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Updated: May 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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深度学习模型用于区分三种鼻恶性瘤使用多序MRI.

Luxi Wang1,2, Naier Lin2, Wei Chen1,2

  • 1Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.

BMC medical imaging
|February 21, 2025
PubMed
概括
此摘要是机器生成的。

使用MRI的深度学习 (DL) 模型有效地区分鼻腔状细胞癌 (SCC),腺囊性癌 (ACC) 和嗅觉神经母细胞瘤 (ONB). 人工智能辅助显著提高了初级和高级放射科医生的诊断准确度.

关键词:
深度学习是一种深度学习.磁共振成像技术 磁共振成像技术神经网络的神经网络的神经网络在Sinon的salon里.状细胞癌瘤是一种状细胞癌.

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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 使用MRI区分鼻腔状细胞癌 (SCC),腺囊性癌 (ACC) 和嗅觉神经母细胞瘤 (ONB) 是一个挑战.
  • 深度学习 (DL) 提供了改善复杂瘤成像诊断准确性的潜力.

研究的目的:

  • 开发和评估基于MRI的深度学习 (DL) 模型,以区分鼻腔SCC,ACC和ONB.
  • 评估这些DL模型对放射科医生的诊断性能的影响.

主要方法:

  • 对465名患有鼻腔SCC,ACC或ONB的患者进行了回顾性分析.
  • 使用T2WI,CE-T1WI和ADC序列开发传统的MRI和DL模型.
  • 使用ResNet101,ResNet50和DensNet121架构对DL模型性能进行评估.
  • 评估放射科医生的表现,有或没有人工智能辅助.

主要成果:

  • 传统的MRI模型实现了78.8%的AUC.
  • ResNet101 DL模型表现出卓越的性能,平均融合序列的宏观AUC为0.892和微观AUC为0.875.
  • 人工智能辅助显著提高了高级和初级放射科医生的诊断准确度,回忆,精度和F1分数.

结论:

  • 该ResNet101DL模型有效地区分了鼻腔SCC,ACC和ONB.
  • 人工智能驱动的DL模型提高了放射科医生的诊断能力,从而提高了鼻瘤分类的准确性.