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基于Metaverse的深度学习框架用于使用基于蒙特卡罗脱落的ResNet-152进行冠状动脉狭窄症分类
T Sivaranjani1, B Sasikumar2, G Sugitha3
1Department of Computer Science and Engineering, Bharath Niketan Engineering College, Aundipatty, Theni, Tamil Nadu, India.
Computers in biology and medicine
|July 10, 2025
概括
这项研究引入了一种人工智能驱动的metaverse方法,用于使用增强的成像技术来诊断冠状动脉狭窄. 该方法达到99.20%的准确性,提高了诊断精度,以获得更好的患者结果.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的虚拟现实
- 人工智能在医学中的应用
背景情况:
- 融合虚拟现实 (VR) 和人工智能 (AI) 的元宇宙,为医学诊断提供了一个新的医疗保健平台.
- 现有的挑战包括空间分辨率不足,不确定性管理,以及在元宇宙医疗保健应用中忽视患者特定的虚拟事件.
- 冠状动脉狭窄的诊断目前面临的局限性,需要先进的,以患者为中心的方法.
研究的目的:
- 开发一种先进的方法来识别冠状动脉狭窄,使用交互式3D患者模型的元宇宙.
- 通过沉浸式的虚拟体验来提高诊断准确度和患者参与度.
- 通过整合AI和VR来解决当前诊断技术的局限性.
主要方法:
- 侵袭性冠状动脉血管学 (ICA) 数据使用量子适应扩散 (QAD) 和交叉相关方法进行预处理,以减少噪音和运动器件.
- 语义细分和灰色水平共发生矩阵 (GLCM) 用于特征提取,其次是随机梯度下降 (SGD) 与Adam优化用于特征选择.
- 使用基于蒙特卡罗脱落的ResNet-152 (MCD-ResNet-152) 进行分类,以检测狭窄,结果在元宇宙VR环境中讨论.
主要成果:
- 拟议的方法实现了冠状动脉狭窄的诊断准确率为99.20%.
- 特征提取和选择方法有效地确定了纹理和形状异常,表明了狭窄.
- 人工智能和VR在元宇宙中的集成促进了以患者为中心的诊断讨论.
结论:
- 开发的AI驱动的metaverse方法显著提高了冠状动脉狭窄症诊断的准确性.
- 这种创新方法提供了一个更加精确和以患者为中心的诊断工具,有可能提高治疗策略和患者的治疗结果.
- 超宇宙平台提供了一个有效的媒介,通过交互式3D模型和VR检查与患者讨论复杂的医疗信息.
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