Methods: We retrospectively studied 61 patients with C2 (+) OPLL who had posterior instrumented laminectomy and fusion at Ganga Hospital, Coimbatore between July 2011 and January 2021, with a minimum follow-up of 2 years. Data on demographics, clinical outcomes, radiology, and post-surgical outcomes were gathered. Results: Among 61 patients, 56 were males and five were females. The OPLL pattern was mixed in 32 cases (52.5%), continuous in 26 cases (42.6%), segmental in two cases (3.3%), and circumscribed in one patient (1.6%). All of our patients showed signs of neurological improvement after a 24-month follow-up. The mean preoperative modified Japanese Orthopaedic Association (mJOA) score was 10.6 (range, 5–11) and the postoperative mJOA score was 15.8 (range, 12–18). The recovery rate was >75% in 27 patients (44.6%), >50% in 32 patients (52.5%), and >25% in two patients (3.3%). The average recovery rate was 71% (range, 33%–100%). The independent risk factor for predicting recovery rate is the preoperative mJOA score. Conclusions In C2 (+) OPLL, posterior instrumented decompression and fusion provide a relatively safe approach and satisfactory results.

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords “artificial intelligence” AND “spine,” “machine learning” AND “spine,” and “deep learning” AND “spine” extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.