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Advances in Nano Research Volume 19, Number 3, September 2025 , pages 245-252 DOI: https://doi.org/10.12989/anr.2025.19.3.245 |
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Integrating Nano-AI predictive models for postoperative risk assessment in spinal fusion |
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Li Chunhui, Zhang Wenjia and Wu Yue
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| Abstract | ||
| Nanotechnology has created new opportunities in precision medicine, providing a chance to monitor the biological signals, molecular markers, and micro-environmental changes at the ultrasensitive level, which occurs before complications. The issue of postoperative complications that arise after performing spinal fusion surgeries is a burning clinical issue, and most of the time, it brings about long-term recovery, increased healthcare expenses, and deteriorated patient outcomes. We suggest the incorporation of nano-enabled AI prediction models in this work that utilizes data collected by nano-sensors, nanomaterials-based diagnostics, and traditional clinical data to improve risk stratification in spinal fusion. Working on a nanoscale, these systems bead on minor physiological changes, including inflammatory biomarkers, metabolic changes, and tissue healing, obscured by conventional techniques. Combined with artificial intelligence, nano-derived datasets offer unmatched granularity, increasing the forecasting performance and allowing timely detection of patients who are at a significant risk of unfavorable events. This model of Nano-AI fills the gap between nano-medicine and computational modeling to provide an innovative solution to customized care postoperative. Finally, the nano-integrated predictive analytics can transform the paradigm of surgical risk assessment, which will inform proactive solutions and support patient safety in sophisticated spinal surgeries. | ||
| Key Words | ||
| AI-powered risk prediction; machine learning in healthcare; postoperative complications; spinal fusion surgery; surgical outcome optimization | ||
| Address | ||
| Li Chunhui and Zhang Wenjia: Department of Neurosurgery, The First Hospital of Hunan University of Chinese Medicine, No. 95 Shaoshan Middle Road, Yuhua District, Changsha, Hunan, Postal Code 410021 Wu Yue: Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Chaoyang District, Beijing, 100020 | ||