Application of deep learning towards automated electromyographic wave classification for neuromonitoring in thyroid and parathyroid surgery

The newly developed convolutional neural network enables accurate automated classification of electromyographic peak signals, facilitating the identification and correction of mislabelled intraoperative nerve monitoring data. Such optimized data quality is essential for artificial intelligence training, enabling neuromonitoring machines to alert the surgeon in the operating theatre of mislabelling. Future studies will aim to include a wider range of clinical scenarios and external data sets, in order to further optimize the existing labelling tool and allow clinical applications.