Accuracy and safety evaluation of a novel artificial intelligence-based robotic system for autonomous spinal posterior decompression.

Abstract

Objective: This study aimed to introduce a novel artificial intelligence (AI)-based robotic system for autonomous planning of spinal posterior decompression and verify its accuracy through a cadaveric model.

Methods: Seventeen vertebrae from 3 cadavers were included in the study. Three thoracic vertebrae (T9-11) and 3 lumbar vertebrae (L3-5) were selected from each cadaver. After obtaining CT data, the robotic system independently planned the laminectomy path based on AI algorithms before the surgical procedure and automatically performed the decompression during the procedure. A postoperative CT scan was performed, and the deviation of each cutting plane from the preoperative plan was quantitatively analyzed to evaluate the accuracy and safety of the cuts. The duration of laminectomy was also recorded.

Results: A total of 285 cuts were made on thoracic and lumbar vertebrae. The average duration for unilateral longitudinal cutting was 16.38 ± 4.76 minutes, while for transverse cutting it was 4.44 ± 1.52 minutes. In terms of accuracy assessment, 3 levels were divided based on the distance between the actual cutting plane and the preplanned plane: 77 (84%) were grade A, 15 (16%) were grade B, and none were grade C. Regarding safety assessment, 74 (80%) were designated safe (grade A), with 18 (20%) classified as uncertain (grade B).

Conclusions: The results confirm the accuracy and preliminary safety of the robotic system for autonomous planning and cutting of spinal decompression.