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Assessment of Pelvic Tilt in Anteroposterior Radiographs by Area Ratio Based on Deep Learning.
Xian, Jianming; Sun, Jingwei; Xie, Ruimou; Yang, Fei; Huang, Jiaqi; Yuan, Kehong; Pan, Yu; Luo, Zhendong.
Affiliation
  • Xian J; Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, China.
  • Sun J; Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, China.
  • Xie R; Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
  • Yang F; School of Clinical Medicine, Tsinghua University, Beijing, China.
  • Huang J; Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
  • Yuan K; School of Clinical Medicine, Tsinghua University, Beijing, China.
  • Pan Y; Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, China.
  • Luo Z; Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong Province, China.
Article de En | MEDLINE | ID: mdl-38975768
ABSTRACT
STUDY

DESIGN:

Diagnostics.

OBJECTIVES:

Based on deep learning semantic segmentation model, we sought to assess pelvic tilt by area ratio of the lesser pelvic and the obturator foramen in anteroposterior (AP) radiographs.

BACKGROUND:

Pelvic tilt is a critical factor in hip and spinal surgery, commonly evaluated by medical professionals through sagittal pelvic radiographs. The inherent pelvic asymmetry, as well as potential obstructions from clothing and musculature in roentgenography, may result in ghosting and blurring artifacts, thereby complicating precise measurement.

METHODS:

PT directly affects the area ratio of the lesser pelvis to the obturator foramen in AP radiographs. An exponential regression analysis of simulated radiographs from ten male and ten female pelvises in specific tilt positions derived a formula correlating this area ratio with PT. Two blinded investigators evaluated this formula using 161 simulated AP pelvic radiographs. A deep learning semantic segmentation model was then fine-tuned to automatically calculate the area ratio, enabling intelligent PT evaluation. This model and the regression function were integrated for automated PT measurement and tested on a dataset of 231 clinical cases.

RESULTS:

We observed no disparity between males and females in the aforementioned area ratio. The test results from two blinded investigators analyzing 161 simulated radiographs revealed a mean absolute error of 0.19° (SD±4.71°), with a correlation coefficient between them reaching 0.96. Additionally, the mean absolute error obtained from testing 231 clinical AP radiographs using the fine-tuned semantic segmentation model mentioned earlier is -0.58° (SD±5.97°).

CONCLUSION:

We found that using deep learning neural networks enabled a more accurate and robust automatic measurement of PT through the area ratio of the lesser pelvis and obturator foramen.

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Spine (Phila Pa 1976) Année: 2024 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Spine (Phila Pa 1976) Année: 2024 Type de document: Article Pays d'affiliation: Chine