骨转移分析中的人工智能:当前的进展,机遇和挑战
Marwa Afnouch1, Fares Bougourzi2, Olfa Gaddour3
1Laboratory of IEMN, CNRS, Centrale Lille, UMR 8520, Univ. Polytechnique Hauts-de-France, F-59313, Valenciennes, France; CES-laboratory, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia.
Computers in biology and medicine
|June 4, 2025
概括
人工智能 (AI) 显示出在医学成像中分析骨转移的前景. 对数据多样性,可解释性和验证性的进一步研究对于瘤学的临床整合至关重要.
科学领域:
- 医学成像分析 医学成像分析
- 在瘤学中使用人工智能
- 放射学工作流的优化 工作流的优化
背景情况:
- 人工智能 (AI) 正在彻底改变医学成像,特别是检测骨转移 (BM),这是晚期癌症的一个关键问题.
- 机器学习和深度学习为BM检测,识别和细分提供了先进的功能,但面临着数据稀缺性和可解释性等挑战.
研究的目的:
- 审查人工智能 (AI) 方法和用于骨转移 (BM) 分析的应用,跨越各种医学成像模式.
- 检查数据集对AI开发对BM分析的影响.
主要方法:
- 在CT,MRI,PET,SPECT和骨光学中对骨转移分析的AI技术进行PRISMA指导的审查.
- 包括传统的机器学习和深度学习模型,包括卷积神经网络 (CNN) 和变压器.
- 评估现有数据集及其对该领域AI发展的影响.
主要成果:
- 人工智能模型,特别是CNN和变压器,在不同成像模式和任务的BM分析中表现出高性能.
- 持续存在的局限性包括数据不平衡,过度装配的风险,以及需要加强模型透明度.
- 临床翻译受到监管和验证挑战的阻碍.
结论:
- 人工智能具有显著的潜力,可以提高骨转移 (BM) 诊断,并改善瘤学中的放射学工作流程.
- 解决数据多样性,模型可解释性和进行大规模验证对于临床采用和信任至关重要.
关键词:
人工智能的人工智能是人工智能.骨转移发生在骨中.在美国,CNN是CNN.数据集数据集数据集深度学习是一种深度学习.没有了,没有了,没有了.机器学习是机器学习.医学成像分析分析 医学成像分析变压器变压器变压器更多相关视频
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