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在多中心超声波图像上对青春期乳腺发育进行深度学习辅助诊断

Chao Geng^1,2, Chao Qian¹, Xiaoshi Hu^3,4

¹Department of Ultrasound, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, No. 100 Xianggang Road, Wuhan, Hubei 430016, China.

|January 7, 2026

概括

一个新的AI模型,STransXNet,使用超声波准确地分阶段青春期乳腺发育. 该工具提供了一种标准化的方法,改进了当前的临床方法,并有助于诊断青春期疾病.

科学领域:

医疗成像医学成像
人工智能的人工智能
儿科内分泌学儿科内分泌学

背景情况:

精确的青春期乳腺发育阶段对于识别青春期时机障碍至关重要.
乳房超声波是一种有价值的,非侵入性的工具,但受到了观察者之间的变化.
需要标准化和客观的方法来补充临床评估.

研究的目的:

开发和验证混合深度学习模型STransXNet,用于使用超声波自动测试青春期乳腺发育阶段.
评估模型的准确性,概括性和性能,以区分具有挑战性的中间阶段.

主要方法:

一项多中心的回顾性研究,涉及来自三个医院的2576张超声波图像.
开发和验证混合深度学习模型 (STransXNet).
在不同的数据集和成像系统中进行内部和外部队列测试.

主要成果:

在内部测试组中,STransXNet实现了88.0%的准确性,超过了基线模型和放射科医生.
该模型在外部数据集和成像系统中展示了强大的通用性.
在区分具有挑战性的青春期发育中期阶段时,观察到卓越的表现.

结论:

STransXNet提供了一种标准化和可重复的方法,通过超声波来评估青春期乳腺发育.
人工智能模型可以支持临床决策,特别是在资源有限的环境中.
这项技术有可能改善对青春期时间和疾病的评估.

关键词:

人工智能的人工智能是人工智能.发育解剖学的发展解剖学

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