Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2021, Vol. 35 ›› Issue (6): 125-131.doi: 10.6040/j.issn.1673-3770.0.2021.026

Previous Articles     Next Articles

Application of artificial intelligence based on machine learning in clinical diagnosis and treatment in otolaryngology

WANG Di, CHENG Jinzhang,YU Dan   

  1. Department of Otorhinolaryngology & Head and Neck Surgery, the Second Hospital of Jilin University, Changchun 130041, Jilin, China
  • Published:2021-12-10

PDF (PC)

275

Abstract: Artificial intelligence(AI)is a branch of computer science, which has been applied in various fields, and its role has been fully demonstrated. In the field of medicine, AI is changing the traditional medical model, and will become the direction of human medical development in the future. Machine learning is an important method to realize AI technology. At present, it is widely used in clinical data deep mining, imageomics feature analysis, disease prediction model building and other related aspects,and it plays an important auxiliary role in clinical diagnosis, treatment and prognosis. In the field of Otolaryngology, there are image analysis and classification, voice signal processing,construction of various disease prediction models to assist medical decision-making. Only by recognizing and understanding the AI technology based on machine learning, and applying it in clinic, can we maximize its effect. In view of this, this paper will briefly introduce the concept of artificial intelligence and its application status in the medical field, focusing on the application progress of AI technology based on machine learning in the clinical diagnosis and treatment of Otolaryngology related diseases, so as to provide reference for otolaryngologists in clinical diagnosis and treatment

Key words: Artificial Intelligence, Machine Learning, Deep Learning, Otolaryngology

CLC Number: 

  • R762
[1] Amisha, Malik P, Pathania M, et al.Overview of artificial intelligence in medicine[J]. J Fam Med Prim Care, 2019,8(7): 2328-2331. doi:10.4103/jfmpc.jfmpc_440_19.
[2] Krittanawong C, Zhang H J, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine[J]. J Am Coll Cardiol,2017, 69(21): 2657-2664. doi: 10.1016/j.jacc.2017.03.571.
[3] He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine[J]. Nat Med, 2019, 25(1): 30-36. doi: 10.1038/s41591-018-0307-0.
[4] Crowson MG, Ranisau J, Eskander A, et al. A Contemporary Review of Machine Learning in Otolaryngology-Head and Neck Surgery[J]. Laryngoscope, 2020, 130(1): 45-51. doi: 10.1002/lary.27850.
[5] Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future[J]. Stroke Vasc Neurol, 2017, 2(4): 230-243. doi:10.1136/svn-2017-000101.
[6] Tama BA, Kim DH, Kim G, et al. Recent advances in the application of artificial intelligence in otorhinolaryngology-head and neck surgery[J]. Clin Exp Otorhinolaryngol, 2020, 13(4): 326-339. doi:10.21053/ceo.2020.00654.
[7] Liu GS, Zhu MH, Kim J, et al. ELHnet: a convolutional neural network for classifying cochlear endolymphatic Hydrops imaged with optical coherence tomography[J]. Biomed Opt Express, 2017, 8(10): 4579-4594. doi:10.1364/BOE.8.004579.
[8] Chowdhury NI, Smith TL, Chandra RK, et al. Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks[J]. Int Forum Allergy Rhinol, 2019, 9(1): 46-52. doi:10.1002/alr.22196.
[9] Huang J, Habib AR, Mendis D, et al. An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans[J]. J Laryngol Otol, 2020, 134(1): 52-55. doi:10.1017/S0022215119002536.
[10] Ren JL, Tian J, Yuan Y, et al. Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage I-II and III-IV head and neck squamous cell carcinoma[J]. Eur J Radiol, 2018, 106: 1-6. doi:10.1016/j.ejrad.2018.07.002.
[11] Forghani R, Chatterjee A, Reinhold C, et al. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning[J]. Eur Radiol, 2019, 29(11): 6172-6181. doi:10.1007/s00330-019-06159-y.
[12] 陈海兵, 卫亚楠, 许晓泉, 等. 基于XGBoost人工智能结合CT构建甲状腺癌颈部淋巴结转移预测模型[J]. 山东大学耳鼻喉眼学报, 2020,34(3): 40-45. doi:10.6040/j.issn.1673-3770.0.2016.377. CHEN Haibing, WEI Yanan, XU Xiaoquan, et al. Prediction of cervical lymph node metastasis in papillary thyroid cancer based on XGBoost artificial intelligence and enhanced computed tomography[J]. J Otolaryngol Ophthalmol Shandong Univ, 2020, 34(3): 40-45. doi:10.6040/j.issn.1673-3770.0.2016.377.
[13] Tran TT, Fang TY, Pham VT, et al. Development of an automatic diagnostic algorithm for pediatric otitis media[J]. Otol Neurotol, 2018, 39(8): 1060-1065. doi:10.1097/MAO.0000000000001897.
[14] Khan MA, Kwon S, Choo J, et al. Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks[J]. Neural Netw, 2020, 126: 384-394. doi:10.1016/j.neunet.2020.03.023.
[15] Li CF, Jing BZ, Ke LR, et al. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies[J]. Cancer Commun(Lond), 2018, 38(1): 59. doi:10.1186/s40880-018-0325-9.
[16] Ren JJ, Jing XP, Wang J, et al. Automatic recognition of laryngoscopic images using a deep-learning technique[J]. Laryngoscope, 2020, 130(11): E686-E693. doi:10.1002/lary.28539.
[17] Tamashiro A, Yoshio T, Ishiyama A, et al. Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks[J]. Dig Endosc, 2020, 32(7): 1057-1065. doi:10.1111/den.13653.
[18] Kuo CFJ, Kao CH, Dlamini S, et al. Laryngopharyngeal reflux image quantization and analysis of its severity[J]. Sci Rep, 2020, 10(1): 1-12. doi:10.1038/s41598-020-67587-1.
[19] Zhang LL, Wu YZ, Zheng B, et al. Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy[J]. Theranostics, 2019, 9(9): 2541-2554. doi:10.7150/thno.32655.
[20] Halicek M, Dormer JD, Little JV, et al. Hyperspectral imaging of head and neck squamous cell carcinoma for cancer margin detection in surgical specimens from 102 patients using deep learning[J]. Cancers(Basel), 2019, 11(9): E1367. doi:10.3390/cancers11091367.
[21] Fang SH, Tsao Y, Hsiao MJ, et al. Detection of pathological voice using cepstrum vectors: a deep learning approach[J]. J Voice, 2019, 33(5): 634-641. doi:10.1016/j.jvoice.2018.02.003.
[22] Powell ME, Rodriguez Cancio M, Young D, et al. Decoding phonation with artificial intelligence(DeP AI): Proof of concept[J]. Laryngoscope Investig Otolaryngol, 2019, 4(3): 328-334. doi:10.1002/lio2.259.
[23] Kim H, Jeon J, Han YJ, et al. Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy[J]. J Clin Med, 2020, 9(11): 3415. doi:10.3390/jcm9113415.
[24] Rameau A. Pilot study for a novel and personalized voice restoration device for patients with laryngectomy[J]. Head Neck, 2020, 42(5): 839-845. doi:10.1002/hed.26057.
[25] Markandeya MN, Abeyratne UR, Hukins C. Overnight airway obstruction severity prediction centered on acoustic properties of smart phone: validation with esophageal pressure[J]. Physiol Meas, 2020, 41(10): 105002. doi:10.1088/1361-6579/abb75f.
[26] Jothi N, Rashid NA, Husain W. Data mining in healthcare-A review[J]. Procedia Comput Sci, 2015, 72: 306-313. doi:10.1016/j.procs.2015.12.145.
[27] 汪志伟, 陆月文, 窦晓辉, 等. 基于人工智能下突发性聋与血清指标的相关因素分析[J]. 临床耳鼻咽喉头颈外科杂志, 2020, 34(11): 977-980. doi:10.13201/j.issn.2096-7993.2020.11.004. WANG Zhiwei, LU Yuewen, DOU Xiaohui, et al. Analysis of related factors between sudden sensorineural hearing loss and serum indices base on artificial intelligence and big data[J]. J Clin Otorhinolaryngol Head Neck Surg, 2020, 34(11): 977-980. doi:10.13201/j.issn.2096-7993.2020.11.004.
[28] 郭宇, 李凤美, 陈雨行, 等. 基于决策树的听力损失致病基因筛查方法[J]. 太赫兹科学与电子信息学报, 2020, 18(4): 703-707. GUO Yu, LI Fengmei, CHEN Yuhang, et al. A method of genetic disease screening for hearing loss based on decision tree[J]. J Terahertz Sci Electron Inf Technol, 2020, 18(4): 703-707.
[29] Wilson MB, Ali SA, Kovatch KJ, et al. Machine learning diagnosis of peritonsillar abscess[J]. Otolaryngol Head Neck Surg, 2019, 161(5): 796-799. doi:10.1177/0194599819868178.
[30] Smith JB, Shew M, Karadaghy OA, et al. Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning[J]. Head Neck, 2020, 42(9): 2330-2339. doi:10.1002/hed.26246.
[31] Mayo CS, Mierzwa M, Moran JM, et al. Combination of a big data analytics resource system with an artificial intelligence algorithm to identify clinically actionable radiation dose thresholds for dysphagia in head and neck patients[J]. Adv Radiat Oncol, 2020, 5(6): 1296-1304. doi:10.1016/j.adro.2019.12.007.
[32] Tsui SY, Tsao Y, Lin CW, et al. Demographic and symptomatic features of voice disorders and their potential application in classification using machine learning algorithms[J]. Folia Phoniatr Logop, 2018, 70(3/4): 174-182. doi:10.1159/000492327.
[1] HUA Hongli, LI Song,TAO Zezhang. Research progress of artificial intelligence in the diagnosis and treatment of nasopharyngeal carcinoma [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2022, 36(2): 113-119.
[2] Huang Tianze, Chen Di,LI Ying. Advances of machine learning in the diagnosis of ocular surface diseases and guiding corneal surgical procedures [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2021, 35(6): 13-19.
[3] LI Jingjing, WU Xinxin, MAO Ning, ZHENG Guibin, MU Yakui, CHU Tongpeng, JIA Chuanliang, ZHENG Haitao, MI Jia, SONG Xicheng. CT-based radiomics nomogram for the preoperative prediction of central lymph node metastases of papillary thyroid carcinoma [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2021, 35(4): 51-59.
[4] WANG Yanjie, CHENG FengliOverview,ZHAO ChangqingGuidance. Latest research progress of 3D printing technology and clinical applications in otorhinolaryngology [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2021, 35(2): 114-118.
[5] CHEN Haibing, WEI Ya'nan, XU Xiaoquan, CHEN Xi. Prediction of cervical lymph node metastasis in papillary thyroid cancer based on XGBoost artificial intelligence and enhanced computed tomography [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2020, 34(3): 40-45.
[6] Huimin LIANG,Liqin WANG,Wenjin WAN,Sailei SHI,Jing YUAN,Yujie SHEN,Jiacheng ZHANG,Zefan MAO,Jingzi NI,Yiwen PAN,Zhibin CHEN,Lei CHENG. COVID-19 prevention and control scheme in an ENT endoscopy unit in China [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2020, 34(2): 88-92.
[7] Zhenhua JIANG,Lijun ZHANG,Ying LI,Yanqin XIAO,Chao LI,Bo SHI,Guiying ZHANG,Bin XU,Wei DENG,Gang LUO,Jifang LUO,Guoqi LIU. Epidemic control practices of an otolaryngology-head and neck surgery ward in an area with non-high incidence of COVID-19 [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2020, 34(2): 93-98.
[8] ZHU Zhiling, LI SongOverview,GUAN GuofangGuidance. Application and prospect of artificial intelligence in otolaryngology [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2020, 34(2): 115-120.
[9] Min YIN,Yawen SHI,Xinjie ZHU,Zhibin CHEN,Xi CHEN,Lei CHENG. PBL teaching and the standardization training of residents in otolaryngology-head and neck surgery [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2019, 33(3): 154-156.
[10] Changyu QIU,Jun ZHOU,De′en ZHUANG,Qing YANG,Meiping LU,Lei CHENG. Artificial intelligence technology application in disclosing allergic rhinitis patients′ needs to otolaryngologists [J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2019, 33(3): 88-94.
[11] WU Xifu, ZHANG Gehua. Assessment of the effectiveness of problem-based learning during the probation period of the otolaryngology head and [J]. J Otolaryngol Ophthalmol Shandong Univ, 2018, 32(4): 116-118.
[12] LI Chao, YAN Yujang, ZHANG Qinyue, CHEN Xia, CAO Yonghua. The application of standardized patients in case-based learning of otolaryngology. [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2017, 31(5): 111-114.
[13] GUO Yingyuan, ZHANG Dejun, GUAN Guofang, WEN Lianji, ZHANG Hui, YU Duojiao, MA Shuaishuai. Exploration of the application of three-dimensional printing technology in the standardized training of doctors in the otolaryngological department. [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2017, 31(3): 119-122.
[14] YANG Jingpu, ZHANG Dejun, ZHAO Yin, CHENG Jinzhang, ZHAO Xue, LIU Yan, YU Dan, WANG Haitao, WEN Lianji. Construction and application of the teaching case database of otolaryngology head and neck surgery. [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2016, 30(6): 98-100.
[15] CHEN Xiumei, SONG Xicheng. Diagnosis and treatment of cervical necrotizing fasciitis: a report of 7 cases and literature review. [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2016, 30(3): 65-67.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] LIN Bin,WANGHui-ge . Functional endoscopic sinus surgery, FESS[J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2006, 20(6): 481 -487 .
[2] GONG Lei,SUN Jie,XUE Zi-chao,LI Jing-hua,XUE Wei-guo . DNA analysis of the cell cycle in sino-nasal neoplasm[J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2008, 22(3): 193 -195 .
[3] CHEN Wen-wen . [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2006, 20(5): 472 -472 .
[4] LUAN Jiangang,LIANG Chuanyu,WEN Yanjun,LI Jiong . Construction of RNAi expressing plasmid vector of pSIRENshuttle for EGFR gene silencing[J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2006, 20(1): 4 -8 .
[5] MA Jing, ZHONG Cui-ping . Surgical method for nasopharyngeal fibroangioma encroaching on fossa pterygopalatina: with a report of 5 cases[J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2006, 20(1): 30 -32 .
[6] LIU Qiang-he,LUO Xiang-lin,GENG Wan-ping,CHEN Chen,LEI Xun,LIU Fang-xian,DENG Ming . Age-related spiral ganglion neuron damages and hearing loss in senescence accelerated mice[J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2008, 22(3): 215 -217 .
[7] . [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2008, 22(3): 223 -226 .
[8] . [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2008, 22(3): 246 -247 .
[9] . [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2008, 22(3): 250 -252 .
[10] . [J]. JOURNAL OF SHANDONG UNIVERSITY (OTOLARYNGOLOGY AND OPHTHALMOLOGY), 2008, 22(3): 260 -262 .
Website Copyright: Editorial Office of Journal of Otolaryngology and Ophthalmology of Shandong University
Address: No.27 Shanda South Road, Jinan, Shandong, China. Post code: 250100 Tel: +86-0531-88366912 Email: ebhxb@sdu.edu.cn Supported by Beijing Magtech Co.Ltd