Surgical procedure recognition is the process of identifying tasks and gestures done during a surgical process and is a field that has been widely researched due to its use in robot assisted surgeries to improve surgical performances and training. This work investigates the use of Quantum Machine Learning (QML) algorithms, in particular Quantum Support Vector Classifier (QSVC), for the identification of patterns in kinematic data collected from the JIGSAWS dataset which includes 76 kinematic features related to suturing, knot tying and needle passing. In order to evaluate QSVC performance, we compared its performance measures such as accuracy, precision, recall and F1-score with that of a classical Support Vector Classifier (SVC). Quantum kernel-based methods like QSVM embed classical data into high-dimensional Hilbert spaces via quantum feature maps, offering the potential to capture complex data relationships more efficiently. Using ZFeatureMap and quantum circuits implemented in Qiskit, we demonstrate that QSVM shows slight performance advantages over its classical counterpart in specific tasks. These findings lay the groundwork for a context-aware surgical system to support medical practitioners in real time and help advance surgical practice and educational approaches for enhancing patients’ quality of life.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

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Ethical clearance was provided by Ethics committee of the University of Energy and Natural Resources, Sunyani, Ghana

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The dataset used in this study is the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), which was developed through a collaboration between Johns Hopkins University and Intuitive Surgical Inc. The dataset is publicly available for research purposes upon request and approval from the dataset providers. Access can be obtained by submitting a request through the official project website: https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release/

https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release/