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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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深度学习与光学连贯断层扫描用于黑色素瘤识别和风险预测.

Pei-Yu Lai1, Tai-Yu Shih1, Yu-Huan Chang1

  • 1Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Journal of biophotonics
|October 27, 2024
PubMed
概括

这项研究开发了一个卷积神经网络 (CNN),用于使用光学连贯断层扫描 (OCT) 成像进行早期黑色素瘤检测. 人工智能模型在识别黑色素瘤和分配风险得分方面取得了高准确性,有助于临床诊断.

关键词:
卷积神经网络是一种卷积神经网络.黑色素瘤是一种黑色素瘤.一个小鼠模型模型.光学连贯性断层扫描技术风险预测风险预测

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科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 人工智能在医学中的应用
  • 皮肤病学 皮肤病学

背景情况:

  • 恶性黑色素瘤的发病率正在上升,需要先进的诊断工具.
  • 目前黑色素瘤的非侵入性成像技术通常依赖于皮肤镜像.
  • 很少有研究利用前性数据集来开发黑色素瘤诊断模型.

研究的目的:

  • 开发和评估用于黑色素瘤识别和风险预测的卷积神经网络 (CNN).
  • 为了利用光学连贯断层扫描 (OCT) 成像用于黑色素瘤诊断.
  • 评估CNN在动物模型的纵向数据上的表现.

主要方法:

  • 开发CNN模型用于图像分析.
  • 应用CNN对光学连贯断层扫描 (OCT) 图像的小鼠皮肤.
  • 在四种动物模型上进行了纵向测试:黑色素瘤,失塑性神经和对照组.

主要成果:

  • 在对黑色素瘤与健康组织进行分类时,CNN实现了高灵敏度 (0.99) 和特异性 (0.98).
  • 该模型成功地根据黑色素瘤概率分配了风险得分.
  • 准确的分类和风险分层被证明在未来的数据集.

结论:

  • 开发的CNN显示了早期黑色素瘤检测和使用OCT成像进行风险分层的巨大潜力.
  • 这种人工智能驱动的方法可能会改善黑色素瘤的临床管理.
  • 基于OCT的CNN模型为非侵入性皮肤癌诊断提供了一个有希望的途径.