Enhancing the Effectiveness of Monitoring the Robust Bulbocavernosus Reflex in Cauda Equina Surgeries: Harnessing Machine Learning to Decode the Complexity of Stimulation Parameters

Cauda equina surgeries present significant challenges due to pathological diversity and anatomical variability, particularly in pediatric cases involving tethered spinal cord syndrome, lumbosacral spinal tumors, and congenital deformities. The Bulbocavernosus Reflex (BCR), mediated by the S2–S4 sacral segments, is an efficient multimodality intraoperative neurophysiological monitoring (IONM) tool for preserving sacral neural pathways during these complex procedures. Since its introduction in the IONM arena in the late 1990s, the efficacy of BCR in preventing pelvic neural deficits was gradually established. At the same time, advancements in IONM technology over the decades fostered its widespread and reliable utility. Despite evidence-based advantages, achieving robust BCR monitorability remains challenging due to highly variable stimulation parameters. Standardized protocols can provide a pathway to enhance its potential by enabling widespread adaptability and ensuring consistent neuromonitoring outcomes. A systematic meta-analysis was performed across relevant databases, focusing on studies utilizing BCR as a multimodality IONM during spinal surgeries. The analysis primarily aimed to establish a feasible set of BCR stimulation parameters to enhance monitorability alongside an evaluation of the sensitivity and specificity of BCR in preventing genitourinary postoperative deficits. AI machine learning tools were also employed to determine stimulation parameter combinations associated with 100% monitorability, providing insights into optimal protocols. Optimal BCR monitorability (100%) was consistently achieved with stimulation intensity of 40–50 mA, pulse counts of 4–8, pulse durations between 0.1–0.5 ms, and interstimulus intervals (ISI) of 2–3 ms, reflecting the critical role of not only stimulation intensity but also the synergistically interdependent dynamics of involved parameters, including temporal dynamics. Broad intensity ranges paired with shorter pulse durations and higher pulse counts effectively enhanced neural activation, underscoring the craft of devising a precise set of stimulation parameters essential for achieving reliable monitorability. Machine learning analysis identified stimulation intensity and pulse count as the most influential predictors of monitorability, explaining 69.25% of the variance and providing key insights for optimizing stimulation protocols. BCR monitoring is pivotal in preserving sacral nerve integrity and minimizing postoperative deficits. Advancing parameter optimization within IONM protocols ensures improved monitorability, enhancing surgical precision and patient outcomes.