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), and standard deviation (SD) were used for statistical analysis
while, ANOVA was used to search for significant differences between the AI and human readers. RESULTS: Automatic measurements (AI) showed excellent correlation with the reference standard, with all ICCs within the range of the readers (TK: 0.92 [AI] vs. 0.85-0.96 [readers]
LL: 0.95 vs. 0.87-0.98
SS: 0.93 vs. 0.89-0.98
SVA: 1.00 vs. 0.99-1.00
all p <
0.001). Analysis of the MAE (± SD) revealed comparable results to the six readers (TK: 3.71° (± 4.24) [AI] v.s 1.86-5.88° (± 3.48-6.17) [readers]
LL: 4.53° ± 4.68 vs. 2.21-5.34° (± 2.60-7.38)
SS: 4.56° (± 6.10) vs. 2.20-4.76° (± 3.15-7.37)
SVA: 2.44 mm (± 3.93) vs. 1.22-2.79 mm (± 2.42-7.11))
while, ANOVA confirmed no significant difference between the errors of the AI and any human reader (all p >
0.05). Human reading time was on average 139 s per case (range: 86-231 s). CONCLUSION: Our AI algorithm provides spinopelvic measurements accurate within the variability of experienced readers, but with the potential to save time and increase reproducibility.