Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). MATERIAL AND METHODS: Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. RESULTS: The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. CONCLUSION: This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.

Novel AI-Based Algorithm for the Automated Measurement of Cervical Sagittal Balance Parameters. A Validation Study on Pre- and Postoperative Radiographs of 129 Patients.

STUDY DESIGN: Retrospective, mono-centric cohort research study. OBJECTIVES: The analysis of cervical sagittal balance parameters is essential for preoperative planning and dependent on the physician's experience. A fully automated artificial intelligence-based algorithm could contribute to an objective analysis and save time. Therefore, this algorithm should be validated in this study. METHODS: Two surgeons measured C2-C7 lordosis, C1-C7 Sagittal Vertical Axis (SVA), C2-C7-SVA, C7-slope and T1-slope in pre- and postoperative lateral cervical X-rays of 129 patients undergoing anterior cervical surgery. All parameters were measured twice by surgeons and compared to the measurements by the AI algorithm consisting of 4 deep convolutional neural networks. Agreement between raters was quantified, among other metrics, by mean errors and single measure intraclass correlation coefficients for absolute agreement. RESULTS: ICC-values for intra- (range: .92-1.0) and inter-rater (.91-1.0) reliability reflect excellent agreement between human raters. The AI-algorithm could determine all parameters with excellent ICC-values (preop:0.80-1.0; postop:0.86-.99). For a comparison between the AI algorithm and 1 surgeon, mean errors were smallest for C1-C7 SVA (preop: -.3 mm (95% CI:-.6 to -.1 mm), post: .3 mm (.0-.7 mm)) and largest for C2-C7 lordosis (preop:-2.2° (-2.9 to -1.6°), postop: 2.3°(-3.0 to -1.7°)). The automatic measurement was possible in 99% and 98% of pre- and postoperative images for all parameters except T1 slope, which had a detection rate of 48% and 51% in pre- and postoperative images. CONCLUSION: This study validates that an AI-algorithm can reliably measure cervical sagittal balance parameters automatically in patients suffering from degenerative spinal diseases. It may simplify manual measurements and autonomously analyze large-scale datasets. Further studies are required to validate the algorithm on a larger and more diverse patient cohort.

Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients.

INTRODUCTION: Spinal measurements play an integral role in surgical planning for a variety of spine procedures. Full-length imaging eliminates distortions that can occur with stitched images. However, these images take radiologists significantly longer to read than conventional radiographs. Artificial intelligence (AI) image analysis software that can make such measurements quickly and reliably would be advantageous to surgeons, radiologists, and the entire health system. MATERIALS AND METHODS: Institutional Review Board approval was obtained for this study. Preoperative full-length standing anterior-posterior and lateral radiographs of patients that were previously measured by fellowship-trained spine surgeons at our institution were obtained. The measurements included lumbar lordosis (LL), greatest coronal Cobb angle (GCC), pelvic incidence (PI), coronal balance (CB), and T1-pelvic angle (T1PA). Inter-rater intra-class correlation (ICC) values were calculated based on an overlapping sample of 10 patients measured by surgeons. Full-length standing radiographs of an additional 100 patients were provided for AI software training. The AI algorithm then measured the radiographs and ICC values were calculated. RESULTS: ICC values for inter-rater reliability between surgeons were excellent and calculated to 0.97 for LL (95% CI 0.88-0.99), 0.78 (0.33-0.94) for GCC, 0.86 (0.55-0.96) for PI, 0.99 for CB (0.93-0.99), and 0.95 for T1PA (0.82-0.99). The algorithm computed the five selected parameters with ICC values between 0.70 and 0.94, indicating excellent reliability. Exemplary for the comparison of AI and surgeons, the ICC for LL was 0.88 (95% CI 0.83-0.92) and 0.93 for CB (0.90-0.95). GCC, PI, and T1PA could be determined with ICC values of 0.81 (0.69-0.87), 0.70 (0.60-0.78), and 0.94 (0.91-0.96) respectively. CONCLUSIONS: The AI algorithm presented here demonstrates excellent reliability for most of the parameters and good reliability for PI, with ICC values corresponding to measurements conducted by experienced surgeons. In future, it may facilitate the analysis of large data sets and aid physicians in diagnostics, pre-operative planning, and post-operative quality control.