High-Speed Instance Segmentation for Endoscopic Spine Surgery: Multicenter Validation and Inference Speed Evaluation

초록

Study Design Retrospective Multicenter Cohort Study.Objectives To develop and validate an AI-based high-speed multi-class instance segmentation system for lumbar spinal endoscopic surgery using multicenter surgical video data and to assess performance across hardware environments. Methods Endoscopic videos from 112 patients at 5 hospitals (2020-2025) were analyzed. One frame per 300 frames was sampled, yielding 58,087 annotated images for 7 classes (instrument, fat, soft tissue, bone, nerve, disc, vessel). A Segment Anything Model (SAM)-assisted workflow improved annotation efficiency, followed by expert refinement. A YOLOv11-seg model was trained with a patient-level 4:1 split. Performance was evaluated using precision, recall, F1-score, mAP50, and mAP50-95, stratified by surgical approach. Inference speed was benchmarked across CPU (Intel i5/i7) and GPU (RTX 4080/5080) configurations. Results In the biportal group, overall precision, recall, F1-score, and mAP50 were 0.975, 0.633, 0.768, and 0.629, respectively. The uniportal group demonstrated 0.659, 0.670, 0.664, and 0.682, respectively. Class-wise performance varied substantially by surgical approach: the instrument class showed exceptionally high mAP50 (0.949) in uniportal settings, whereas anatomical structures like vessels were detected with superior accuracy in biportal settings (mAP50 = 0.863). Benchmarking yielded 21.86-27.45 FPS with CPU-only, similar to 92 FPS with RTX 4080, and similar to 117 FPS with RTX 5080. Conclusions This multicenter study highlights the potential of high-speed, multi-class instance segmentation in endoscopic spine surgery. Improving model robustness in visually degraded environments requires further research. Prioritizing high precision to prevent surgeon distraction, supported by rapid inference to maintain temporal continuity, is a practical direction for future surgical AI models.

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