2025 · The Spine Journal
척추가 옆으로 휜 영상에서도 골반과 요추 각도를 자동 측정할 수 있을까?
반복 측정 ICC는 모든 각도에서 1.00이었습니다. 기준값과의 일치도 및 교차검증 ICC는 0.96–0.99였습니다.
척추·골반 각도를 읽는 측면 X-ray
그림 크게 보기LL·PI·PT·SS와 기준점을 표시한 측면 X-ray입니다.
CONNECTEVE · CONNEVO SPIN · Owner-supplied product brochure
연구 수치로 읽는 결과
그림 크게 보기시험군 113명 중 퇴행성 측만증 환자는 39명이었습니다. LL·PI·PT·SS 측정은 분류 진단과 다른 과제입니다. 같은 영상의 반복 ICC 1.00과 전문의 기준값과의 일치도를 구분해야 합니다.
The Spine Journal (2025). DOI: 10.1016/j.spinee.2025.01.020 · Original explanatory chart based on reported data
연구에서 묻는 질문
대상과 방법
주요 결과
진료와 수술에서 읽을 지점
해석의 제한
짧게 정리하면
어떤 진료 질문과 연결되나요?
Spinopelvic alignment의 반복 측정 부담을 줄일 수 있습니다. PI–LL mismatch와 보상성 PT를 읽을 때 영상 품질, 대퇴골두 인식과 개별 오차를 먼저 확인해야 합니다.
Keywords · 주제어
- Artificial intelligence
- Convolutional neural network
- Coronal deformity
- Keypoint detection
- Pelvic parameter
- Sagittal alignment
- Segmentation
- Neural Networks, Computer
Abstract · 원문 초록
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.
원래 논문
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