AI-driven tissue discrimination sensing methods for robot-assisted spine surgery: a state-of-the-art review.

Background Spinal surgical robots have achieved widespread clinical adoption over two decades, yet their safe and precise bone resection remains fundamentally limited by the lack of reliable real-time tissue discrimination capabilities. Conventional single-modality sensing approaches (imaging, force, acoustic, bioelectrical impedance) each suffer from inherent drawbacks that compromise surgical safety. This state-of-the-art review systematically evaluates AI-driven tissue discrimination sensing methods specifically developed for robot-assisted spine surgery.Methods A comprehensive literature search was conducted across PubMed, IEEE Xplore, and CNKI (1990–2025) following PRISMA guidelines. Sixty eligible studies were included and categorized by sensing modality. We performed a comparative analysis focusing on the interplay between surgical procedures, sensor technologies, and AI algorithm architectures, with particular emphasis on the clinical value of AI-enabled multimodal fusion.Results Three key insights emerged. First, surgical operations exhibit distinct AI-sensing pairing preferences: drilling uses force/acoustic signals with SVM/BP networks for real-time breakthrough detection; milling relies on force signals with LSTMs for continuous time-series processing; cutting employs imaging with CNNs for anatomical segmentation. Second, sensor type fundamentally dictates AI design—imaging sensors pair with CNNs for spatial features, while force/acoustic sensors use recurrent networks for temporal patterns. Third, AI-driven multimodal fusion’s core value is resolving single-modality blind spots, not incremental accuracy gains. Fusion of force and acoustic signals elevated transitional zone recognition from 62% to 92%, significantly improving clinical robustness.Conclusion Single-modality sensing has fundamental limitations, making AI-enabled multimodal fusion an inevitable development trend. We propose a decision tree framework for selecting optimal AI-sensing combinations based on surgical requirements. Future directions include integrated AI-sensor platforms, pathology-adaptive learning models, and standardized evaluation protocols to advance autonomous spinal surgery.