对于Lentigo Maligna的人工智能:通过基于Sox-10的黑色素细胞密度映射进行自动化边际评估
Rieke Löper1, Lennart Abels2, Daniel Otero Baguer2
1Department of Dermatology, Venereology and Allergology, University Medical Center Göttingen, Robert-Koch-Straße 40, 37075 Göttingen, Germany.
Dermatopathology (Basel, Switzerland)
|January 21, 2026
概括
一个人工智能 (AI) 工具准确地评估了黑色素细胞密度 (MD) 以诊断黄斑恶性 (LM). 这种人工智能支持病理学家有效评估切除边缘,提高诊断可重现性.
科学领域:
- 皮肤病理学 皮肤病理学
- 计算病理学计算病理学
- 在瘤学瘤学.
背景情况:
- 阴茎恶性病 (LM) 诊断依赖于切除边缘的组织学评估,这是具有挑战性和耗时的.
- 黑色素细胞密度 (MD) 是对LM评估的可靠指标.
- 人工智能 (AI) 提供了自动化复杂诊断任务的潜力.
研究的目的:
- 通过测量黑色素细胞密度 (MD) 来研究人工智能工具在支持LM评估中的有效性.
- 评估人工智能工具在检测表皮和在数字化幻灯片上量化MD方面的性能.
- 确定AI工具是否可以提高LM切割边缘评估的效率和可重复性.
主要方法:
- 一个回顾性单中心研究使用了86个全幻灯片图像 (WSIs) 来进行人工智能训练和177个WSIs进行测试.
- 人工智能工具被训练来识别表皮,测量其长度,并使用Sox-10染色幻灯片计算MD.
- 人工智能模型生成了热图,显示了低,边界和高的MD,定义的正切线为每0.5毫米≥30个黑色素细胞.
主要成果:
- 人工智能模型表现出高灵敏度 (87.84%),特异性 (72.82%) 和准确性 (79.10%),测试组的AUC为0.818.
- 人工智能工具自动识别了表皮,并测量了MD,与手动计数可比.
- 该模型的热图可视化提供了跨WSIs的MD分布的快速概述.
结论:
- 开发的AI工具有效地支持皮肤病理学家通过自动化MD测量来评估LM.
- 这种由人工智能驱动的方法可以显著提高在LM病例中评估切除边缘的效率和可重复性.
- 人工智能模型有望融入常规皮肤病理学工作流程,帮助更快,更可靠的诊断.
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