R.Du Hyun RoOrthopedics & Research
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Imaging AISpinePelvic parameters

2025 · The Spine Journal

Can Pelvic and Lumbar Angles Be Measured Automatically on Radiographs of a Laterally Curved Spine?

Repeat-measurement ICC was 1.00 for every angle. Agreement with reference measurements and cross-validation ICCs were 0.96–0.99.

Lateral X-ray illustrating spinopelvic angles

Spinopelvic measurement example from CONNEVO SPIN product materialsEnlarge Figure

A lateral X-ray showing LL, PI, PT, SS and the reference landmarks.

CONNECTEVE · CONNEVO SPIN · Owner-supplied product brochure

Evidence & Original SourceImage use terms

Results explained with study numbers

Of 113 test patients, 39 had degenerative scoliosis. LL, PI, PT and SS measurement differs from diagnostic classification. Repeat ICC 1.00 for the same image must be distinguished from agreement with specialist reference values.Enlarge Figure

Of 113 test patients, 39 had degenerative scoliosis. LL, PI, PT and SS measurement differs from diagnostic classification. Repeat ICC 1.00 for the same image must be distinguished from agreement with specialist reference values.

The Spine Journal (2025). DOI: 10.1016/j.spinee.2025.01.020 · Original explanatory chart based on reported data

Evidence & Original SourceImage use terms

The research question

Can Pelvic and Lumbar Angles Be Measured Automatically on Radiographs of a Laterally Curved Spine?

Participants and Methods

U-Net segmentation and Keypoint R-CNN were combined to calculate LL, PI, PT, and SS. Results were compared with specialist reference values and two readers, using Bland–Altman analysis, ICC, and 5-fold cross-validation. 1,011 standing lateral lumbar radiographs. Of 113 test patients, 39 (34.5%) had degenerative lumbar scoliosis.

Main Findings

The repeat-measurement ICC was 1.00 for all angles. ICCs for agreement with the reference values and cross-validation were 0.96–0.99. The mean errors in Figure 2 for the test set were PI 0.23°, LL 0.05°, SS 0.03°, and PT 0.20°, but the range of individual errors differed by angle. Errors and agreement in the degenerative scoliosis subgroup differed from those in the general group. Figure 3 presents both accurate examples and examples with large errors.

Implications for clinical and surgical practice

Automatic measurement may reduce the burden of repeatedly measuring spinopelvic alignment. When interpreting PI–LL mismatch and compensatory PT, first check image quality, femoral-head detection, and errors in the individual image.

Limitations

This retrospective single-center study needs external validation using other equipment and institutions. A repeatability ICC of 1.00 means that the same input produces the same output; it does not mean that every image has an accurate angle measurement. The 39 participants with scoliosis in the test set had a mean Cobb angle of 14.8°. The results cannot be generalized to all severe deformities or postoperative images.

Let's put it briefly.

Which clinical question does this address?

Automatic measurement may reduce the burden of repeatedly measuring spinopelvic alignment. When interpreting PI–LL mismatch and compensatory PT, first check image quality, femoral-head detection, and errors in the individual image.

Keywords

Abstract · Original text

Dong-Ho Kang, Ye-Jin Jeong, Sung Taeck Kim, Younguk Kim, Bong-Soon Chang, Hyoungmin Kim, Sam Yeol Chang, Du Hyun Ro. Automated measurement of pelvic parameters using convolutional neural network in complex spinal deformities: overcoming challenges in coronal deformity cases. The spine journal : official journal of the North American Spine Society. 2025;25(8):1688-1697. doi:10.1016/j.spinee.2025.01.020.

BACKGROUND CONTEXT

Accurate and consistent measurement of sagittal alignment is challenging, particularly in patients with severe coronal deformities, including degenerative lumbar scoliosis (DLS).

PURPOSE

This study aimed to develop and validate an artificial intelligence (AI)-based system for automating the measurement of key sagittal parameters, including lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope, with a focus on its applicability across a wide range of deformities, including severe coronal deformities, such as DLS.

DESIGN

Retrospective observational study.

PATIENT SAMPLE

A total of 1,011 standing lumbar lateral radiographs, including DLS.

OUTCOME MEASURE

Interclass and intraclass correlation coefficients (CC), and Bland-Altman plots.

METHODS

The model utilizes a deep-learning framework, incorporating a U-Net for segmentation and a Keypoint Region-based Convolutional Neural Network for keypoint detection. The ground truth masks were annotated by an experienced orthopedic specialist. The performance of the model was evaluated against ground truth measurements and assessments from two expert raters using interclass and intraclass CC, and Bland-Altman plots.

RESULTS

In the test set of 113 patients, 39 (34.5%) had DLS, with a mean Cobb's angle of 14.8°±4.4°. The AI model achieved an intraclass CC of 1.00 across all parameters, indicating perfect consistency. Interclass CCs comparing the AI model to ground truth ranged from 0.96 to 0.99, outperforming experienced orthopedic surgeons. Bland-Altman analysis revealed no significant systemic bias, with most differences falling within clinically acceptable ranges. A 5-fold cross-validation further demonstrated robust performance, with interclass CCs ranging from 0.96 to 0.99 across diverse subsets.

CONCLUSION

This AI-based system offers a reliable and efficient automated measurement of sagittal parameters in spinal deformities, including severe coronal deformities. The superior performance of the model compared with that of expert raters highlights its potential for clinical applications.

Read abstract on PubMed

The original paper.

Automated measurement of pelvic parameters using convolutional neural network in complex spinal deformities: overcoming challenges in coronal deformity cases

Kang DH, Jeong YJ, Kim ST, Kim Y, Chang BS, Kim H, Chang SY, Ro DH
The Spine Journal · 2025

DOI · 10.1016/j.spinee.2025.01.020PubMedBased data
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