人工智能作为角病的诊断方式:系统性审查和元分析
Azzahra Afifah1,2, Fara Syafira2, Putri Mahirah Afladhanti2
1Undaan Eye Hospital, Surabaya, Indonesia.
Journal of Taibah University Medical Sciences
|January 29, 2024
概括
人工智能 (AI) 在诊断角 (KC) 方面表现出高准确度. 人工智能模型,特别是神经网络,显示出出色的灵敏度和特异性,有助于KC患者的临床决策.
科学领域:
- 眼科医生 眼科 眼科
- 医学诊断 医学诊断 医学诊断
- 人工智能的人工智能
背景情况:
- 角 (KC) 诊断带来了挑战,引发了对先进诊断工具的兴趣.
- 人工智能 (AI) 提供了一种新的方法来提高KC诊断的效率和准确性.
研究的目的:
- 系统地审查和分析人工智能作为角瘤诊断模式的实用性.
- 评估各种AI模型在识别KC方面的表现.
主要方法:
- 一个系统的审查和元分析,遵循PRISMA 2020指南.
- 在PubMed,Medline和ScienceDirect (2018-2023) 搜索了有关AI和KC诊断的研究.
- 使用审查管理器5.4分析了6篇符合条件的文章,在森林地块中展示了结果.
主要成果:
- 神经网络是KC诊断中最常用的AI模型.
- 神经网络和原始贝叶斯实现了最高的诊断准确性,灵敏度为1.00.
- 随机森林显示灵敏度>0.90,所有分析的研究报告高灵敏度和特异性 (>0.90).
结论:
- 人工智能在诊断KC方面表现出很高的表现,特别是在灵敏度和特异性方面.
- 人工智能驱动的诊断可以显著帮助临床医生为KC患者做出明智的医疗决定.
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