深度学习用于多种类型的传染性角膜炎诊断:全国性的,横截面的,多中心的研究
Zhongwen Li1,2, He Xie2, Zhouqian Wang2
1Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China.
NPJ digital medicine
|July 6, 2024
概括
一个新的深度学习系统DeepIK,可以从裂纹灯图像中准确诊断传染性角膜炎. 这种人工智能工具有助于眼科医生快速识别和治疗细菌,真菌,病毒和阿米比克角膜炎.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 传染性角膜炎是全球角膜失明的主要原因.
- 及时诊断对于有效的管理至关重要,但目前的方法,如角膜擦除培养,是缓慢的,往往不准确的.
研究的目的:
- 开发和验证深度学习系统 (DeepIK) 用于诊断传染性角膜炎.
- 评估DeepIK使用裂纹灯图像区分细菌,真菌,病毒,阿米比和非传染性角膜炎的能力.
主要方法:
- 一个深度学习系统DeepIK被训练并测试了来自12个临床中心的23055张灯图像.
- 使用内部,外部和前数据集来评估性能.
- 将DeepIK的诊断准确性与三个最先进的算法进行了比较.
主要成果:
- 在所有数据集中,DeepIK实现了高诊断性能 (ROC曲线下的面积>0.96).
- 该系统的性能超过了DenseNet121,InceptionResNetV2和Swin-Transformer算法.
- 迪比克在识别和区分各种类型的角质炎方面表现出能力.
结论:
- DeepIK显示出作为一种帮助眼科医生快速准确诊断传染性角膜炎的工具的巨大潜力.
- 这种人工智能驱动的方法可以促进及时和有针对性的治疗,改善患者的治疗结果.
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