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混合深度学习和机器学习用于检测肝细胞在肝脏超声波图像中的气球化

Fahad Alshagathrh¹, Mahmood Alzubaidi¹, Samuel Gecík²

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.

Diagnostics (Basel, Switzerland)

|December 17, 2024

概括

这项研究开发了一种非侵入性AI方法,使用超声图像检测非酒精性脂肪肝疾病 (NAFLD) 和非酒精性脂肪肝炎 (NASH) 的肝细胞气球化 (HB). 人工智能实现了高精度,为肝脏活检提供了精确的替代方案.

科学领域:

医疗成像医学成像
人工智能的人工智能
肝病学肝病学是一种肝病学.

背景情况:

肝细胞膨胀 (HB) 是非酒精性脂肪肝疾病 (NAFLD) 和非酒精性脂肪肝炎 (NASH) 进展的关键指标.
肝脏活检是目前HB检测的黄金标准,具有侵入性和风险,需要使用非侵入性诊断工具.

研究的目的:

开发和验证一种结合深度学习和机器学习的新方法,用于在肝超声图像中准确识别和量化肝胆异常.
建立一个非侵入性诊断方法,用于肝细胞膨胀,减少对肝脏活检的依赖.

主要方法:

训练包括InceptionV3,ResNet50,DenseNet121和EfficientNetB0在内的深层卷积神经网络 (CNN) 在扩展的肝脏超声波图像数据集上.
实施混合方法,将InceptionV3用于特征提取与随机森林分类器相结合,以提高准确性和稳定性.
利用双重二分法分类策略将图像分类为健康与疾病,随后轻度与重度的气球化阶段.

主要成果:

混合InceptionV3-Random Forest模型实现了高性能,精度为97.40%,曲线下的面积 (AUC) 为0.99.
该模型在第三个评估阶段显示出在检测"Many"类 (严重HB) 中具有很好的灵敏度 (99%).
双二分法分类显著提高了识别严重肝细胞膨胀的敏感性,交叉验证证实了模型的稳定性.

结论:

开发的人工智能驱动的方法为NAFLD和NASH的早期检测和监测提供了一个非侵入性的,准确的替代方案,可能减少侵入性肝脏活检的需要.
未来的研究将专注于用更大的多中心数据集验证这些模型,以评估可通用性和促进临床整合.

关键词:

在NAFLD诊断时,他们会发现NAFLD.处理类不平衡处理类不平衡计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.双重二分法分类的分类方式.肝细胞膨胀检测气球检测机器学习是机器学习.医疗图像分析分析转移学习转移学习

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