在标准放射学中对两个人工智能算法的临床评估,用于儿童外周四肢的创伤后探索
Benjamin Robin1, Rabah Boukheddaden2, Sarah Ifri2
1Radiology Department, Centre Hospitalier Universitaire de Reims, 45 Rue Cognacq-Jay, 51092, Reims, France. benjamin.rob51@yahoo.com.
Pediatric radiology
|November 13, 2025
概括
人工智能 (AI) 软件在检测儿科四肢骨折方面表现一致. 人工智能工具提高了对较少经验的放射科医生的诊断准确度,但对更有经验的人来说并非如此.
科学领域:
- 放射学 放射学是一门学科.
- 儿科急救医学 儿科急救医学
- 医疗保健中的人工智能
背景情况:
- 儿科创伤是急诊室访问的常见原因之一.
- 人工智能 (AI) 提供了改善放射图片中骨折检测的潜力.
- 在同一个儿科患者群体内,对人工智能工具性能存在有限的研究.
研究的目的:
- 评估两个商业AI骨折检测软件程序在儿童中的诊断性能.
- 为了比较人工智能的性能与放射科住院医生.
- 评估AI对居民诊断准确性的影响.
主要方法:
- 366名患有外周四肢创伤的18岁以下儿童接受了X射线检查.
- 两个人工智能算法 (Boneview,Rayvolve) 被评估用于骨折检测.
- 放射科住院医生解释了带有和没有人工智能辅助的放射图.
主要成果:
- 两种AI算法都表现出可接受的灵敏度和特异性,用于骨折检测.
- 人工智能显著提高了初级居民的表现,但并没有提高高级居民的表现.
- 人工智能对联合溢出检测居民表现的影响有限.
结论:
- 商业人工智能算法在儿科四肢骨折检测方面表现一致和可接受.
- 人工智能工具显示出提高诊断准确性的潜力,特别是对经验较少的临床医生来说.
- 需要进一步评估,以了解人工智能在儿科放射学中的作用.
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