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使用生成性深度学习的光学连贯性断层扫描胆道增强
Valentina Bellemo1,2,3, Ankit Kumar Das4, Syna Sreng1,3
1Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore.
NPJ digital medicine
|May 4, 2024
概括
一个人工智能 (AI) 模型增强了光谱域光学连贯性断层扫描 (SDOCT) 图像,改善了胸腔视觉化. 这种人工智能解决方案可以使用负担得起的SDOCT设备准确量化测量胆道指标.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 光谱域光学连贯性断层扫描 (SDOCT) 是一种标准的临床眼睛成像技术,但其透深度有限,阻碍了胸膜可视化.
- 胸腔视觉化对于诊断胸腔关节疾病至关重要,虽然扫描源OCT (SSOCT) 提供了更好的视觉化,但它昂贵且难以获得.
研究的目的:
- 开发和验证基于人工智能 (AI) 的解决方案,使用生成深度学习 (DL) 来增强SDOCT图像中的胆道可视化.
- 通过人工智能增强的SDOCT图像进行胆道指标的定量测量,克服标准SDOCT透深度的局限性.
主要方法:
- 一个生成的深度学习 (DL) 模型使用150,784张SDOCT-SSOCT配对图像的数据集进行训练,这些图像来自健康的眼睛,青光眼和糖尿病视网膜病变.
- 该模型学会了通过利用配对图像的深层解剖特征来生成合成增强的SDOCT B扫描,从而提高胆道可见性.
- 使用独立的外部测试数据集来评估合成增强的SDOCT图像的真实性和质量,专家在区分真实和合成图像方面表现不佳.
主要成果:
- 合成增强的SDOCT图像显示出更好的胸腔可见性,专家无法可靠地将其与真实SSOCT图像区分开来 (准确率为47.5%).
- 从增强的SDOCT测量胆道厚度,面积,体积和血管度指数的测量显示,与参考SSOCT测量有很高的相关性 (皮尔森r > 0.87).
- 冠状体指标的类内相关性值很高,表明增强的SDOCT和SSOCT测量之间有很好的一致性 (ICC>0.93).
结论:
- DL生成模型成功地创建了与SSOCT图像无法区分的现实,增强的SDOCT数据,显著改善了胸腔视觉化.
- 这种人工智能技术能够准确的定量测量胆道指标,克服传统SDOCT的成像深度限制.
- 这些发现表明,这种人工智能驱动的方法可以扩大可负担得起的SDOCT设备的实用性,用于在各种眼睛条件下研究状腺.
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