Comparative Analysis of Utilization of Artificial Intelligence in Minimally-Invasive Adult Spinal Deformity Surgery

Introduction: Advancements in artificial intelligence (AI), machine learning, and minimally-invasive (MIS) technique may offer enhanced preoperative planning, intraoperative robotic or navigational guidance, and prediction of post-operative complications for adult spinal deformity patients. Despite relatively widespread utilization, there remains a paucity of literature assessing the clinical and radiographic impact of AI in MIS surgery.

Methods: Operative patients ≥18yrs with complete pre-(BL) and up to 2-year(2Y) postop radiographic/HRQL data were stratified by primary utilization AI-based patient-specific rod customization and robotic or navigational assistance in pre- and peri-operative course (AI+) or not (AI-).

Results: 133 MIS patients were included (51.74±11.59 years, 41% female, 30.85±6.93 kg/m2) Of these patients, 44 (33.1%) were classified as AI+. At baseline, patient groups were comparable in BL age, BMI, and CCI (all p>.05), though AI+ patients were more likely to be male (p=.040). Patient groups were comparable in terms of both regional and global radiographic alignment, as well as HRQLs at BL (all p>.05). Surgically, AI+ patients had significantly shorter operative times overall (p=.022) and decreased EBL (p=.001), as well as decreased likelihood of undergoing corpectomy (p=.001). Furthermore, AI+ patients reported significantly lower hospital LOS versus AI- patients (p=.012). At 2Y post-operatively, AI+ patients with custom rods were noted to have significantly improved segmental alignment in terms of decreased pelvic tilt (S1PT) and pelvic incidence (S1PI) (both p<.001). Adjusted complications analysis revealed that AI+ patients were significantly less likely to experience any post-operative complication (p=.003), neurological complications (p=.021), or complication requiring reoperation (p=.003).

Conclusion : The findings in this study demonstrate that patients operated on using AI-based robotic or navigational guidance, as well as the utilization of customized instrumentation may reduce intraoperative invasiveness, shorten hospital length of stay, and decrease complication rates.