Intraoperative Spinal Cord and Nerve Root Monitoring: A Pilot Survey

Rattenni, Rachel; Cheriyan, Thomas; Lee, Alexandra A.; Bendo, John A.; Errico, Thomas J.; Goldstein, Jeffrey A.

doi:10.1016/j.spinee.2014.08.291

Cited by 1 publication

(1 citation statement)

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“…It is also less sensitive to the changes of nerve root function because the signal is the result of a summation of neural signal from multiple segments before it enters the spinal cord [3]. EMG can show continuous nerve root events since it is free running, but it has a high false positive rate and low specificity in determining events that are significant for the surgeon to alert on [4]. MEP or transcranial motor-evoked potential (TcMEP) has high sensitivity and specificity towards nerve root injury, but it relies on the alarm criteria used and which myotome is monitored [5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Application of Transcranial Motor-Evoked Potential to Predict Positive Functional Outcomes of Patients

Jamaludin

Lai

Chuah

et al. 2022

Computational Intelligence and Neuroscience

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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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Section: Introductionmentioning

confidence: 99%