Clinical Research
Study on the practicality of the semi-automatic measurement for the urogenital hiatus
Tingting Ye, Huifang Wang, Hua Chen, Dong Ni, Qiuxiang Chen, Xiaoshuang Deng, Min Wu
Published 2019-03-25
Cite as Chin J Ultrasonogr, 2019, 28(3): 256-260. DOI: 10.3760/cma.j.issn.1004-4477.2019.03.013
Abstract
ObjectiveTo determine the consistency of urogenital hiatus (UH) data between the semi-automatic measurement and manual measurement using transperineal pelvic floor ultrasonography.
MethodsTotal of 286 three-dimensional images of minimal UH dimension were obtained. And they were divided into study group (100 images) and test group (186 images) randomly. Three experts traced and created the whole profile of the UH of those images in the study group by MATLAB. Then the semi-automatic software was obtained through machine learning algorithms. In the test group, 6 parameters of UH (including anterioposterior diameter, transverse diameter, circumference, area, left and right levator urethral gap distance) were measured by two experts (D1 and D2) both manually and semi-automatically. The time experts spent on measuring was also recorded and compared.
ResultsThe time used for semi-automatic measurement was significantly shorter than that for manual measurement[ (7.49±1.51)s vs (42.42±11.08)s, (7.52±1.37)s vs (43.45±9.09)s for D1 and D2, t=-12.09, -13.64, all P=0.00]. The Pearson correlation coefficients between semi-automatic and manual measurements of 6 parameters were 0.857-0.985 (P<0.01), 0.853-0.979 (P<0.01) in D1 and D2, respectively. The interclass correlation coefficients (ICC) of six parameters were ranged from 0.846-0.985 for D1 and 0.843~0.979 for D2(all P<0.01). The Bland Altman plot also showed good agreement between two methods.
ConclusionsIntellectual recognition and semi-automatic measurement has simplified the process for UH measurement, and it is proved to be a reliable and timesaving method that is practical for clinical use.
Key words:
Ultrasonography, transperineal; Urogenital hiatus; Intelligent identification; Semi-automatic measurement; artificial intelligence
Contributor Information
Tingting Ye
Department of Ultrasound, Shenzhen Second People′s Hospital, Shenzhen 518035, China
Huifang Wang
Department of Ultrasound, Shenzhen Second People′s Hospital, Shenzhen 518035, China
Hua Chen
Department of Ultrasound, Shenzhen Second People′s Hospital, Shenzhen 518035, China
Dong Ni
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
Qiuxiang Chen
Department of Ultrasound, Shenzhen Second People′s Hospital, Shenzhen 518035, China
Xiaoshuang Deng
Department of Ultrasound, Shenzhen Second People′s Hospital, Shenzhen 518035, China
Min Wu
Department of Ultrasound, Shenzhen Second People′s Hospital, Shenzhen 518035, China
