Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.

ABSTRACT:

Purpose

To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.

Methods

On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard. Intraclass correlation coefficient (ICC), mean absolute error (MAE),

SUBMITTER: Hoppe BF 

PROVIDER: S-EPMC11903605 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature