计算机视觉用于传染性角质炎的应用:系统性审查
Jad F Assaf1, Abhimanyu S Ahuja1, Vishnu Kannan2
1Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Ophthalmology science
|August 8, 2025
概括
人工智能 (AI) 显示出在感染性角膜炎中识别病原体的前景,这是可预防的失明的主要原因. 需要进一步的研究来提高AI模型的通用性和临床适用性.
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:
- 角膜每年导致超过200万例可预防的失明病例,不成比例地影响低收入和中等收入国家.
- 准确和快速的病原体鉴定对于有效的抗菌治疗传染性角质炎至关重要,但由于成本,速度和专业知识要求,目前的方法往往无法获得.
研究的目的:
- 系统地审查有关人工智能 (AI) 模型的文献,这些模型是为感染性角膜炎的病原体检测和分类而开发的.
- 在这个领域分析AI模型的方法,数据集和验证实践.
主要方法:
- 对2017年至2024年间发表的研究进行了系统的文献审查.
- 分析了37项研发或验证人工智能模型用于感染性角膜炎病原体检测的研究.
- 分析重点是模型类型,输入数据,数据集,基准真相确定和验证策略.
主要成果:
- 人工智能模型在病原体检测方面表现出高精度,特别是通过图像解释.
- 发现的关键局限性包括不良的概括性,有限的数据集多样性,缺乏多标签分类和不一致的基本真相标准.
- 大多数研究都使用单一中心的回顾性数据集,阻碍了现实世界的临床应用.
结论:
- 人工智能具有显著的潜力,可以提高诊断准确性和传染性角质炎病原体检测的全球可访问性.
- 未来的研究必须优先考虑多样化的数据集,多标签分类,前性和多中心验证以及标准化的基本真相定义,以克服当前的局限性.
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