所有你需要的是数据准备:在医疗支持系统的多中心/设备研究中对图像协调技术的系统审查
Silvia Seoni1, Alen Shahini1, Kristen M Meiburger1
1Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
Computer methods and programs in biomedicine
|April 27, 2024
概括
图像协调标准化用于人工智能 (AI) 分析的多源医疗成像数据. 这一系统性审查表明,协调技术可以改善各种成像模式的AI性能,从而实现更强大的医疗保健洞察力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 多中心和多设备研究提供了强大的AI洞察力,但由于各种采集协议而面临数据不一致.
- 图像数据的变化阻碍了人工智能分析多源数据集的有效聚合.
- 图像协调对于标准化外观至关重要,并使各种医学成像数据的可靠AI分析成为可能.
研究的目的:
- 在多中心和多设备医学成像研究中系统审查图像协调策略.
- 评估不同图像协调技术在改进AI分析方面的有效性.
- 确定协调方法和应用领域的趋势.
主要方法:
- 根据PRISMA指南对2013年至2023年间发表的论文进行了文献搜索.
- 专注于研究分析利用图像协调的多中心和多设备医学成像.
- 分析了常见的协调技术及其对AI性能的影响.
主要成果:
- 灰度标准化使分类准确度提高了高达24.42%的程度.
- 重新采样使强大的放射性特征从59.5%增加到89.25%.
- 图像协调始终提高了跨模式的AI性能,提高了分类准确性和细分的子得分.
结论:
- 图像协调是推动医疗保健人工智能的关键策略,它可以对集成的多源数据集进行可靠的分析.
- 在临床环境中对成像数据的标准化支持个性化,基于证据的护理.
- 协调减轻了偏见,并释放了数据驱动的医疗保健技术的潜力.
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