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在宫淋巴结中识别微转移 - - 一种基于机器学习的方法
Kuntala Mondal1, Sowmya Sv1, Dominic Augustine1
1Department of Oral & Maxillofacial Pathology and Oral Microbiology, Faculty of Dental Sciences, M. S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India.
International dental journal
|February 5, 2026
概括
一个新的卷积神经网络 (CNN) 算法有效地检测了淋巴结中的口腔状细胞癌 (OSCC) 微转移. 这种人工智能工具提高了诊断准确度,并有助于为OSCC患者规划治疗.
科学领域:
- 在瘤学瘤学.
- 数字病理学数字病理学
- 人工智能在医学中的应用
背景情况:
- 口腔状细胞癌 (OSCC) 发病率在全球范围内不断上升,宫淋巴结转移是关键的预后因素.
- 手动显微镜检查微转移是耗时的,劳动密集的,容易出现错误的.
- 使用机器学习的自动检测可以克服手动病理分析的局限性.
研究的目的:
- 采用卷积神经网络 (CNN) 算法用于OSCC的淋巴结部分中自动检测微转移.
- 评估CNN模型与手动诊断方法的性能.
主要方法:
- 分析了30例OSCC病例的50个淋巴结档案组织切片,其中25例转移性和25例非转移性.
- 修改的帕帕尼科劳 (PAP) 染色被用于组织制备.
- 使用配备CCD摄像头的奥林巴斯研究显微镜获得500张图像的数据集.
主要成果:
- 与手动检测微转移相比,基于CNN的算法表现出更高的性能.
- 该模型实现了89.36%的验证准确度,85%的分类准确度,0.8667的灵敏度和0.8333.3的特异性.
- 通过CNN早期检测微转移,有助于3例瘤升级,影响OSCC治疗和预后.
结论:
- CNN模型表现出强大的区分能力 (ROC AUC 0.9056),证明其用于改善临床N0OSCC患者的诊断和治疗规划.
- CNN模型是病理学家的宝贵补充工具,提高了诊断效率和处理大量病理学数据的准确性.
- 这种人工智能驱动的方法支持简化疾病状况的识别和评估,特别有利于大规模的人口查.
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