一个系统的文献综述:探索医疗成像组合模型的挑战
Muhamad Rodhi Supriyadi1,2, Azurah Bte A Samah3, Jemie Muliadi2
1Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia.
BMC medical imaging
|April 18, 2025
概括
合体模型可以提高医学成像的疾病诊断准确性. 本系统性审查突出了它们的潜力和局限性,用于未来临床应用研究.
科学领域:
- 医疗保健中的人工智能
- 医学成像分析 医学成像分析
- 计算病理学计算病理学
背景情况:
- 医学成像对于疾病诊断至关重要,但解释是耗时的,容易出现人为错误.
- 集合模型通过分析复杂的数据模式来提高诊断准确性.
- 挑战包括训练和维护组合模型所需的计算资源.
研究的目的:
- 系统地审查医疗成像组合模型的最新进展.
- 确定当前的方法和综合模型在疾病诊断中的应用.
- 突出医疗成像中合体模型的潜力和局限性.
主要方法:
- 进行了一次系统文献综述 (SLR).
- 在Scopus和Web of Science数据库中进行了搜索.
- 关键词包括"整体模型"和"医学成像",遵循PRISMA指南.
主要成果:
- 该审查分析了2019年至2024年间发表的75篇论文.
- 30篇论文的子集被批判性地检查了分类,方法和诊断应用.
- 通过医学成像识别疾病诊断使用集合模型的关键趋势被确定.
结论:
- 整体模型正在成为使用医学成像诊断疾病的有价值工具.
- 与传统方法相比,这些模型显示出更高的准确性.
- 需要进一步的研究来解决这些局限性,并优化整体模型的可扩展性.
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