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Skin Cancer

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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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多模式机器学习方法用于诊断无形皮肤炎.

Alida Widiawaty1,2, Wresti Indriatmi2,3, Wisnu Jatmiko4

  • 1Faculty of Medicine, Universitas Riau, Pekanbaru, Riau, Indonesia.

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概括

一个新的人工智能 (AI) 模型通过结合图像分析和患者病史来准确诊断亚托皮炎 (AD). 这种人工智能工具模仿临床推理,提高了这种常见皮肤疾病的诊断准确度.

关键词:
亚托邦性皮肤炎 (Atopic Dermatitis) 是一种疾病.临床决策支持 临床决策支持皮肤病学诊断 皮肤病学诊断可解释的人工智能 (XAI)在MPNet中使用MPNet.机器学习是机器学习.多模式的人工智能 (AI)在ResNet50中使用ResNet50

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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
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 亚托匹性皮肤炎 (AD) 是一种常见的慢性炎症性皮肤病,表现不同.
  • 阿尔茨海默病的临床诊断可能是主观和不一致的,特别是在全科医生中.
  • 开发客观的诊断工具对于改善患者护理至关重要.

研究的目的:

  • 开发和评估一种多式人工智能 (AI) 模型,用于增强阿托皮性皮炎 (AD) 诊断.
  • 整合病变图像分析和结构化的患者病史 (记忆录),以提高诊断准确度.
  • 将多式联络人工智能模型的性能与仅图像和仅文本模型进行比较.

主要方法:

  • 一个两阶段的诊断研究,利用后期和前性数据.
  • 开发了结合ResNet50图像特征和MPNet文本特征的晚期融合模型.
  • 根据综合的视觉和临床数据,根据AAD 2014标准将病例分类为AD或非AD.

主要成果:

  • 多式人工智能模型在分类AD与非AD方面取得了98.28%的准确性.
  • 综合模型的性能优于仅依赖图像或文本的模型.
  • 人工智能模仿医生的推理,提供一致且不那么主观的诊断评估.

结论:

  • 该ResNet50-MPNet多式联络人工智能模型在诊断AD时表现出高准确度.
  • 该模型通过模仿临床医生的推理来提供一致的,整体的评估.
  • 进一步的外部验证和可解释的AI (XAI) 对于广泛的临床采用是必要的.