{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:54:43Z","timestamp":1768348483566,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T00:00:00Z","timestamp":1542326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 91630206"],"award-info":[{"award-number":["No. 91630206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician\u2019s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists\u2019 ground truth, and our results matched the neurologists\u2019 level with 97.5% accuracy.<\/jats:p>","DOI":"10.3390\/fi10110111","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T11:48:31Z","timestamp":1542368911000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks"],"prefix":"10.3390","volume":"10","author":[{"given":"Anping","family":"Song","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Zuoyu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Xuehai","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Qian","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Xinyi","family":"Di","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1288\/00005537-198308000-00016","article-title":"Facial nerve grading system","volume":"93","author":"House","year":"2010","journal-title":"Laryngoscope"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/0030-4220(90)90347-U","article-title":"Facial nerve function index: A clinical measurement of facial nerve activity in patients with facial nerve palsies","volume":"69","author":"Fields","year":"1990","journal-title":"Oral Surg. Oral Med. Oral Pathol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ross, B.R., Fradet, G., and Nedzelski, J.M. (1994). Development of a Sensitive Clinical Facial Grading System, Springer.","DOI":"10.1007\/978-3-642-85090-5_63"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1002\/lary.20868","article-title":"Sunnybrook facial grading system: Reliability and criteria for grading","volume":"120","author":"Facs","year":"2010","journal-title":"Laryngoscope"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.otohns.2008.12.031","article-title":"Facial Nerve Grading System 2.0","volume":"140","author":"Vrabec","year":"2009","journal-title":"Otolaryngol. Head Neck Surg."},{"key":"ref_6","first-page":"1","article-title":"Analysis of Facial Paralysis Disease using Image Processing Technique","volume":"54","author":"Anguraj","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1097\/00005537-199604000-00009","article-title":"Quantitative assessment of the variation within grades of facial paralysis","volume":"106","author":"Neely","year":"1996","journal-title":"Laryngoscope"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1097\/00005537-199710000-00010","article-title":"Validation of objective measures for facial paralysis","volume":"107","author":"Helling","year":"1997","journal-title":"Laryngoscope"},{"key":"ref_9","first-page":"109","article-title":"Advancement in the evaluation of facial function","volume":"15","author":"Neely","year":"2002","journal-title":"Adv. Otolaryngol."},{"key":"ref_10","unstructured":"Mcgrenary, S., O\u2019Reilly, B.F., and Soraghan, J.J. (2005, January 23\u201324). Objective grading of facial paralysis using artificial intelligence analysis of video data. Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Dublin, Ireland."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"081282","DOI":"10.1186\/1687-5281-2007-081282","article-title":"Biomedical Image Sequence Analysis with Application to Automatic Quantitative Assessment of Facial Paralysis","volume":"2007","author":"He","year":"2007","journal-title":"Eurasip J. Image Video Process."},{"key":"ref_12","unstructured":"Wachtman, G.S., Liu, Y., Zhao, T., Cohn, J., and Schmidt, K. (2018, November 15). Measurement of Asymmetry in Persons with Facial Paralysis. Available online: https:\/\/www.ri.cmu.edu\/publications\/measurement-of-asymmetry-in-persons-with-facial-paralysis\/."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3342\/ceo.2013.6.3.135","article-title":"Agreement between the Facial Nerve Grading System 2.0 and the House-Brackmann Grading System in Patients with Bell Palsy","volume":"6","author":"Lee","year":"2013","journal-title":"Clin. Exp. Otorhinolaryngol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Yann","year":"2015","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_19","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014, January 23\u201328). Deep Learning Face Representation from Predicting 10,000 Classes. Proceedings of the Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.244"},{"key":"ref_22","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (arXiv, 2017). Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Hoochang","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sajid, M.S.T., Baig, M., Riaz, I., Amin, S., and Manzoor, S. (2018). Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions. Symmetry, 10.","DOI":"10.3390\/sym10070242"},{"key":"ref_25","first-page":"1","article-title":"Assessment for facial nerve paralysis based on facial asymmetry","volume":"40","author":"Song","year":"2017","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","unstructured":"Tieleman, T., and Hinton, G. (2012). Lecture 6.5-RMSProp, COURSERA: Neural Networks for Machine Learning, University of Toronto."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1097\/MAO.0b013e3181edb6b8","article-title":"Computerized Objective Measurement of Facial Motion: Normal Variation and Test-Retest Reliability","volume":"31","author":"Neely","year":"2010","journal-title":"Otol. Neurotol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11893","DOI":"10.1007\/s11042-015-2696-0","article-title":"Automatic evaluation of the degree of facial nerve paralysis","volume":"75","author":"Wang","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","first-page":"2751","article-title":"Automatic recognition of facial movement for paralyzed face","volume":"24","author":"Wang","year":"2014","journal-title":"Biomed. Mater. Eng."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/10\/11\/111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:13Z","timestamp":1760196613000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/10\/11\/111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,16]]},"references-count":30,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["fi10110111"],"URL":"https:\/\/doi.org\/10.3390\/fi10110111","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,16]]}}}