Automatic bone maturity grading from EOS radiographs in Adolescent Idiopathic Scoliosis

Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence

PURPOSE

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS.

METHODS

This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS.

RESULTS

40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%.

CONCLUSION

This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management.

Normal spinopelvic parameters and correlation analysis in 217 asymptomatic children

BACKGROUND

Understanding spinal sagittal balance is crucial for assessing and treating spinal deformities in pediatric populations.

OBJECTIVE

The aim of the present observational study is to examine the parameters of sagittal alignment of the regional spine and spinopelvic region in asymptomatic pediatric populations and the characteristics of these parameters with age and sex.

METHODS

We enrolled 217 participants, consisting of 112 males (51.6%) and 105 females (48.4%), aged between 4 and 15 years, with an average age of 12.19 years. Pelvic incidence, pelvic tilt, sacral slope, lumbar lordosis, thoracic kyphosis, T1 slope, C7 slope, cervical sagittal vertical axis, and C2-7 Cobb angle were measured. Three spine surgeons conducted radiographic measurements utilizing the PACS software. The measurement reliability was assessed through ICCs.

RESULTS

Our results show significant age-related changes in pelvic tilt and cervical sagittal vertical axis, with notable gender differences in pelvic tilt, lumbar lordosis, and thoracic kyphosis. Girls have larger PT, boys have larger cSVA. PI, PT, and cSVA also differ among different age groups. Correlation analysis shows that a series of relationships that align with adult population patterns between pelvic incidence, pelvic tilt, sacral slope, lumbar lordosis, and thoracic kyphosis.

CONCLUSION

Significant variations in PT and cSVA across diverse age cohorts highlights notable disparities in the distribution of PT and cSVA values within the pediatric population. Gender-based differences in PT, LL, and TK and correlation in spinopelvic parameter could enhances our understanding of compensatory mechanisms.