医疗图像质量评估的系统审查
H M S S Herath1, H M K K M B Herath1, Nuwan Madusanka2
1Department of Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Journal of imaging
|April 25, 2025
概括
高质量的医学成像对于准确的诊断和有效的患者护理至关重要. 本综述强调了AI支持的医学图像质量评估 (MIQA) 如何提高诊断精度和效率,解决当前面临的挑战,以获得更好的医疗保健结果.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 诊断的准确性 诊断的准确性
背景情况:
- 医学图像质量评估 (MIQA) 对于可靠的诊断,治疗计划和患者的治疗结果至关重要.
- 目前的MIQA方法包括主观手动评估和客观规则驱动的评估.
- 在临床实践中,对一致,高质量的医学成像的需求至关重要.
研究的目的:
- 审查各种MIQA方法及其对诊断性能,患者结果和工作流程效率的影响.
- 探索机器智能和机器学习 (ML) 在MIQA自动化中的作用.
- 确定和讨论MIQA现有的挑战,并提出标准化解决方案.
主要方法:
- 对42项关于MIQA各种方法的研究研究进行了系统审查.
- 主观 (手动) 与客观 (自动) 图像质量评估技术的比较.
- 对人工智能驱动的工具进行分析,用于自动化质量检查,降低噪音和删除文物.
主要成果:
- 人工智能驱动的MIQA工具在自动化质量控制,提高图像一致性和减少文物方面显示出巨大潜力.
- 图像质量的提高与诊断精度的提高和更好的临床决策直接相关.
- 挑战包括图像质量的变化,人类评分器的不一致性和小型数据集,阻碍了标准化.
结论:
- 高质量的医学成像,由先进的MIQA促进,是现代医疗保健不可或缺的.
- 人工智能和机器学习为自动化MIQA,提高诊断准确性和简化临床工作流提供了变革性的潜力.
- 通过更好的数据,具有成本效益的标签和协作研究来解决标准化挑战,对于推进医疗保健至关重要.
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