{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:01:56Z","timestamp":1787022116153,"version":"build-2736575974"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Intraoperative neurophysiological monitoring is essential in neurosurgical procedures. In this study, we built and evaluated the performance of a deep neural network in differentiating between the presence and absence of a lateral spread response, which provides critical information during microvascular decompression surgery for the treatment of hemifacial spasm using intraoperatively acquired electromyography images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>\n                      A total of 3,674 image screenshots of monitoring devices from 50 patients were prepared, preprocessed, and then adopted into training and validation sets. A deep neural network was constructed using current-standard, off-the-shelf tools. The neural network correctly differentiated 50 test images (accuracy, 100%; area under the curve, 0.96) collected from 25 patients whose data were never exposed to the neural network during training or validation. The accuracy of the network was equivalent to that of the neuromonitoring technologists (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.3013) and higher than that of neurosurgeons experienced in hemifacial spasm (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.0001). Heatmaps obtained to highlight the key region of interest achieved a level similar to that of trained human professionals. Provisional clinical application showed that the neural network was preferable as an auxiliary tool.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A deep neural network trained on a dataset of intraoperatively collected electromyography data could classify the presence and absence of the lateral spread response with equivalent performance to human professionals. Well-designated applications based upon the neural network may provide useful auxiliary tools for surgical teams during operations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0276378","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T13:52:41Z","timestamp":1667397161000},"page":"e0276378","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0128-4917","authenticated-orcid":true,"given":"Minsoo","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0546-9014","authenticated-orcid":true,"given":"Sang-Ku","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuhiro","family":"Kubota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-0074","authenticated-orcid":true,"given":"Seunghoon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwan","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7519-3594","authenticated-orcid":true,"given":"Doo-Sik","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"issue":"2","key":"pone.0276378.ref001","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1001\/archneur.1966.00470080033005","article-title":"Cross talk\u2014the paradoxical transmission of a nerve impulse","volume":"14","author":"WJ Gardner","year":"1966","journal-title":"Arch Neurol"},{"issue":"4","key":"pone.0276378.ref002","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1212\/WNL.34.4.427","article-title":"Pathophysiology of hemifacial spasm: II. Lateral spread of the supraorbital nerve reflex","volume":"34","author":"VK Nielsen","year":"1984","journal-title":"Neurology"},{"issue":"17","key":"pone.0276378.ref003","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1056\/NEJM199604253341701","article-title":"The long-term outcome of microvascular decompression for trigeminal neuralgia","volume":"334","author":"FG Barker","year":"1996","journal-title":"N Engl J Med"},{"key":"pone.0276378.ref004","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1080\/02688690120105101","article-title":"Intraoperative monitoring of facial EMG responses during microvascular decompression for hemifacial spasm. Prognostic value for long-term outcome: a study in a 33-patient series","volume":"15","author":"J Hatem","year":"2001","journal-title":"Br J Neurosurg"},{"key":"pone.0276378.ref005","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s10143-010-0254-9","article-title":"Microvascular decompression for treating hemifacial spasm: lessons learned from a prospective study of 1,174 operations","volume":"33","author":"SJ Hyun","year":"2010","journal-title":"Neurosurg Rev"},{"key":"pone.0276378.ref006","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s00701-012-1277-5","article-title":"A proposal for standardized analysis of the results of microvascular decompression for trigeminal neuralgia and hemifacial spasm","volume":"154","author":"A Kondo","year":"2012","journal-title":"Acta Neurochir (Wien)"},{"issue":"1","key":"pone.0276378.ref007","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3171\/2016.9.JNS161022","article-title":"An investigation into quality of life improvement in patients undergoing microvascular decompression for hemifacial spasm","volume":"128","author":"JD Lawrence","year":"2018","journal-title":"J Neurosurg"},{"key":"pone.0276378.ref008","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3171\/jns.2007.106.3.384","article-title":"Prognostic value of the lateral spread response for intraoperative electromyography monitoring of the facial musculature during microvascular decompression for hemifacial spasm","volume":"106","author":"DS Kong","year":"2007","journal-title":"Journal of neurosurgery"},{"key":"pone.0276378.ref009","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.ijsu.2011.02.010","article-title":"The value of abnormal muscle response monitoring during microvascular decompression surgery for hemifacial spasm","volume":"9","author":"TT Ying","year":"2011","journal-title":"Int J Surg"},{"issue":"7","key":"pone.0276378.ref010","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2018.03.006","article-title":"A new method for monitoring abnormal muscle response in hemifacial spasm: A prospective study","volume":"129","author":"S Lee","year":"2018","journal-title":"Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology"},{"key":"pone.0276378.ref011","article-title":"Adam: A Method for Stochastic Optimization","author":"D Kingma","year":"2014","journal-title":"International Conference on Learning Representations"},{"key":"pone.0276378.ref012","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"A Krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems\u2014Volume 1; Lake Tahoe, Nevada: Curran Associates Inc"},{"key":"pone.0276378.ref013","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/3-540-49430-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"L. Prechelt","year":"1998"},{"issue":"4","key":"pone.0276378.ref014","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1162\/neco.1992.4.4.473","article-title":"Simplifying Neural Networks by Soft Weight-Sharing","volume":"4","author":"SJ Nowlan","year":"1992","journal-title":"Neural Computation"},{"key":"pone.0276378.ref015","doi-asserted-by":"crossref","DOI":"10.1109\/WACV.2018.00097","article-title":"editors. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks","author":"A Chattopadhay","year":"2018","journal-title":"2018 IEEE Winter Conference on Applications of Computer Vision (WACV)"},{"key":"pone.0276378.ref016","article-title":"Smoothgrad: Removing noise by adding noise","author":"D Smilkov","year":"2017","journal-title":"arXiv"},{"key":"pone.0276378.ref017","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.patcog.2016.09.027","article-title":"Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation","volume":"63","author":"T Xu","year":"2017","journal-title":"Pattern Recognit"},{"key":"pone.0276378.ref018","doi-asserted-by":"crossref","first-page":"103616","DOI":"10.1016\/j.compbiomed.2020.103616","article-title":"Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques","volume":"117","author":"M Goyal","year":"2020","journal-title":"Comput Biol Med"},{"issue":"1","key":"pone.0276378.ref019","doi-asserted-by":"crossref","first-page":"15239","DOI":"10.1038\/s41598-019-51363-x","article-title":"Deep learning-based optical field screening for robust optical diffraction tomography","volume":"9","author":"D Ryu","year":"2019","journal-title":"Sci Rep"},{"issue":"7","key":"pone.0276378.ref020","doi-asserted-by":"crossref","first-page":"486","DOI":"10.21037\/atm.2020.03.24","article-title":"Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network","volume":"8","author":"G Liu","year":"2020","journal-title":"Ann Transl Med"},{"issue":"4","key":"pone.0276378.ref021","doi-asserted-by":"crossref","first-page":"e0227240","DOI":"10.1371\/journal.pone.0227240","article-title":"Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography","volume":"15","author":"T Sogawa","year":"2020","journal-title":"PLoS One"},{"issue":"1","key":"pone.0276378.ref022","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10278-016-9914-9","article-title":"High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks","volume":"30","author":"A Rajkomar","year":"2017","journal-title":"J Digit Imaging"},{"issue":"2","key":"pone.0276378.ref023","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1148\/radiol.2018181422","article-title":"Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs","volume":"290","author":"JA Dunnmon","year":"2019","journal-title":"Radiology"},{"issue":"1","key":"pone.0276378.ref024","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s41747-020-00148-3","article-title":"Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach","volume":"4","author":"J Th\u00fcring","year":"2020","journal-title":"Eur Radiol Exp"},{"issue":"4","key":"pone.0276378.ref025","doi-asserted-by":"crossref","first-page":"e0232376","DOI":"10.1371\/journal.pone.0232376","article-title":"Deep learning in rare disease. Detection of tubers in tuberous sclerosis complex","volume":"15","author":"I S\u00e1nchez Fern\u00e1ndez","year":"2020","journal-title":"PLoS One"},{"issue":"12","key":"pone.0276378.ref026","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1007\/s10439-018-2095-6","article-title":"Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks","volume":"46","author":"Y Du","year":"2018","journal-title":"Ann Biomed Eng"},{"key":"pone.0276378.ref027","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.compmedimag.2019.101645","article-title":"An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets","volume":"77","author":"S Zhang","year":"2019","journal-title":"Comput Med Imaging Graph"},{"key":"pone.0276378.ref028","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2016.01.034","article-title":"A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images","volume":"191","author":"J Xu","year":"2016","journal-title":"Neurocomputing"},{"key":"pone.0276378.ref029","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cmpb.2017.06.016","article-title":"Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement","volume":"148","author":"L Zhou","year":"2017","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"pone.0276378.ref030","doi-asserted-by":"crossref","DOI":"10.1042\/BSR20180497","article-title":"Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage","volume":"38","author":"B Wang","year":"2018","journal-title":"Biosci Rep"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0276378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T13:53:28Z","timestamp":1667397208000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0276378"}},"subtitle":[],"editor":[{"given":"Anwar","family":"P.P. Abdul Majeed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":30,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0276378","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,2]]}}}