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在儿科部X射线中测量放射性参数时评估深度学习的准确性

Byoung-Dai Lee¹, Jin Young Kim², Ki-Ryum Moon¹

¹Division of AI and Computer Engineering, Graduate School, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, 16227, Suwon-si, Gyeonggi-do, Republic of Korea.

BMC medical imaging

|December 30, 2025

概括

一个新的深度学习系统准确地测量了儿科骨盆X射线的关键参数,帮助评估部发育. 该系统在各种年龄组中显示出高可靠性,提高了诊断能力.

科学领域:

儿科放射学儿科放射学
医学成像分析分析医学成像分析
医疗保健中的人工智能

背景情况:

评估儿科骨盆X射线对于评估部发育至关重要.
目前的深度学习 (DL) 方法缺乏特定年龄的可靠性和全面的参数测量.
这项研究解决了儿科部评估中的这些局限性.

研究的目的:

开发和验证基于DL的系统,用于测量儿科骨盆X射线中的多个X射线参数.
评估DL系统的特定年龄的可靠性.
为了评估系统在区分正常与脱的部的准确性.

主要方法:

开发了一个DL系统,使用1495 (培训) 和1300 (评估) 韩国儿童的AP盆腔放射图.
测量了骨指数 (AcI),申顿线 (ShL),骨盆旋转指数 (PRI) 和骨盆倾斜指数 (PTI).
对与年龄相关的骨发育进行了子组分析,并使用ICC,r,MAE,RMSE,HD和FD进行评估.

主要成果:

DL系统与放射科医生对AcI,PRI,PTI和ShL的测量显示出强烈的一致性和相关性.
在所有参数中观察到高可靠性 (例如,AcI:ICC=0.89,r=0.91;PRI:ICC=0.94,r=0.94).
性能因年龄相关的盆骨发育而有所不同,需要进行子组分析.

结论:

开发的DL系统非常可靠和准确,用于测量儿科盆腔X射线中的关键放射性参数.
它显示了区分正常与脱的部和评估X线图质量的潜力.
该系统为儿科部发育评估提供了一个全面的解决方案.

关键词:

乙肌体指数 (ACE) 是一个指数.深度学习是一种深度学习.儿科骨盆评估儿科骨盆评估骨盆对齐是如何进行的盆腔放射图片盆腔放射图片盆腔放射图

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