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人工智能治疗与药物相关的骨瘤:一个范围审查

Yuichi Mine¹, Shota Okazaki¹, Sachiko Yamasaki²

¹Project Research Center for Integrating Digital Dentistry, Hiroshima University, Hiroshima, Japan; Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Oral surgery, oral medicine, oral pathology and oral radiology

|May 20, 2025

概括

人工智能 (AI) 显示出预测和诊断与药物相关的骨 (MRONJ) 的前景. 需要进一步的研究来解决数据质量和临床整合对AI在MRONJ管理中的挑战.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
口腔和牙面部外科手术

背景情况:

与药物相关的下巴骨硬化 (MRONJ) 是一个重大的临床挑战.
目前对MRONJ的诊断和预测方法可以通过先进的技术进行改进.

研究的目的:

审查目前对人工智能 (AI) 应用的研究,用于MRONJ的预测,诊断和管理.
为了为MRONJ确定人工智能研究的趋势和差距.

主要方法:

在MRONJ中对AI应用进行了全面的范围审查.
在主要数据库 (PubMed/MEDLINE,Scopus,Web of Science) 进行了搜索,截至2024年12月.
数据提取侧重于人工智能任务,模型,数据集和性能指标.

主要成果:

包括8项研究,重点研究MRONJ发病预测 (5项研究),诊断 (2项研究) 和患者教育 (1项研究).
机器学习模型在MRONJ发病预测方面取得了很高的表现 (AUC 0.793-0.973).
深度学习模型在从放射图像中诊断MRONJ时表现出高准确度 (96%).

结论:

人工智能显示出改善MRONJ预测和诊断的巨大潜力.
在数据质量,模型验证和对MRONJ人工智能工具的临床整合方面仍然存在挑战.
未来的研究应该优先考虑标准化,可解释的AI模型和临床实施准则.

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