Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: A retrospective cohort study.

BACKGROUND

Deep learning has been validated as a promising technique for automatic segmentation and rapid three-dimensional (3D) reconstruction of lumbosacral structures on CT. Simulated foraminoplasty of percutaneous endoscopic transforaminal discectomy (PETD) through the Kambin triangle may benefit viability assessment of PETD at L5/S1 level.

MATERIAL AND METHODS

Medical records and radiographic data of patients with L5/S1 lumbar disc herniation (LDH) who received a single-level PETD from March 2013 to February 2018 were retrospectively collected and analyzed. Deep learning was adopted to achieve semantic segmentation of lumbosacral structures (nerve, bone, disc) on CT, and the segmented masks on reconstructed 3D models. Two observers measured the area of the Kambin triangle on 6 selected deep learning-derived 3D (DL-3D) models and ground truth-derived 3D (GT-3D) models, and intraclass correlation coefficient (ICC) was calculated to assess the test-retest and interobserver reliability. Foraminoplasty of PETD was simulated on L5/S1 lumbosacral 3D models. Patients with extended foraminoplasty or stuck canula occurs on simulations were predicted as PETD-difficult cases (Group A). The remaining patients were regarded as PETD-normal cases (Group B). Clinical information and outcomes were compared between the two groups.

RESULTS

Deep learning-derived 3D models of lumbosacral structures (nerves, bones, and disc) from thin-layer CT were reliable. The area of the Kambin triangle was 161.27 ± 40.10 mm on DL-3D models and 153.57 ± 32.37 mm on GT-3D models (p = 0.206). Reliability test revealed strong test-retest reliability (ICC between 0.947 and 0.971) and interobserver reliability of multiple measurements (ICC between 0.866 and 0.961). The average operation time was 99.62 ± 17.39 min in Group A and 88.93 ± 21.87 min in Group B (P = 0.025). No significant differences in patient-reported outcomes or complications were observed between the two groups (P > 0.05).

CONCLUSION

Deep learning achieved accurate and rapid segmentations of lumbosacral structures on CT, and deep learning-based 3D reconstructions were efficacious and reliable. Foraminoplasty simulation with deep learning-based lumbosacral reconstructions may benefit surgical difficulty prediction of PETD at L5/S1 level.