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恶性和非恶性口腔病变的分类和诊断使用深度神经网络

Viduni Liyanage¹, Mengqiu Tao², Joon Soo Park³

¹International Research Collaborative - Oral Health and Equity, The University of Western Australia, Crawley, Western Australia, Australia; UWA Dental School, The University of Western Australia, Nedlands, Western Australia, Australia.

Journal of dentistry

|August 13, 2023

概括

这项研究表明,人工智能 (AI) 智能手机应用程序可以帮助诊断口腔病变,特别是在偏远的人群中. 为了准确的非恶性病变分类,需要进一步改进人工智能模型.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
口腔病理学口腔病理学

背景情况:

口腔癌的发病率正在上升,需要为偏远的高风险社区提供可访问的诊断工具.
目前的视觉诊断方法在服务不足的地区可能缺乏负担能力和用户友好性.

研究的目的:

探索智能手机应用程序的实用性和可行性,用于口腔病变的视觉诊断.
评估AI模型在分类口腔病变中的准确性.

主要方法:

利用教科书中的342张口腔病变图像,分为非新生病,良性新生病和前恶性/恶性.
使用MobileNetV3和EfficientNetV2人工智能模型进行图像分析.
生成精度曲线,混矩阵和ROC曲线来评估模型性能.

主要成果:

EfficientNetV2实现了71%的验证准确度,分别为64%和80%的测试准确度,对非新发性和前恶性/恶性病变进行测试.
移动NetV3达到70%的验证准确度,显示了64%和82%的测试准确度,分别针对非新生病变和前恶性/恶性病变.
这两种模型都显示出区分恶性病变的潜力.

结论:

人工智能软件有望帮助查和识别恶性口腔病变,特别是在偏远地区.
进一步完善模型和分类系统对于提高非恶性病变诊断的准确性至关重要.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.诊断诊断诊断诊断诊断机器学习是机器学习.口腔癌是口腔癌,口腔癌是口腔癌.

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