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人工智能辅助的超声波在脏疾病:一个系统的审查
Xiaowen Liang1,2, Meng Du1,2, Zhiyi Chen1,2,3
1Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China.
Quantitative imaging in medicine and surgery
|June 7, 2023
概括
人工智能 (AI) 在脏超声波中显示出有前途的疾病诊断和图像细分. 然而,许多人工智能模型具有高偏差风险,需要提高数据质量和可靠临床使用的验证.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 为提高脏超声波性能提供了新的策略.
- 人工智能技术越来越多地应用于性超声波,用于图像细分和疾病诊断.
研究的目的:
- 澄清和分析人工智能辅助的脏疾病超声波研究的现状.
- 反映脏超声波领域内人工智能方法的进步.
主要方法:
- 以PRISMA 2020指南为指导的系统审查.
- 在2022年6月之前对人工智能辅助的脏超声波研究 (图像细分和疾病诊断) 进行查,来自PubMed和Web of Science.
- 使用准确度/子相似系数 (DICE),AUC,灵敏度,特异性和PROBAST进行偏差风险评估的评估.
主要成果:
- 从364篇文章中分析了38项研究,分为人工智能辅助诊断/预测 (28) 和图像细分 (10).
- 人工智能辅助诊断/预测研究显示,中位精度为0.88和AUC为0.96.
- 86%的人工智能辅助诊断/预测模型具有高偏差风险,归因于数据问题,小样本大小和不充分的验证.
结论:
- 人工智能是病超声诊断的潜在工具,但可靠性和可用性需要改进.
- 未来的应用可能会专注于人工智能辅助的超声波用于慢性脏疾病和定量水缩症诊断.
- 进一步的研究必须优先考虑样本数据的大小和质量,严格的外部验证以及遵守指南.
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