预测口腔潜在恶性疾病恶性转变风险的机器学习方法:系统性审查
Simran Uppal1, Priyanshu Kumar Shrivastava1, Atiya Khan1
1Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
International journal of medical informatics
|March 29, 2024
概括
机器学习模型准确地预测了口腔潜在恶性疾病 (OPMDs) 中的恶性转变. 这些人工智能工具具有高灵敏度和特异性,有助于风险分层,以更好地管理患者.
科学领域:
- 口腔医学是指口腔医学.
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 口腔潜在恶性疾病 (OPMDs) 是一组不同的疾病,具有发展为口腔癌的显著风险.
- 准确的OPMD风险分层对于确定适当的患者管理策略至关重要,区分高风险和低风险病例.
- 机器学习 (ML) 在各种牙科应用中显示出显著的前景,这表明它在预测癌前口腔病变恶性转变方面的潜在实用性.
结论:
- 机器学习是OPMD风险预测的宝贵工具,提供更高的灵敏度,自动化分析和更高的准确性.
- ML有助于整合多个变量来监测OPMD的进展和预测恶性潜在.
- 优化输入参数对于最大限度地提高分类器效率至关重要,因为ML对数据集特征敏感.
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