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, ∼92 FPS with RTX 4080, and ∼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.

Static eccentricity is an important early-stage fault in marine diesel generators, as small air-gap deviations caused by misalignment or mechanical wear can escalate into bearing damage and rotor–stator contact. To address the challenge of detecting such subtle faults, this study proposes a current signal analysis method based on the two-dimensional short-time Fourier transform (2D STFT) for early detection of static eccentricity faults in marine diesel generators. Using three-phase currents measured during normal operation and fault data synthesized with a physics-based electromechanical coupling model (1–5% eccentricity), we construct a two-dimensional phase–time representation rather than treating each phase as an independent one-dimensional time series and then apply 2D STFT. This formulation enables the simultaneous capture of inter-phase relationships and spatial patterns in the time–frequency–phase domain. Experiments indicate a distinct energy rise near 1020 Hz as static eccentricity increases. This trend enables the proposed method to distinguish small faults of approximately 5% eccentricity, which remain difficult to detect using conventional 1D STFT. As a result, the approach improves the diagnostic accuracy of non-contact, current-based monitoring for static eccentricity faults. Future work will include validation using real in-service fault data and extensions to other fault modes such as dynamic eccentricity and bearing defects.