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通过人工智能增强肝细胞癌的超声波检测:当前的应用,挑战和未来的方向

Janthakan Wongsuwan¹, Teeravut Tubtawee², Sitang Nirattisaikul²

¹Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand.

BMJ open gastroenterology

|July 1, 2025

概括

人工智能 (AI) 用超声波成像显示了早期肝细胞癌 (HCC) 检测的前景. 深度学习模型,如卷积神经网络,可以改善诊断,但必须解决数据质量和标准化等挑战,以获得更广泛的临床使用.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

肝细胞癌 (HCC) 是全球癌症死亡的主要原因.
早期发现HCC对于改善患者存活率至关重要.
医学图像分析中的人工智能 (AI) 为HCC诊断和监测提供了潜力.

研究的目的:

审查人工智能在HCC监测中的当前状态,重点是超声波 (美国) 成像.
探索AI改变临床实践和改善患者治疗结果的潜力.
确定AI在HCC监测中的挑战和未来方向.

主要方法:

对用于HCC诊断的AI模型进行全面分析,强调深度学习.
专注于人工智能在B模式超声波成像中的应用,以检测肝脏病变.
检查阻碍临床整合的挑战和改善策略.

主要成果:

深度学习模型,特别是卷积神经网络,在检测美国人的焦点肝病变方面取得了成功.
在某些情况下,人工智能模型可以超过传统的放射性评估.
主要挑战包括数据异质性,缺乏标准化,可解释性,监管障碍和临床采用障碍.

结论:

新兴的人工智能趋势,如多式联运集成和可解释的人工智能,为HCC监控提供了进步.
这些创新可以提高人工智能驱动的HCC监测的准确性,效率和临床适用性.
应对当前的挑战对于实现AI在改善HCC患者治疗结果方面的全部潜力至关重要.

关键词:

肝细胞癌细胞癌症肝脏成像技术肝脏成像技术监视银行监视银行超声波检测超声波检测超声波检测超声波检测

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