一种用于诊断口腔潜在恶性疾病的深度学习方法
1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China; Institute of Wenzhou, Zhejiang University, Wenzhou, China.
Journal of dentistry
|September 28, 2025
概括
一种新的两阶段深度学习方法显著改善了口腔潜在恶性疾病 (OPMDs) 的诊断,超过了临床医生. 这种人工智能工具提高了诊断的准确性,特别是对于初级临床医生来说,有助于早期检测和患者的结果.
科学领域:
- 口腔病理学和数字诊断.
- 医疗成像中的人工智能.
- 临床决策支持系统.
背景情况:
- 口腔潜在恶性疾病 (OPMDs) 需要准确和及时的诊断以进行有效的管理.
- 目前的诊断方法在很大程度上依赖于临床医生的专业知识,这可能会有所不同.
- 早期发现OPMD对于预防恶性转变至关重要.
研究的目的:
- 开发和验证一个两阶段深度学习 (AI) 方法来诊断OPMDs.
- 将AI方法的性能与不同经验水平的临床医生进行比较.
- 评估AI作为临床决策支持工具的实用性.
主要方法:
- 创建了一个两阶段的深度学习模型:一个用于区分OPMD与其他病变 (CLA OPMD-OOML),另一个用于分类OPMD亚型 (CLA OPMD).
- 该模型在一个大型内部数据集 (ZJUSS,n=3,305) 上进行了训练,并在两个外部多中心数据集 (WCHS和CS-SJTU,n=1,756) 上进行了验证.
- 九名临床医生 (初级,中级,高级) 在一个盲目的比较研究中,在人工智能协助下独立进行诊断.
主要成果:
- 人工智能方法在诊断OPMD方面显著优于所有临床医师组,获得更高的F1得分 (例如,CLA OPMD的92.2%与临床医师的77.7-80.0%相比).
- 该方法在外部数据集中显示出强大的通用性.
- 人工智能辅助提高了初级和中级临床医生的诊断精度,分别为10.8%和5.9%,将初级临床医生的表现提升到高级水平.
结论:
- 开发的两阶段深度学习方法实现了与经验丰富的OPMD临床医生相比或超过的诊断性能.
- 这种人工智能作为一种强大的辅助工具,大大提高了初级和中级临床医生的诊断能力.
- 人工智能方法显示出大规模OPMD查的潜力,特别是在服务不足的地区,并作为临床医生的培训/决策支持系统.
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