人工智能和放射学在上腺病变中的应用:系统性审查
Matteo Ferro1, Octavian Sabin Tataru2, Giuseppe Carrieri3
1Unit of Urology, Department of Health Science, ASST Santi Paolo and Carlo, University of Milan, Milan, Italy.
人工智能 (AI) 和放射学在特征上腺病变方面表现有前途,基于CT的放射学实现了高精度. 临床整合需要进一步验证.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 上腺病变经常被偶然发现,并带来诊断挑战.
- 在常规成像 (CT/MRI) 中,很难区分良性病变和恶性病变或激素活性病变.
- 人工智能 (AI) 和放射学有可能提高上腺损伤特征的准确性.
研究的目的:
- 系统地审查人工智能和放射学在特征上腺病变中的应用.
- 评估这些技术的诊断性能,方法和临床实用性.
- 评估人工智能和放射学在上腺损伤分析中的当前证据基础.
主要方法:
- 在PRISMA的指导下,系统地审查研究,直到2024年6月.
- 在PubMed,Scopus,Web of Science和Google Scholar中使用关键词进行了搜索:上腺损伤,AI,放射学和机器学习.
- 根据PICO标准,包括了11项涉及996名患者的回顾性研究.
主要成果:
- 基于CT的放射学在区分病变类型方面实现了0.88的平均AUC,顶级模型识别了生产阿尔多的腺瘤 (AUC:0.99).
- 基于MRI的放射学显示平均AUC为0.82,但在测试组中性能下降,表明过拟合.
- 混合 18F-FDG PET/CT 模型在检测转移性病变方面表现出高准确度 (AUC:0.97),人工智能应用在手术内导航方面显示出前景.
结论:
- 基于CT的放射学与MRI相比,在上腺损伤的表征方面表现优越,符合当前的成像指南.
- 人工智能和放射学在完善上腺损伤诊断方面具有重大潜力,并可能减少对侵入性手术的需求.
- 限制包括回顾性研究设计,小队列和协议变异性;前性验证和多中心合作对于临床翻译至关重要.
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