ArticleComputational intelligence and neuroscience2022
Machine Learning Application of Transcranial Motor-Evoked Potential to Predict Positive Functional Outcomes of Patients.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Predicting neurological outcomes following spinal surgery: A machine learning approach using intraoperative neuromonitoring data.North American Spine Society journal · 2026Article
- Long short-term attention memory (LSTAM): a global-feature-integrated model for joint moment prediction in human rehabilitation.Scientific reports · 2026Article
- The future of intraoperative neuromonitoring (IONM) in spinal surgery.North American Spine Society journal · 2025Article
- Influence of Remimazolam and Propofol on Intraoperative Motor Evoked Potentials During Spinal Surgery: A Randomized Crossover Trial.Journal of clinical medicine · 2025Article
- Envisioning the Future of the Neurosurgical Operating Room with the Concept of the Medical Metaverse.Journal of Korean Neurosurgical Society · 2025Article
- Does an improvement in cord-level intraoperative neuromonitoring data lead to a reduced risk for postoperative neurologic deficit in spine deformity surgery?Spine deformity · 2025Article
- Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles.BMC medical informatics and decision making · 2023Article
- Artificial intelligence-assisted air quality monitoring for smart city management.PeerJ. Computer science · 2023Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intraoperative neuromonitoring (IONM) has been used to help monitor the integrity of the nervous system during spine surgery. Transcranial motor-evoked potential (TcMEP) has been used lately for lower lumbar surgery to prevent nerve root injuries and also to predict positive functional outcomes of patients. There were a number of studies that proved that the TcMEP signal's improvement is significant towards positive functional outcomes of patients. In this paper, we explored the possibilities of using a machine learning approach to TcMEP signal to predict positive functional outcomes of patients. With 55 patients who underwent various types of lumbar surgeries, the data were divided into 70 : 30 and 80 : 20 ratios for training and testing of the machine learning models. The highest sensitivity and specificity were achieved by Fine KNN of 80 : 20 ratio with 87.5% and 33.33%, respectively. In the meantime, we also tested the existing improvement criteria presented in the literature, and 50% of TcMEP improvement criteria achieved 83.33% sensitivity and 75% specificity. But the rigidness of this threshold method proved unreliable in this study when different datasets were used as the sensitivity and specificity dropped. The proposed method by using machine learning has more room to advance with a larger dataset and various signals' features to choose from.
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