射线成像骨折诊断中的人工智能:临床整合的证据,陷和途径 (2020-2025)
Mohammed K Elbahi1, Abubakr Muhammed2, Mohammed Fadlelmola Abdalla Mohamednour3
1Department of Surgery, Faculty of Medicine, Al Neelain University, Khartoum, SDN.
Cureus
|October 27, 2025
概括
人工智能 (AI) 在检测骨折方面表现出很高的准确性,与放射科医生相美. 人工智能工具正在改善诊断工作流程,并越来越多地被批准用于临床使用.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 诊断错误的减少 诊断错误的减少
背景情况:
- 错过的骨折是急诊室诊断错误的常见原因,导致成本增加和患者发病率增加.
- 人工智能 (AI),特别是深度学习,正在被探索以加强肌肉骨成像分析.
- 最近的文献 (2020-2025) 关注的是AI的诊断性能,临床实用性以及骨折检测的局限性.
研究的目的:
- 综合最近关于人工智能辅助骨折诊断的文献.
- 评估AI的诊断准确性,不同解剖部位和成像模式的性能,以及现实世界的临床实用性.
- 确定AI整合在断裂检测中的挑战和未来方向.
主要方法:
- 系统审查,元分析和前性研究 (2020-2025) 的叙述综合.
- 专注于人工智能辅助的骨折诊断,使用放射和其他成像方式.
- 审查的主题:诊断准确性,解剖学/模式特异性性能,现实世界的部署,监管状况和整合挑战.
主要成果:
- 分析显示,AI的综合灵敏度和特异性超过90%,相当于放射科医生.
- 人工智能在四肢骨折 (手腕,脚,肩膀) 中表现最强;在肋骨和脊柱中表现不同.
- 人工智能辅助提高了放射科医生的灵敏度6-8%,但没有影响特异性;实际使用显示差异和停留时间减少了.
结论:
- 人工智能显示出作为骨折诊断的可靠辅助工具的潜力,其准确性接近放射科医生的性能.
- 监管批准 (FDA,CE) 和指导 (NICE) 支持人工智能集成,但数据集偏差和通用性等挑战仍然存在.
- 安全的人工智能集成需要监管部门的批准,外部验证,强大的数据集,以及针对不同临床环境的透明报告.
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