[1]刘中华,王小莉,吕国荣,等.人工智能自动识别胎儿颜面部超声标准切面的研究[J].中国医学物理学杂志,2021,38(12):1575-1578.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.021]
 LIU Zhonghua,WANG Xiaoli,et al.Automatic recognition of fetal facial ultrasound standard plane using artificial intelligence[J].Chinese Journal of Medical Physics,2021,38(12):1575-1578.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.021]
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人工智能自动识别胎儿颜面部超声标准切面的研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第12期
页码:
1575-1578
栏目:
医学人工智能
出版日期:
2021-12-24

文章信息/Info

Title:
Automatic recognition of fetal facial ultrasound standard plane using artificial intelligence
文章编号:
1005-202X(2021)12-1575-04
作者:
刘中华12王小莉3吕国荣25杜永兆234柳培忠234吴秀明1何韶铮5
1.福建医科大学附属泉州第一医院超声科, 福建 泉州 362000; 2.泉州医学高等专科学校母婴健康服务应用技术协同创新中心,福建 泉州 362000; 3.华侨大学医学院, 福建 泉州 362000; 4.华侨大学工学院, 福建 泉州 362000; 5.福建医科大学附属第二医院超声科, 福建 泉州 362000
Author(s):
LIU Zhonghua1 2 WANG Xiaoli3 L?Guorong2 5 DU Yongzhao2 3 4 LIU Peizhong2 3 4 WU Xiuming1 HE Shaozheng5
1. Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China 2. Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362000, China 3. School of Medicine, Huaqiao University, Quanzhou 362000, China 4. Engineering Institute, Huaqiao University, Quanzhou 362000, China 5. Department of Ultrasound, the Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
关键词:
人工智能超声检查胎儿颜面部超声标准切面质量控制
Keywords:
Keywords: artificial intelligence ultrasonography fetal facial ultrasound standard plane quality control
分类号:
R318;R445.1
DOI:
DOI:10.3969/j.issn.1005-202X.2021.12.021
文献标志码:
A
摘要:
目的:探讨人工智能(AI)自动识别与分类胎儿颜面部超声标准切面(FFUSP)的价值。方法:以妊娠20~24周FFUSP图像为研究对象,含标准集1 906张和实验集4 532张。标准集分为训练集和测试集用于训练和测试AI模型识别分类鼻唇冠状切面、正中矢状面、经双眼球横切面及非标准切面;以产科超声专家分类为标准,比较分析AI、初级医生组、中级医生组对实验集FFUSP图像识别分类能力差异。结果:AI对测试集各切面的分类准确率均达97%以上,中级医生对实验集FFUSP各切面识别能力皆优于初级医生(P<0.05)。AI对FFUSP各切面总体识别效能优于初级医生和中级医生(P<0.05),与专家分类一致性强(P<0.05);AI分类效率显著优于医生人工分类(P<0.05)。结论:AI对FFUSP识别分类具有较高准确性,可作为胎儿超声规范化培训和图像质量控制的辅助方法。
Abstract:
Abstract: Objective To explore the value of artificial intelligence (AI) for automatically identifying and classifying fetal facial ultrasound standard plane (FFUSP). Methods The FFUSP at 20-24 weeks gestation were taken as the research object, including 1 906 images in standard set and 4 532 images in experimental set. The images in standard set were further divided into training set and test set for training and testing the ability of AI in recognizing and classifying nasolabial coronal plane, median sagittal plane, ocular axial plane and non-standard plane, respectively. Taking the classification by obstetric ultrasound experts as the standard, the differences among AI, junior doctors and intermediate doctors in the?ecognition and classification of FFUSP in experimental set were compared and analyzed. Results The classification accuracy of AI on each kind of planes in test set was higher than 97%. Intermediate doctors surpassed junior doctors in the recognition of FFUSP in experimental set (P<0.05). AI was superior to junior doctors and intermediate doctors in the total recognition efficiency of FFUSP (P<0.05), and had a strong consistency with the classification results obtained by experts (P<0.05). The classification efficiency of AI was significantly better than the artificial classification by doctors (P<0.05). Conclusion AI which has a high accuracy in FFUSP identification and classification can be used as an assistant method for fetal ultrasonic standardized training and image quality control.

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备注/Memo

备注/Memo:
【收稿日期】2021-06-16 【基金项目】福建省自然科学基金项目(2021J011404);福建省科技重大专项(2020HZ02014);教育部泉州医学高等专科学校母婴健康服务应用技术协同创新中心经费资助项目[闽科教(2017)49号] 【作者简介】刘中华,副主任医师,主要研究方向:产科超声、介入超声,E-mail: liuzhonghua2005@126.com 【通信作者】吕国荣,教授,主任医师,研究方向:产科超声、介入超声,E-mail: lgr_feus@sina.com
更新日期/Last Update: 2021-12-24