深度学习用于早期诊断阴道黑色素瘤:系统性审查和元分析
Francisco Cezar Aquino de Moraes1, Gustavo Tadeu Freitas Uchôa Matheus2, Ísis Larissa de Brito Dichtl3
1Department of Clinical and Toxicological Analyses, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil. francisco.cezar2205@gmail.com.
概括
深度学习算法对诊断眼膜黑色素瘤 (UM),一种罕见的眼睛癌症,显示出前景. 使用人工智能分析 fundus 图像可能会导致更早,非侵入性检测和更好的患者结果.
科学领域:
- 眼科医生 眼科 眼科
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 卵巢黑色素瘤 (UM) 是一种罕见的眼睛癌,在转移后预后不佳.
- 目前对UM的诊断方法存在局限性,这凸显了早期检测策略的必要性.
- 转移,主要到肝脏,发生在约一半的UM患者中,这强调了改善诊断的紧迫性.
研究的目的:
- 为了评估深度学习算法的诊断准确性,深度学习算法用于皮膜黑色素瘤.
- 评估AI的灵敏度,特异性和F1评分,以区分UM与其他眼部疾病.
- 确定人工智能的潜力,作为一种具有成本效益和非侵入性的工具,用于早期发现UM.
主要方法:
- 在PubMed,Scopus和Web of Science进行了系统的文献搜索.
- 包括了将UM患者的AI诊断工具与健康个体或眼相比较的研究.
- 在遵守PRISMA指南的情况下,提取了性能指标 (灵敏度,特异性).
主要成果:
- 分析了五项涉及6388名患者 (2981个UM) 的研究.
- 在UM检测中对AI的聚合灵敏度为89.0%,没有异质性.
- 聚合特异性为84.9%,尽管观察到显著的异质性 (I2=72.3%).
结论:
- 深度学习算法展示了使用 fundus 图像进行准确的 UM 诊断的潜力.
- 基于人工智能的 fundus 图像分析可以实现早期,非侵入性检测UM.
- 开发这样的软件可能会提供一个具有成本效益的诊断解决方案,改善门诊护理.
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