Radiography is the main tool used to assess spinal deformities in patients with adolescent idiopathic scoliosis (AIS) but requires repeated exposure to ionising radiation, potentially raising safety concerns. Consequently, new methods are needed. Among them, the acquisition of static parameters during quantitative gait analysis (QGA) has received attention in recent years. However, no data on correlations linking static QGA parameters to standard radiographic parameters are available. The objective of this study was to assess correlations between static QGA parameters and standard radiographic parameters in patients with AIS.

Spinal deformities in patients with AIS can be evaluated based on static QGA parameters.

We studied patients scheduled for surgery to treat AIS. On the day before surgery, QGA was performed and antero-posterior and lateral radiographs obtained. QGA involved analysing the positions of 32 reflective markers, including 6 used to assess the spine. The coronal vertical axis (CVA), thoracic and lumbar Cobb angles, sagittal vertical axis (SVA), thoracic kyphosis, lumbar lordosis, and pelvic tilt were recorded and compared to the corresponding radiographic parameters.

The study included 57 patients, including 48 (84%) females, with a mean age of 15.2 years. Among them, 45 had Lenke 1 and 12 Lenke 5 scoliosis. The mean main Cobb angle was 51.9°. In the coronal plane, significant correlations with the corresponding radiographic parameters were found for the CVA (R=0.524, p<0.01) and thoracic Cobb angle (R=0.599, p<0.01). All the sagittal parameters correlated significantly with the corresponding radiographic parameters: SVA, R=0.313; pelvic tilt, R=0.342; thoracic kyphosis, R=0.575; and lumbar lordosis, R=0.360 (p<0.05 for all four parameters).

In this study, static QGA parameters accurately reflected the spinal deformities visualised radiographically. The lumbar deformity was more difficult to characterise, probably because it was mild in our population. Research efforts should focus on improving the performance of QGA, notably for detecting curve progression. Thus, QGA may allow a decrease in radiation exposure of patients with AIS.

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