在罕见的儿科生长障碍中进行自动骨年龄评估:使用Deeplasia进行的一项比较研究
Kyra Skaf1, Minu Fardipour1, Philipp Schmidt1
1Medical Faculty, Otto-Von-Guericke-University Magdeburg, Magdeburg, Germany.
Frontiers in endocrinology
|February 23, 2026
概括
深度学习模型Deeplasia在罕见的儿科疾病中准确评估骨年龄,优于人类专家. 这证实了它在监测增长的复杂案例中的可靠性.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 骨龄 (BA) 评估对于生长监测和治疗指导至关重要.
- 深度学习 (DL) 模型提供自动化的BA预测,但在复杂的儿科群体中面临普遍性挑战.
- 在不同患者群体上验证DL工具对于临床采用至关重要.
研究的目的:
- 验证Deeplasia开源深度学习系统对患有综合症,内分泌和溶酶体储存障碍 (LSD) 的儿科患者的外部数据进行验证.
- 为了比较Deeplasia的准确性和一致性,与多个专家人类评级者进行骨龄评估.
- 在罕见和复杂的儿科病例中评估基于DL的骨年龄预测的概括性.
主要方法:
- 追溯分析了1,138张来自儿科患者的手部X射线图,这些患者患有各种内分泌,综合征和LSD.
- 骨年龄参考标准是通过多位专家评级者使用格鲁利奇和皮尔方法建立的.
- 模型性能使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和1年准确度进行评估,并与个人人类评分器进行比较.
主要成果:
- 深血症实现了5.95个月的MAE和89.9%的内分泌/综合征条件的1年准确性 (队列1).
- 对于LSDs (第二组),Deeplasia实现了7.13个月的MAE和81.2%的1年准确度.
- 在与其他专家进行测试时,Deeplasia与个人人类专家评级者相比显示出更高的准确性和一致性.
结论:
- 迪普拉西亚是复杂儿科病例骨龄评估的经过验证,一致,强大和可靠的工具.
- 与个人人类评分器相比,DL系统显示出更高的准确性.
- 深度形成症有可能帮助临床医生评估骨龄,特别是在具有挑战性的诊断场景中.
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