Prenatal Ultrasonogaphy
Study of extracting key plane of 11-13+ 6 weeks normal fetal palate by three-dimensional ultrasound based on artificial intelligence
Pan Wenxiong, Zhang Dandan, Pan Ruijuan, Huang Yuhao, Deng Shihua, Zhang Yuanji, Zheng Mali, Ni Dong, Li Mei, Xiong Yi
Published 2023-03-25
Cite as Chin J Ultrasonogr, 2023, 32(3): 227-233. DOI: 10.3760/cma.j.cn131148-20220811-00553
Abstract
ObjectiveTo explore the feasibility of extracting the key plane of the normal fetal palate on the 11-13+ 6 week from tomography ultrasonography imaging based on artificial intelligence.
MethodsThe fetal volume datas of 235 cases of 11-13+ 6 week normal fetal were collected from the Department of Ultrasound in the Luohu District People′s Hospital of Shenzhen and Huazhong University of Science and Technology Union Shenzhen Hospital from May 2020 to April 2021. The data acquisition was completed by sonographers A and B by using the GE Voluson E10 color Doppler ultrasound diagnostic instrument. All datas were marked offline by sonographer C. Tomographic imaging was performed on all included data by sonographer D, the tomographic images were saved and the time-consuming was recorded, and the datas of the sonographer group were obtained. The labeled data were randomly divided into the training set and test set for model transfer learning and testing.The 4-fold cross-validation was adopted to record the test set image output by the model and the time consumption to obtain the intelligent group data. A senior sonographer performed image analysis on the two groups of data images. The feasibility of the intelligent model was verified by comparing the score of the plane of retronasal triangle(RTP), the acquisition rate of RTP, the acquisition rate of the fault, and the time-consuming difference between the sonographer group and the intelligent group.
Results①There was no significant difference in the overall distribution of RTP scores between the sonographer group and intelligent group [5 (5, 6) points vs 5 (5, 6) points, Z=0.355, P=0.722]. The RTP acquisition rate of the sonographer group and intelligent group was not statistically significant (78.72% vs 76.60%, χ2=0.55, P=0.458). The consistency and correlation of RTP obtained by the two groups were high (Kappa=0.645, φ=0.646, both P<0.001). ②The effective layers of the sonographer group were 9 (8, 9) and the intelligent group was 8 (7, 9). The fault acquisition rate of the doctor group was higher than that of the intelligent group (78.72% vs 68.51%, χ2=12.52, P=0.001). The consistency and correlation of the two groups in obtaining faults were media (Kappa=0.503, φ=0.521, both P<0.001). ③The time-consuming of the intelligent group was significantly lower than that of the sonographer group [1.50 (1.23, 1.75)s vs 26.94 (22.28, 30.48)s, Z=11.440, P<0.001].
ConclusionsThis research model can quickly and accurately realize the extraction and tomography of the key plane of the normal fetal palate on the 11-13+ 6 week.
Key words:
Ultrasonography, three-dimensional; Fetus; Palate; Tomography; Artificial intelligence
Contributor Information
Pan Wenxiong
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
Zhang Dandan
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
Pan Ruijuan
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
Huang Yuhao
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound &
Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging &
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
Deng Shihua
Department of Ultrasound, Shenzhen Union Hospital of Huazhong University of Science and Technology, Shenzhen 518052, China
Zhang Yuanji
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
Zheng Mali
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
Ni Dong
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound &
Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging &
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
Li Mei
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
Xiong Yi
Department of Ultrasound, Shenzhen Luohu People′s Hospital, the Third Affiliated Hospital, Shenzhen University, Shenzhen 518020, China
