Bishara Anthony, Patel Saarang, Warman Anmol, Jo Jacob, Hughes Liam P, Khalifeh Jawad M, Azad Tej D
Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.
Spine Deform. 2025 May 23. doi: 10.1007/s43390-025-01111-1.
This review evaluates advances made in deep learning (DL) applications to automatic spinopelvic parameter estimation, comparing their accuracy to manual measurements performed by surgeons.
The PubMed database was queried for studies on DL measurement of adult spinopelvic parameters between 2014 and 2024. Studies were excluded if they focused on pediatric patients, non-deformity-related conditions, non-human subjects, or if they lacked sufficient quantitative data comparing DL models to human measurements. Included studies were assessed based on model architecture, patient demographics, training, validation, testing methods, and sample sizes, as well as performance compared to manual methods.
Of 442 screened articles, 16 were included, with sample sizes ranging from 15 to 9,832 radiograph images and reporting interclass correlation coefficients (ICCs) of 0.56 to 1.00. Measurements of pelvic tilt, pelvic incidence, T4-T12 kyphosis, L1-L4 lordosis, and SVA showed consistently high ICCs (>0.80) and low mean absolute deviations (MADs <6°), with substantial number of studies reporting pelvic tilt achieving an excellent ICC of 0.90 or greater. In contrast, T1-T12 kyphosis and L4-S1 lordosis exhibited lower ICCs and higher measurement errors. Overall, most DL models demonstrated strong correlations (>0.80) with clinician measurements and minimal differences compared to manual references, except for T1-T12 kyphosis (average Pearson correlation: 0.68), L1-L4 lordosis (average Pearson correlation: 0.75), and L4-S1 lordosis (average Pearson correlation: 0.65).
Novel computer vision algorithms show promising accuracy in measuring spinopelvic parameters, comparable to manual surgeon measurements. Future research should focus on external validation, additional imaging modalities, and the feasibility of integration in clinical settings to assess model reliability and predictive capacity.
本综述评估深度学习（DL）在自动估计脊柱骨盆参数方面的进展，并将其准确性与外科医生的手动测量结果进行比较。
在PubMed数据库中查询2014年至2024年间关于成人脊柱骨盆参数DL测量的研究。如果研究聚焦于儿科患者、非畸形相关疾病、非人类受试者，或者缺乏将DL模型与人类测量结果进行比较的足够定量数据，则将其排除。纳入的研究根据模型架构、患者人口统计学、训练、验证、测试方法和样本量进行评估，以及与手动方法相比的性能。
在442篇筛选的文章中，纳入了16篇，样本量从15到9832张X线图像不等，组内相关系数（ICC）报告为0.56至1.00。骨盆倾斜度、骨盆入射角、T4 - T12后凸、L1 - L4前凸和矢状面垂直轴（SVA）的测量显示出一致的高ICC（>0.80）和低平均绝对偏差（MADs <6°），大量研究报告骨盆倾斜度的ICC达到0.90或更高。相比之下，T1 - T12后凸和L4 - S1前凸的ICC较低且测量误差较高。总体而言，除了T1 - T12后凸（平均皮尔逊相关系数：0.68）、L1 - L4前凸（平均皮尔逊相关系数：0.75）和L4 - S1前凸（平均皮尔逊相关系数：0.65）外，大多数DL模型与临床医生测量结果显示出强相关性（>0.80），与手动参考值的差异最小。
新型计算机视觉算法在测量脊柱骨盆参数方面显示出有前景的准确性，与外科医生的手动测量相当。未来的研究应集中在外部验证、额外的成像模态以及在临床环境中整合的可行性，以评估模型的可靠性和预测能力。
