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KMID : 1120320240100010022
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Osteoporosis and Sarcopenia
2024 Volume.10 No. 1 p.22 ~ p.27
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Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study
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Huy G. Nguyen
Hoa T. Nguyen
Linh T.T. Nguyen
Thach S. Tran
Lan T. Ho-Pham
Sai H. Ling
Tuan V. Nguyen
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Abstract
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Objectives: Vertebral fracture is both common and serious among adults, yet it often goes undiagnosed. This study aimed to develop a shape-based algorithm (SBA) for the automatic identification of vertebral fractures.
Methods: The study included 144 participants (50 individuals with a fracture and 94 without a fracture) whose plain thoracolumbar spine X-rays were taken. Clinical diagnosis of vertebral fracture (grade 0 to 3) was made by rheumatologists using Genant’s semiquantitative method. The SBA algorithm was developed to determine the ratio of vertebral body height loss. Based on the ratio, SBA classifies a vertebra into 4 classes: 0 = normal, 1 = mild fracture, 2 = moderate fracture, 3 = severe fracture). The concordance between clinical diagnosis and SBAbased classification was assessed at both person and vertebra levels.
Results: At the person level, the SBA achieved a sensitivity of 100% and specificity of 62% (95% CI, 51%?72%).
At the vertebra level, the SBA achieved a sensitivity of 84% (95% CI, 72%?93%), and a specificity of 88% (95% CI, 85%?90%). On average, the SBA took 0.3 s to assess each X-ray.
Conclusions: The SBA developed here is a fast and efficient tool that can be used to systematically screen for
asymptomatic vertebral fractures and reduce the workload of healthcare professionals.
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KEYWORD
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Artificial intelligence, X-ray, Vertebra segmentation, Vertebral fracture, Shape-based algorithm
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