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可解释的人工智能驱动的前列腺癌查使用外体多标志物基于双门FET生物传感器.

Jae Yi Choi1, Sungwook Park2, Ji Sung Shim3

  • 1Center for Advanced Biomolecular Recognition, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea; Department of Medical Device Engineering and Management, College of Medicine, Yonsei University, Seoul, 06229, Republic of Korea.

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概括

使用生物传感器的新型可解释AI (XAI) 系统改善了前列腺癌 (PCa) 检测,特别是在模两可的PI-RADS 3病变中. 这种非侵入性方法为临床决策提供了更高的准确性和可解释的结果.

关键词:
癌症查 癌症查双门场效应晶体管传感器传感器可解释的人工智能皮拉德斯 (PI-RADS) 是一个前列腺癌是什么意思 前列腺癌是什么意思尿道外基因组是什么意思

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用
  • 瘤学 诊断 诊断 瘤学

背景情况:

  • 前列腺癌 (PCa) 查依赖前列腺成像报告和数据系统 (PI-RADS) MRI,但PI-RADS 3病变的诊断准确性低 (30-40%) 和高假阳性.
  • 模两可的PI-RADS 3病变代表了对准确PCa诊断的重大未满足需求.
  • 目前的方法缺乏解释性,阻碍了基于证据的临床决策.

研究的目的:

  • 开发和验证基于可解释的人工智能 (XAI) 的PCa选系统.
  • 集成一个高度敏感的双门场效应晶体管 (DGFET) 多标记生物传感器,用于识别模两可的病变.
  • 为改善PCa诊断提供可解释的结果,特别是PI-RADS 3病例.

主要方法:

  • 开发一个XAI系统,使用DGFET生物传感器分析三种泌尿外体生物标记物的传感模式.
  • 使用102个盲目样本对系统进行验证.
  • 评估XAI系统预测的诊断准确性和可解释性.

主要成果:

  • 基于XAI的PCa选系统实现了高精度,曲线下的面积 (AUC) 为0.93.
  • 在PI-RADS 3患者中,PCa诊断的准确性是传统PI-RADS评分的两倍多.
  • 该系统将TMEM256确定为查PI-RADS 3病例的主要生物标志物,提供可解释的决策依据.

结论:

  • 基于XAI的生物传感器系统显著提高了PCa查的准确性,特别是PI-RADS 3病变.
  • 该系统的可解释性通过突出关键生物标志物的重要性来促进基于证据的临床决策.
  • 这种非侵入性方法提供了一个实用的工具,以帮助医疗保健专业人员准确诊断PCa.