On the Use of Muscle Activation Patterns and Artificial Intelligence Methods for the Assessment of the Surgical Skills of Clinicians †

Journal article

Samuel, O. 2024. On the Use of Muscle Activation Patterns and Artificial Intelligence Methods for the Assessment of the Surgical Skills of Clinicians †. Engineering Proceedings. 58 (1). https://doi.org/10.3390/ecsa-10-16231
AuthorsSamuel, O.
Abstract

The ranking and evaluation of a surgeon’s surgical skills is an important factor in order to be able to appropriately assign patient cases according to the necessary level of surgeon competence in addition to helping us in the process of pinpointing the specific clinicians within the surgical cohort who require further developmental training. One of the more frequent means of surgical skills evaluation is through a qualitative assessment of a surgeon’s portfolio alongside other supporting pieces of information, a process which is rather subjective. The contribution presented as part of this paper involves the use of a set of Delsys Trigno EMG wearable sensors, which track and record the muscular activation patterns of a surgeon during a surgical procedure, alongside computationally driven artificial intelligence (AI) methods towards the differentiation and ranking of the surgical skills of a clinician in a quantitative fashion. The participants in the research involved novice-level surgeons, intermediate-level surgeons and expert-level surgeons in various simulated surgical cases. A comparison of different signal processing approaches has shown that the proposed approach can prove beneficial in monitoring and differentiating the skillsets of various surgeons for various kinds of surgical cases. The presented method could also be used to track the evolution of the surgical competencies of various trainee surgeons at various stages during their training.

Keywordswearable sensors; surgery; surgical education
Year2024
JournalEngineering Proceedings
Journal citation58 (1)
PublisherMDPI
ISSN2673-4591
Digital Object Identifier (DOI)https://doi.org/10.3390/ecsa-10-16231
Web address (URL)https://www.mdpi.com/2673-4591/58/1/116
Output statusPublished
Publication dates
Online15 Nov 2024
Publication process dates
Deposited21 Nov 2024
Permalink -

https://repository.derby.ac.uk/item/qqzq0/on-the-use-of-muscle-activation-patterns-and-artificial-intelligence-methods-for-the-assessment-of-the-surgical-skills-of-clinicians

Log in to edit
  • 3
    total views
  • 0
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies †
Samuel, O. 2024. Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies †. Engineering Proceedings. 58 (1). https://doi.org/10.3390/ecsa-10-16245
Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries
Karmani, P., Chandio, A. A., Korejo, I. A., Samuel, O. and Aborokbah, M. 2024. Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries. Peer J Computer Science . pp. 1-20. https://doi.org/10.7717/peerj-cs.2397
Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach
Ahmad, I., Liu, Z., Li, L., Ullah, I., Wang, X., Samuel, O., Li, G., Tao, Y., Chen, Y. and Chen, S. 2024. Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach. IEEE Journal of Biomedical and Health Informatics. pp. 1-13. https://doi.org/10.1109/JBHI.2024.3396130
A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization
Samuel, O., Asogbon, M. and McEwan, A. 2024. A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization. Biomedical Signal Processing and Control. 95 (2024), p. 106446. https://doi.org/https://doi.org/10.1016/j.bspc.2024.106446
Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Kandwal, A., Li, J., Kulwa, F., Samuel, O. and Nie, Z. 2024. Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE Transactions on Instrumentation and Measurements. 73, pp. 1-8. https://doi.org/10.1109/TIM.2024.3400333
A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees
Nsugbe, E., Samuel, O., Asogbon, M. and Jose, J. R. L. 2024. A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees. Biomedical Engineering Advances. 7 (2024), pp. 1-10. https://doi.org/10.1016/j.bea.2024.100117
Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization
Asogbon, M.G., Samuel, O., Meziane, F., Li, G. and Li, Y. 2024. Investigation of Artifact Contamination Impact on EEG Oscillations Towards Enhanced Motor Function Characterization. 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012373400003657
Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography
Zhang, H., Peng, B., Tian, L., Samuel, O. and Li, G. 2024. Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography. Cyborg and Bionic Systems. pp. 1-11. https://doi.org/10.34133/cbsystems.0094
An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection
Ahmad, I., Yao, C., Li, L., Chen, Y., Liu, Z., Ullah, I., Shabaz, M., Wang, X., Huang, K., Li, G., Zhao, G., Samuel, O. and Chen, S. 2023. An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection. Journal of Information Security and Applications. 80, pp. 1-17. https://doi.org/10.1016/j.jisa.2023.103654
Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters
Awomuti, A., Alimo, P., Young, G., Agyeman, S., Akintunde, T., Agbeja, A., Oderinde, O., Samuel, O. and Otobrise, H. 2023. Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters. City and Environment Interactions. 20, pp. 1-12. https://doi.org/10.1016/j.cacint.2023.100127
A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition
Kulwa, F., Zhang, H., Samuel, O., Asogbon, M., Scheme, E., Kushaba, R., McEwan, A. and Li, G. 2023. A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition. IEEE Transactions on Human-Machine Systems. pp. 1-12. https://doi.org/10.1109/THMS.2023.3329536
Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor
Asogbon, M., Huai, Y., Samuel, O., Jing, Z., Ma, Y., Liu, J., Jiang, Y., Fu, Y., Li, G. and Li, Y. 2023. Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE Xplore. https://doi.org/https://doi.org/10.1109/EMBC40787.2023.10340688
An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals
Zangene, A., Samuel, O., Abbasi, A., Nazarpour, K., McEwan, A. and Li, G. 2023. An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals. 45th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340791
Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors
Samuel, O., Asogbon, M., Kulwa, F., Zangene, A., Oyemakinde, T., Igbe, T., McEwan, A., Li, Y. and Li, G. 2023. Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. https://doi.org/10.1109/EMBC40787.2023.10340683
Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis
Igbe, T., Samuel, O.W., Li, J., Kulwa, F., Kandwal, A. and Nie, Z. 2023. Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE International Workshop on Medical Measurement and Applications (MEMEA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171941
A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems
Kulwa, F., Samuel, O.W., Asogbon, M., Oyemakinde, T.T., Obe, O.O. and Li, G. 2023. A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems. 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE. https://doi.org/10.1109/MeMeA57477.2023.10171910
An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running
Zangene, A. R., Samuel, O., Abbasi, A., McEwan, A., Asogbon, M. G., Li, G. and Nazarpour, K. 2023. An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running. Biomedical Signal Processing and Control. 86 (B), pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105103
A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution
Li, F., Han, F., Wang, L., Huang, L., Samuel, O.W., Zhao, H., Xie, R., Wang, P., Tian, Q., Li, Q., Zhao, Y., Yu, Mei, Sun, J., Yang, R., Zhou, X., Li, F., Li, G., Lu, Y., Guo, P. and Liu, Z. 2023. A Hybrid Strategy-based Ultra-narrow Stretchable Microelectrodes with Cell-level Resolution. Advanced Functional Materials. 2300859, pp. 1-9. https://doi.org/10.1002/adfm.202300859
On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines
Nsugbe, E., Reyes-Lagos, J.J., Adams, D. and Samuel, O. 2023. On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines. Healthcare Technology Letters. 10 (1-2), pp. 11-22. https://doi.org/10.1049/htl2.12044
Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition
Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O.W. and Li, G. 2023. Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition. Nature Scientific Data. 10 (358), pp. 1-16. https://doi.org/10.1038/s41597-023-02263-3
A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals
Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S. 2023. A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2023.3265983