Research of intelligent model for automatically counting the number of vertebral ossification center below the end of conus medullaris
10.3760/cma.j.cn131148-20240226-00113
- VernacularTitle:胎儿脊髓圆锥末端尾侧椎体骨化中心自动计数模型研究
- Author:
Zhiwei GUO
1
;
Huaxuan WEN
;
Dandan LUO
;
Bocheng LIANG
;
Guanghua TAN
;
Hongjie ZHANG
;
Ying TAN
;
Ying YUAN
;
Shengli LI
Author Information
1. 南方医科大学第一临床医学院，广州 510515
- Keywords:
Ultrasonography;
Artificial intelligence;
Fetus;
Conus medullaris;
Spina bifida
- From:
Chinese Journal of Ultrasonography
2024;33(8):677-682
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and test the intelligent model for automatically counting the number of vertebral ossification centers below the end of conus medullaris.Methods:From January 2021 to October 2022, 3 000 ultrasound images of the sacrococcygeal spinal middle sagittal plane were retrospectively selected from Shenzhen Maternal and Child Healthcare Hospital and Zhuhai People′s Hospital. The vertebral ossification center and spinal conus medullaris were artificially fine-marked in 2, 800 images for segmentation training. Yolov8 algorithm was used to build the segmentation model for segmentation training, and the fitting and automatic counting of vertebral ossification centers were carried out by post-processing. In the other 200 planes, the counting was performed by the artificial intelligence (AI) model, attending physician (D1), and junior physician (D2), and the accuracy of their performance were evaluated by a specialist physician. The accuracy and the time spent between D1, D2, and AI were compared.Results:The accuracy of AI model segmentation fitting and counting reached 95.00% (190/200) by the specialist physician evaluation, which was almost equal to 94.50%(189/200) by D1( P=0.823) and higher than that of 88.50% by D2(177/200)( P=0.012). The counting time spent for D1, D2, and AI model were 5.00 (4.25, 6.00)s, 7.00 (7.00, 8.00)s, 0.09 (0.08, 0.10)s, respectively, showing that the time spent by AI model was significantly shorter than that of doctors(all P<0.001). Conclusions:The trained artificial intelligence model can efficiently and accurately complete the vertebral ossification center counting below the end of conus medullaris, equivalent to the level of attending physicians. This study is expected to be further applied in the screening of fetal spina bifida and improve the automation and intelligence level of prenatal ultrasound screening.
