{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:38:49Z","timestamp":1771331929322,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kaohsiung Veterans General Hospital, Taiwan,","award":["KSVMHU110-003"],"award-info":[{"award-number":["KSVMHU110-003"]}]},{"name":"Kaohsiung Veterans General Hospital, Taiwan,","award":["VGHMHU111-002"],"award-info":[{"award-number":["VGHMHU111-002"]}]},{"name":"Kaohsiung Veterans General Hospital, Taiwan,","award":["MOST 111-2221-E-324-022"],"award-info":[{"award-number":["MOST 111-2221-E-324-022"]}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["KSVMHU110-003"],"award-info":[{"award-number":["KSVMHU110-003"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["VGHMHU111-002"],"award-info":[{"award-number":["VGHMHU111-002"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 111-2221-E-324-022"],"award-info":[{"award-number":["MOST 111-2221-E-324-022"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient\u2019s epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient\u2019s epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients.<\/jats:p>","DOI":"10.3390\/s23187669","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T10:26:43Z","timestamp":1693909603000},"page":"7669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Development of a Deep Learning-Based Epiglottis Obstruction Ratio Calculation System"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3905-8870","authenticated-orcid":false,"given":"Hsing-Hao","family":"Su","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology-Head and Neck Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan"},{"name":"Department of Physical Therapy, Shu-Zen Junior College of Medicine and Management, Kaohsiung 82144, Taiwan"},{"name":"Department of Pharmacy and Master Program, College of Pharmacy & Health Care, Tajen University, Pingtung 90741, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan-Pin","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.sleep.2021.02.034","article-title":"Obstructive sleep apnea and influenza infection: A nationwide population-based cohort study","volume":"81","author":"Chen","year":"2021","journal-title":"Sleep Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.otc.2020.02.009","article-title":"The goals of treating obstructive sleep apnea","volume":"53","author":"Evans","year":"2020","journal-title":"Otolaryngol. Clin. N. Am."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.sleep.2020.05.024","article-title":"Objective and subjective long term outcome of maxillomandibular advancement in obstructive sleep apnea","volume":"74","author":"Lin","year":"2020","journal-title":"Sleep Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"433","DOI":"10.5664\/jcsm.2658","article-title":"Evaluation of drug-induced sleep endoscopy as a patient selection tool for implanted upper airway stimulation for obstructive sleep apnea","volume":"9","author":"Vanderveken","year":"2013","journal-title":"J. Clin. Sleep Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.anl.2020.09.018","article-title":"The correlation between drug-induced sleep endoscopy findings and severity of obstructive sleep apnea","volume":"48","author":"Hsu","year":"2021","journal-title":"Auris Nasus Larynx"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1002\/lary.27808","article-title":"Is epiglottis surgery necessary for obstructive sleep apnea patients with epiglottis obstruction?","volume":"129","author":"Kwon","year":"2019","journal-title":"Laryngoscope"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35849","DOI":"10.1038\/srep35849","article-title":"Dynamic drug-induced sleep computed tomography in adults with obstructive sleep apnea","volume":"6","author":"Li","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xia, F., and Sawan, M. (2021). Clinical and Research Solutions to Manage Obstructive Sleep Apnea: A Review. Sensors, 21.","DOI":"10.3390\/s21051784"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.maturitas.2020.06.002","article-title":"Obstructive sleep apnea and depression: A systematic review and meta-analysis","volume":"142","author":"Edwards","year":"2020","journal-title":"Maturitas"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106749","DOI":"10.1016\/j.clineuro.2021.106749","article-title":"Risk assessment of obstructive sleep apnea syndrome and other sleep disorders in multiple sclerosis patients","volume":"207","author":"Sunter","year":"2021","journal-title":"Clin. Neurol. Neurosurg."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jonas, D.E., Amick, H.R., Feltner, C., Weber, R.P., Arvanitis, M., Stine, A., Lux, L., Middleton, J.C., Voisin, C., and Harris, R.P. (2017). Screening for Obstructive Sleep Apnea in Adults Us Preventive Services Task Force Recommendation Statement.","DOI":"10.1001\/jama.2016.19635"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s00405-011-1633-8","article-title":"Drug-induced sleep endoscopy: The VOTE classification","volume":"268","author":"Kezirian","year":"2011","journal-title":"Eur. Arch. Oto-Rhino-Laryngol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9477","DOI":"10.1109\/TPAMI.2021.3127674","article-title":"Pharmacological, non-pharmacological policies and mutation: An artificial intelligence based multi-dimensional policy making algorithm for controlling the casualties of the pandemic diseases","volume":"44","author":"Tutsoy","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","unstructured":"Gonzalez, R.C., and Woods, R.E. (2017). Digital Image Processing, Pearson. [4th ed.]."},{"key":"ref_15","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_16","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"1","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","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). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single shot multibox detector","volume":"Volume 9905","author":"Liu","year":"2016","journal-title":"Proceedings of the 14th European Conference on Computer Vision\u2014ECCV 2016"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (2021, January 20). YOLOv3: An Incremental Improvement (Tech Report) 2018. Available online: https:\/\/pjreddie.com\/media\/files\/papers\/YOLOv3.pdf."},{"key":"ref_22","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2022). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 20\u201325). Scaled-YOLOv4: Scaling cross stage partial network. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainablebag-of-freebiessetsnewstate-of-the-artforreal-timeobject detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"138","DOI":"10.22399\/ijcesen.780174","article-title":"Medical data analysis for different data types","volume":"6","author":"EKEN","year":"2020","journal-title":"Int. J. Comput. Exp. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ferrer-Lluis, I., Castillo-Escario, Y., Montserrat, J.M., and Jan\u00e9, R. (2021). Enhanced monitoring of sleep position in sleep apnea patients: Smartphone triaxial accelerometry compared with video-validated position from polysomnography. Sensors, 21.","DOI":"10.3390\/s21113689"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102006","DOI":"10.1016\/j.artmed.2020.102006","article-title":"A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder","volume":"112","author":"Kou","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103584","DOI":"10.1016\/j.amjoto.2022.103584","article-title":"Automatic classification of the obstruction site in obstructive sleep apnea based on snoring sounds","volume":"43","author":"Liu","year":"2022","journal-title":"Am. J. Otolaryngol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.sleep.2022.12.015","article-title":"Automatic scoring of drug-induced sleep endoscopy for obstructive sleep apnea using deep learning","volume":"102","author":"Hanif","year":"2023","journal-title":"Sleep Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1145\/965145.801294","article-title":"Color image quantization for frame buffer display","volume":"16","author":"Heckbert","year":"1982","journal-title":"Comput. Graph."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1002\/lary.25589","article-title":"Epiglottis collapse in adult obstructive sleep apnea: A systematic review","volume":"126","author":"Torre","year":"2016","journal-title":"Laryngoscope"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1016\/j.patcog.2008.10.013","article-title":"Fast connected-component labeling","volume":"42","author":"He","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Morera, \u00c1., S\u00e1nchez, \u00c1., Moreno, A.B., Sappa, \u00c1.D., and V\u00e9lez, J.F. (2020). SSD vs. YOLO for detection of outdoor urban advertising panels under multiple variabilities. Sensors, 20.","DOI":"10.3390\/s20164587"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105336","DOI":"10.1016\/j.compag.2020.105336","article-title":"A novel image measurement algorithm for common mushroom caps based on convolutional neural network","volume":"171","author":"Lu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Su, H.-H., Pan, H.-W., Lu, C.-P., Chuang, J.-J., and Yang, T. (2020). Automatic detection method for cancer cell nucleus image based on deep-learning analysis and color layer signature analysis algorithm. Sensors, 20.","DOI":"10.3390\/s20164409"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR Workshop), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_37","unstructured":"Misra, D. (2020). Mish: A self regularized non-monotonic activation function. arXiv."},{"key":"ref_38","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-iou loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence 2020, New York, NY, USA."},{"key":"ref_39","unstructured":"(2023, July 27). LabelImg. Available online: https:\/\/github.com\/tzutalin\/labelImg."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:50Z","timestamp":1760129210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,5]]},"references-count":39,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187669"],"URL":"https:\/\/doi.org\/10.3390\/s23187669","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,5]]}}}