果和放射性评估.
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