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相关概念视频

Skin Cancer01:30

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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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通过将音频和视觉卷积神经网络相结合的机器学习来检测不同肤色的皮肤癌.

Bruce N Walker1, Travis Wayne Blalock2, Rebecca Leibowitz2

  • 1School of Psychology and School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA.

Oncology
|September 23, 2024
PubMed
概括

使用视听数据的人工智能准确诊断了白色和深色皮肤颜色的皮肤癌. 这种双模式技术有望提高皮肤癌检测的可访问性和有效性.

关键词:
深度学习是一种深度学习.医学可视化医疗可视化预防医学 预防医学皮肤癌是一种皮肤癌.皮肤颜色的颜色的皮肤.声波化是指对声波进行调节.

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科学领域:

  • 皮肤病学 皮肤病学
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 皮肤癌 (SC) 在浅色皮肤 (FS) 中普遍存在,但在深色皮肤 (DS) 中被诊断为晚期,导致结果较差.
  • 机器学习对SC的诊断准确性通常在DS中低于FS.
  • 开发了一种新的风险干预技术,将视觉和声音数据 (像素到声音) 结合起来,以提高SC检测.

研究的目的:

  • 评估双声视觉人工智能 (AI) 分类器在诊断不同肤色皮肤癌的有效性.
  • 为了比较人工智能在白皮肤 (菲茨帕特里克I-III) 与黑皮肤 (菲茨帕特里克IV-VI) 的诊断性能.

主要方法:

  • 一项回顾性研究使用了经过活检验证的智能手机图像,并通过双声视觉卷积神经网络进行分析.
  • 60张菲茨帕特里克I-III和72张菲茨帕特里克IV-VI皮肤病变图像进行了比较.
  • 用灵敏度,特异性和接收器操作特征曲线 (AUC) 下的面积来评估诊断性能.

主要成果:

  • 人工智能分类器在皮肤色调之间实现了可比的诊断性能,FS的AUC为0.858,DS的AUC为0.856.
  • 对FS和DS的敏感性分别为84.4%和79.6% (p=NS).
  • 对FS和DS的特异性分别为84.2%和85.3% (p=NS).

结论:

  • 双模态AI分类器有效地识别出皮肤癌在明亮和深色皮肤色调与类似的准确性.
  • 恶性标志的声学显示了改善皮肤癌诊断的潜力,即使使用智能手机捕获的图像.
  • 这项技术可以为更有效和更容易获得的皮肤癌检测医疗保健做出贡献.