使用Tc-DMSA SPECT与儿童患者的深度学习来区分正常和异常脏
1Department of Nuclear Medicine, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Gueishan District, Taoyuan 33305, Taiwan; School of Chinese Medicine, Chang Gung University, No. 259, Wenhua 1st Rd, Guishan District, Taoyuan 33302, Taiwan.
Clinical radiology
|May 27, 2023
概括
深度学习模型可以有效地区分正常的儿科脏和异常的儿科脏,使用技术-99m双糖酸 (99mTc-DMSA) SPECT成像. 2.5D方法实现了高精度,显示出临床应用的前景.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 儿科脏病学 儿科脏病学
背景情况:
- 准确区分儿童的正常与痕脏对于及时干预至关重要.
- 99mTc-DMSA) SPECT成像是一种标准的诊断工具.
- 解释脏SPECT图像可能是主观的,需要大量的时间.
研究的目的:
- 评估深度学习 (DL) 的可行性,以区分正常和异常的儿科脏.
- 为了评估DL模型的性能,使用各种99mTc-DMSA SPECT图像格式.
- 确定DL在改善儿童脏痕的诊断准确度方面的潜力.
主要方法:
- 对301个儿科99mTc-DMSA脏SPECT检查进行了回顾性分析.
- 在3D SPECT,2D MIP和2.5D MIP (横向,斜,冠状视图) 上培训DL模型.
- 将DL模型的性能与核医学医生的共识读数进行比较.
主要成果:
- 使用2.5D MIP进行训练的DL模型与3D SPECT或2D MIP相比显示出更高的性能.
- 2.5D DL模型的精度达到92.5%,灵敏度达到90%,特异性达到95%.
- 这些结果表明DL模型在区分正常和异常脏SPECT发现方面具有很高的能力.
结论:
- 深度学习显示了使用99mTc-DMSA SPECT准确区分正常和异常儿科脏的巨大潜力.
- 对于DL模型培训的2.5D MIPs方法产生了最佳的诊断性能.
- 基于DL的分析可以提高儿童患者诊断脏异常的效率和可靠性.
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