• Weiying Chen, Mahdieh Khodaei, Marek Reformat, and Edmond Lou.
• Department of Electrical and Computer Engineering, University of Alberta, Donadeo ICE 11-263, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada.
• Eur Spine J. 2024 Jun 27.

PurposeTo validate a fast 3D biplanar spinal radiograph reconstruction method with automatic extract curvature parameters using artificial intelligence (AI).MethodsThree-hundred eighty paired, posteroanterior and lateral, radiographs from the EOS X-ray system of children with adolescent idiopathic scoliosis were randomly selected from the database. For the AI model development, 304 paired images were used for training; 76 pairs were employed for testing. The validation was evaluated by comparing curvature parameters, including Cobb angles (CA), apical axial vertebral rotation (AVR), kyphotic angle (T1-T12 KA), and lordotic angle (L1-L5 LA), to manual measurements from a rater with 8 years of scoliosis experience. The mean absolute differences ± standard deviation (MAD ± SD), the percentage of measurements within the clinically acceptable errors, the standard error of measurement (SEM), and the inter-method intraclass correlation coefficient ICC[2,1] were calculated. The average reconstruction speed of the 76 test images was recorded.ResultsAmong the 76 test images, 134 and 128 CA were exported automatically and measured manually, respectively. The MAD ± SD for CA, AVR at apex, KA, and LA were 3.3° ± 3.5°, 1.5° ± 1.5°, 3.3° ± 2.6° and 3.5° ± 2.5°, respectively, and 98% of these measurements were within the clinical acceptance errors. The SEMs and the ICC[2,1] for the compared parameters were all less than 0.7° and > 0.94, respectively. The average time to display the 3D spine and report the measurements was 5.2 ± 1.3 s.ConclusionThe developed AI algorithm could reconstruct a 3D scoliotic spine within 6 s, and the automatic curvature parameters were accurately and reliably extracted from the reconstructed images.© 2024. The Author(s).

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