以注意力为基础的多模式深度学习用于用超广场底图像和眼睛超声波对膜黑色素瘤进行分类
Albert K Dadzie1, Sabrina P Iddir2, Mansour Abtahi1
1Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois.
Ophthalmology science
|December 24, 2025
概括
一个新型的深度学习模型整合了 fundus 摄影和超声波成像,准确地分类了脑膜黑色素瘤 (UM) 和胸腔瘤. 这种多模式的方法提高了对这些眼睛瘤的诊断能力.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 卵巢黑色素瘤 (UM) 和胸腔瘤是常见的眼内瘤,需要精确的区分.
- 当前的诊断方法在区分这些疾病方面可能存在局限性.
研究的目的:
- 开发和评估一个深度学习模型,用于自动分类阴道黑色素瘤和胸腔瘤.
- 评估结合超广场底部摄影和B扫描超声波的有效性.
主要方法:
- 一项回顾性研究包括了174名患有UM或胸腔内的患者.
- 深度学习模型使用 fundus 摄影,超声波图像或两者的组合进行训练.
- 使用五倍交叉验证来评估模型性能.
主要成果:
- 结合 fundus 摄影和超声波的模型获得了最高的准确性 (94%),F1 评分 (0.9445),和 AUC (0.9606).
- 单模模式模型表现出强的性能,横向超声波产生了最好的结果 (准确率:92%).
- 多式模式有效地整合了互补的信息,优于单式模式的方法.
结论:
- 综合 fundus 摄影和超声波成像的多式深度学习显著改善了UM和胸腔瘤的分类.
- 这种方法证明了利用多种成像模式实现自动诊断的可行性.
- 开发的模型显示了提高眼部瘤诊断精度的前景.
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