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人工智能辅助对骨光学诊断的影响

Yosita Uchuwat^1,2, Natthanan Ruengchaijatuporn^3,4, Chanan Sukprakun⁵

¹Medical Physics Program, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.

Physical and engineering sciences in medicine

|August 5, 2025

概括

这项研究增强了用于骨扫描的深度学习模型MaligNet. 人工智能辅助提高了医生的准确性,减少了阅读时间,有助于核医学解释.

科学领域:

核医学是一种核医学.
医疗成像中的人工智能
在瘤学瘤学.

背景情况:

骨光学对检测骨损伤至关重要.
解读骨扫描需要专业知识,人工智能可能有助于医生.
恶意网络 (MaligNet) 是一个深度学习模型,此前开发用于骨扫描解释.

研究的目的:

改进和评估MaligNet深度学习模型的性能.
评估人工智能协助对核医学医生解读骨扫描的影响.
将诊断性能和阅读时间与AI支持和不支持进行比较.

主要方法:

从553名患者的骨光学数据中重新训练了MaligNet模型.
七名核医学医生 (两名初级医生,五名高级医生) 用和没有MaligNet AI协助解释了病变.
通过使用精度回忆和ROC曲线评估MaligNet性能.
评估医生绩效指标,包括准确性,敏感性,特异性,精度和阅读时间.

主要成果:

重新训练的恶意网络显示性能有所改善 (PR和ROC曲线的AUC更高).
人工智能辅助提高了医生基于患者的分类准确度 (2.14%),灵敏度 (0.89%),特异性 (2.38%) 和精度 (1.97%),阅读时间减少了31.14%.
人工智能协助提高了基于病变的分类精度 (2.95%) 并使初级医生能够达到高级水平的表现.

结论:

人工智能辅助改进的MaligNet模型提高了骨光学诊断性能.
人工智能工具在改善核医学临床实践方面显示出重大前景.
人工智能支持可以帮助弥合初级和高级医生之间的经验差距.

关键词:

人工智能的人工智能是人工智能.人工智能协助的人工智能援助骨光学扫描 (Bone Scintigraphy) 是一种骨光学扫描.恶性恶性恶性

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