一个深度学习系统可以预测口腔白血病的表皮质质变形症
J Adeoye1, A Chaurasia2, A Akinshipo3
1Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China.
Journal of dental research
|October 9, 2024
概括
一种深度学习模型从照片中准确地预测口腔上皮质质变形,有助于早期检测口腔癌. 这种人工智能工具支持活检的临床决策,可能减少不必要的程序,并改善口腔白血病的患者监测.
科学领域:
- 口腔病理学和瘤学
- 医学中的人工智能
- 医学成像分析分析 医学成像分析
背景情况:
- 口腔白血病 (OL) 具有恶性转变为口腔癌的显著风险.
- 在OL中评估上皮质发育不良 (OED) 是至关重要的,但通常需要侵入性活检程序.
- 需要使用非侵入性方法来预测OED并指导诊断决策.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于使用口腔照片预测OED概率.
- 将DL模型的性能与临床医生对OED诊断的评估进行比较.
- 评估DL模型作为OL患者活检选择的决策支持工具的实用性.
主要方法:
- 一个具有EfficientNet-B2骨干的DL模型被训练在2,073个回顾性口腔白血病图像上.
- 组织病理学作为OED状态确定的黄金标准.
- 模型的性能通过内部验证,时间测试和与人类评分器比较的外部验证来评估.
主要成果:
- 在测试期间,DL模型表现出强的性能,曲线下面积高 (0.882),准确度平衡 (81.8%).
- 外部验证证实了良好的性能 (AUC 0.828,平衡精度76.4%) 并在OED分类中超过了92.3%的人类评分器.
- 该模型显示了指导活检决策的显著潜在净益,可能减少不必要的程序.
结论:
- 基于照片的DL模型可以准确预测口腔白血病的OED概率和状态.
- 该模型显示出作为决策支持工具的承诺,有助于选择患者进行活检,并可能实现自我监测.
- 这种人工智能方法为治疗口腔白血病及其恶性潜力的传统方法提供了一个非侵入性的补充.
相关概念视频
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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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01:30Classification of Epithelial Tissues: Simple Epithelium
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Because of the thinness of the cells, simple squamous epithelium is present where the rapid passage of chemical compounds is observed. For example, the endothelium that lines the capillaries and vessels...
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01:29Classification of Epithelial Tissues: Stratified Epithelium
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01:20Classification of Epithelial Tissues: Glandular Epithelium
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Multicellular glands are formed during early development when epithelial budding...
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01:24Cellular Adaptation IV: Dysplasia and Metaplasia
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