Automatic recognition of fetal facial ultrasound standard plane using artificial intelligence(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第12期
- Page:
- 1575-1578
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Automatic recognition of fetal facial ultrasound standard plane using artificial intelligence
- 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
- Keywords:
- Keywords: artificial intelligence ultrasonography fetal facial ultrasound standard plane quality control
- PACS:
- R318；R445.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2021.12.021
- 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.
Last Update: 2021-12-24