BRIEF RESEARCH REPORT article
Front. Neurol.
Sec. Artificial Intelligence in Neurology
Causal Dynamic Decision-Making for Robotic Systems in Non-Markovian High-Difficulty Surgery
Na Guo 1
Minghui Tan 1
Tiantian Li 2
Yang Liu 3
Qinjian Zhang 3
Yuanxin Li 4
Tianlei Xu 5,4
Fuchun Sun 6
1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
2. China Agricultural University College of Information and Electrical Engineering, Beijing, China
3. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China
4. Beijing Tsinghua Changgung Hospital, Beijing, China
5. Institute of Intelligent Healthcare, Tsinghua University, Beijing, China
6. Department of Computer Science and Technology, Tsinghua University, Beijing, China
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Abstract
Markov assumption-based surgical decision models cannot account for the time-varying, irregular effects of high-risk intraoperative anomalies such as sudden hemorrhage or inadvertent instrument loss, making them inadequate for specialized procedures like neurosurgery and spinal interventions. To overcome the non-Markovian limitations of conventional surgical process modeling, this study develops a causal modeling framework based on Vector Auto Regression (VAR) and Granger causality analysis. The framework constructs a causal chain (original gesture ππππ β abnormal event πΈπΈππ β recovery action ππππ) to enable intelligent response and adaptive decision-making. Validation was performed on a large-scale synthetic dataset containing 10,000 samples (including anomaly, positive, and negative cases), and evaluated using accuracy, F1-score, and recall metrics. Experimental results show the proposed method achieves 95.60% accuracy in causal inference, maintaining stability at 10,000 samples with an F1 score of 95.77%. Notably, recall (95.88%) slightly exceeds precision (95.34%), reflecting the clinical principle of prioritizing safety. The framework effectively captures non-Markovian temporal correlations induced by abnormal events, overcoming key limitations of traditional approaches. Its design is not procedure-specific, providing a versatile and generalizable pathway for enhancing autonomous decision-making in surgical robots across diverse clinical applications.
Summary
Keywords
causal inference, dynamic decision-making, Granger causality, Non-Markov processes, surgical robotics
Received
15 December 2025
Accepted
06 February 2026
Copyright
Β© 2026 Guo, Tan, Li, Liu, Zhang, Li, Xu and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Na Guo; Fuchun Sun
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.