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Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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相关实验视频

Updated: Sep 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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可解释的人工智能用于提高对基于深度学习的瘤跟踪模型的信心.

Dragos Grama^1,2, Max Dahele¹, Ben Slotman¹

¹Department of Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Medical physics

|July 15, 2025

概括

可以解释的人工智能方法,如引导后向传播 (GBP) 和DeepLIFT,可以可靠地解释SBRT期间肺瘤跟踪的深度学习模型. 这提高了对人工智能预测的信任,以准确提供放射治疗.

关键词:

可以解释的人工智能AI 辐射疗法辐射疗法瘤跟踪,可以跟踪瘤.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Last Updated: Sep 15, 2025

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科学领域:

医学物理医学物理
人工智能在医学中的应用
放射治疗技术放射治疗技术

背景情况:

卷度调节弧线疗法 (VMAT) 现在提供了研究水平的瘤位置监测,使用光镜图像.
在使用VMAT的立体体辐射疗法 (SBRT) 期间,精确的肺瘤跟踪对于确保在规划目标体积内照射至关重要.

研究的目的:

传统的模板匹配用于瘤跟踪的成功率很低.
深度学习方法提供了潜力,但需要方法来"打开黑子",并确定预测可靠性.

主要方法:

研究了四种可解释的人工智能 (XAI) 方法:引导向后传播 (GBP),层级相关性传播 (LRP),DeepLIFT和模式归属.
通过使用幻象和临床患者数据,对2D无标记肺瘤跟踪模型的XAI方法可靠性进行了评估.
进行了定量和定性评估,以确定XAI适合瘤跟踪.

主要成果:

导向向后传播 (GBP) 和DeepLIFT在所有测试患者和幻影中显示出可靠和一致的性能.
层 wise Relevance Propagation (LRP) 在幻体上表现良好,但在临床数据中产生较低的定性结果.

结论:

GBP和DeepLIFT适合在SBRT VMAT中解释基于深度学习的跟踪模型.
需要进一步的研究,以建立强大的临床可靠性测量AI在治疗期间的交付.