基于里程碑的深度学习用于婴儿部发育性形的放射性查:开发和外部评估与IHDI指导的分类
Masatoshi Oba1, Yuichiro Kawabe1, Kayo Tsuzawa1
1Department of Pediatric Orthopedics, Kanagawa Children's Medical Center, Yokohama, Japan.
概括
一个新的深度学习系统有助于通过婴儿X射线图来诊断发育性关节发育不良. 它的准确性与临床医生相比,支持查决策,并可能提高早期检测率.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 儿科整形外科 儿科整形外科
背景情况:
- 在日本,发育性关节发育不良症查正在扩大.
- 超声波和专家临床医生的有限可用性带来了挑战.
- 婴儿的普通放射图解很难,需要先进的诊断支持.
研究的目的:
- 开发和验证一种深度学习系统,用于发育性关节发育不良的放射性诊断.
- 评估使用临床应用系统的两步选策略.
主要方法:
- 对1188张婴儿骨盆放射图 (2-12个月) 的回顾性分析.
- 系统生成的测量和国际关节发育不良研究所的等级.
- 与两个儿科整形外科医生的共识评分进行比较.
主要成果:
- 深度学习系统在测量方面与临床医生达成了很高的协议 (ICC 0.83-0.84),相当于读者之间的协议 (0.81).
- 对等级的二次加权卡帕在0.63-0.75.5之间.
- 选策略表现出0.75-0.93的敏感性和0.62-0.95.5的特异性.
结论:
- 深度学习系统有效地支持发育性关节形的放射性查决策.
- 该系统的性能可与人类专家相美.
- 建议进一步进行前性多中心评估,以完善年龄和特定地点的门.
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