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基于人工智能的质量控制与低资源环境中改善胎儿超声波图像质量有关:来自中国西部的现实世界多中心研究

Jianxin Zhao^1,2, Yao Tang³, Shengli Li⁴

¹National Office for Maternal and Child Health Surveillance of China, National Center for Birth Defect Surveillance of China, West China Second University Hospital, Sichuan University, Sec.3 No.17 South RenMin Road, Chengdu, Sichuan, 610041, China.

|March 5, 2026

概括

人工智能质量控制 (AI-QC) 在低资源环境中显著改善了胎儿超声波图像质量. 这种人工智能系统随着时间的推移表现出持续的改进,增强了医学成像的标准化.

科学领域:

医疗人工智能医疗人工智能
放射学和成像学放射学和成像学
全球健康全球健康

背景情况:

有限的现实世界证据存在于低资源环境 (LRS) 中的医疗AI应用.
在中国贵州省进行了一项基于人工智能的胎儿超声波质量控制 (AI-QC) 系统的多中心评估.

研究的目的:

评估AI-QC系统在改善LRS胎儿超声波图像质量的有效性.
评估AI在医学成像中的实际临床应用和影响.

主要方法:

实施了一个独立的,后检查AI-QC系统,将图像分类为标准,基本标准或非标准.
在36个月内从34家医院收集了61,959次胎儿超声检查的前性数据.
在考试和平面层面评估质量,分析标准化的时间趋势.

主要成果:

在所有扫描类型中,标准检查的比例显著增加 (例如,第一季度扫描从39.5%增加到82.1%).
大多数县在18-24个月内超过了60%的考试级标准化门.
具体平面质量的显著改善 (例如,跨平面从91%降至97%),并减少了缺陷.

结论:

基于人工智能的质量控制与低资源环境中改善胎儿超声波图像质量有关.
在研究期间观察到图像质量的持续改善.
需要进一步的研究来将改善的图像质量与诊断性能和围产期结果联系起来.

关键词:

人工智能在产科中的应用人工智能的人工智能胎儿超声波检查胎儿超声波检查低资源设置低资源设置质量控制质量控制质量控制

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