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    本研究介绍了VF-Transformer,这是一个新的AI框架,可以使用视野 (VF) 测试数据预测青光眼的进展. 它有效地处理了阶级不平衡,提高了这种不可逆转失明的主要原因的诊断准确度.

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    科学领域:

    • 眼科医生 眼科 眼科
    • 人工智能的人工智能
    • 医疗信息学 医疗信息学

    背景情况:

    • 玻璃眼是全球不可逆转失明的主要原因.
    • 目前的诊断标准是用于视野 (VF) 测试的标准自动周长度.
    • 在眼科预测任务中的类失衡,特别是在纵向VF数据方面,仍然是一个重大挑战.

    研究的目的:

    • 提出VF-Transformer,这是一个基于变压器的框架,用于预测青光眼的视野进展.
    • 为了应对阶级不平衡在纵向VF数据预测中的挑战.
    • 评估框架的性能与现有的方法和不平衡处理策略相比.

    主要方法:

    • 开发了VF-Transformer,这是一个使用变压器架构的深度学习框架.
    • 整合了一个新的反转类依赖温度 (ICDT) 损失函数.
    • 整合体重正常化以减轻阶级失衡的影响.
    • 使用标准性能指标 (准确性,敏感性,特异性,AUC) 对公共和外部医院数据集的验证.

    主要成果:

    • VF-变压器框架在预测VF进展方面表现出有效性.
    • 拟议的ICDT减肥和体重正常化策略成功解决了阶级不平衡问题.
    • 该框架实现了强大的绩效指标,在数据不平衡的情况下表现优于现有方法.

    结论:

    • 视频转换器 (VF-Transformer) 提供了一个强大的解决方案,可以从纵向视频转换器数据中预测青光眼的进展.
    • 该框架处理阶级不平衡的能力提高了其临床适用性.
    • 这种方法有望改善青光眼的治疗和预防不可逆转的失明.