人工智能时代的耳鼻咽喉头颈外科

齐静怀, 张良. 人工智能时代的耳鼻咽喉头颈外科[J]. 临床耳鼻咽喉头颈外科杂志, 2020, 34(12): 1137-1140. doi: 10.13201/j.issn.2096-7993.2020.12.020
引用本文: 齐静怀, 张良. 人工智能时代的耳鼻咽喉头颈外科[J]. 临床耳鼻咽喉头颈外科杂志, 2020, 34(12): 1137-1140. doi: 10.13201/j.issn.2096-7993.2020.12.020
QI Jinghuai, ZHANG Liang. Otolaryngology head and neck surgery in the age of artificial intelligence[J]. J Clin Otorhinolaryngol Head Neck Surg, 2020, 34(12): 1137-1140. doi: 10.13201/j.issn.2096-7993.2020.12.020
Citation: QI Jinghuai, ZHANG Liang. Otolaryngology head and neck surgery in the age of artificial intelligence[J]. J Clin Otorhinolaryngol Head Neck Surg, 2020, 34(12): 1137-1140. doi: 10.13201/j.issn.2096-7993.2020.12.020

人工智能时代的耳鼻咽喉头颈外科

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Otolaryngology head and neck surgery in the age of artificial intelligence

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  • [1]

    Sniecinski I, Seghatchian J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine[J]. Transfus Apher Sci, 2018, 57(3): 422-424. doi: 10.1016/j.transci.2018.05.004

    [2]

    Szaleniec M, Witko M, Tadeusiewicz R, et al. Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase[J]. J Comput Aided Mol Des, 2006, 20(3): 145-157. doi: 10.1007/s10822-006-9042-6

    [3]

    Szaleniec M, Dudzik A, Pawul M, et al. Quantitative structure enantioselective retention relationship for high-performance liquid chromatography chiral separation of 1-phenylethanol derivatives[J]. J Chromatogr A, 2009, 1216(34): 6224-6235. doi: 10.1016/j.chroma.2009.07.002

    [4]

    Waligórski P, Szaleniec M. Prediction of white cabbage(Brassica oleracea var. capitata)self-incompatibility based on neural network and discriminant analysis of complex electrophoretic patterns[J]. Comput Biol Chem, 2010, 34(2): 115-121. doi: 10.1016/j.compbiolchem.2010.03.002

    [5]

    Szaleniec M. Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates[J]. Pharmacol Rep, 2012, 64(4): 761-781. doi: 10.1016/S1734-1140(12)70873-3

    [6]

    William W, Ware A, Basaza-Ejiri AH, et al. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images[J]. Comput MethodsPrograms Biomed, 2018, 164: 15-22.

    [7]

    Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram[J]. Nat Med, 2019, 25(1): 70-74. doi: 10.1038/s41591-018-0240-2

    [8]

    Ravizza S, Huschto T, Adamov A, et al. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data[J]. Nat Med, 2019, 25(1): 57-59. doi: 10.1038/s41591-018-0239-8

    [9]

    Bashiri A, Ghazisaeedi M, Safdari R, et al. Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review[J]. Iran J Public Health, 2017, 46(2): 165-172.

    [10]

    Stepp WH, Farquhar D, Sheth S, et al. RNA Oncoimmune Phenotyping of HPV-Positive p16-Positive Oropharyngeal Squamous Cell Carcinomas by Nodal Status[J]. JAMA Otolaryngol Head Neck Surg, 2018, 144(11): 967-975. doi: 10.1001/jamaoto.2018.0602

    [11]

    Bickelhaupt S, Paech D, Kickingereder P, et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography[J]. J Magn Reson Imaging, 2017, 46(2): 604-616. doi: 10.1002/jmri.25606

    [12]

    Coroller TP, Agrawal V, Narayan V, er al. Radiomic phenotype fetures predict pathological response in nonsmall cell lung cancer[J]. Radiother Oncol, 2016, 119(3): 480-486. doi: 10.1016/j.radonc.2016.04.004

    [13]

    蔡建鹏, 陈伟, 陈流华, 等. 机器人辅助肝切除术: 附71例报告〔J/OL〕. 中华肝脏外科手术学电子杂志, 2019, 8(3): 217-220. doi: 10.3877/cma.j.issn.2095-3232.2019.03.009

    [14]

    Park EJ, Cho MS, Baek SJ, et al. Long-term oncologic outcomes of robotic low anterior resection for rectal cancer: a comparative study with laparoscopic surgery[J]. Ann Surg, 2015, 261(1): 129-137. doi: 10.1097/SLA.0000000000000613

    [15]

    He H, Wu Q, Wang Z, et al. Short-term outcomes of robot-assisted minimally invasive esophagectomy for esophageal cancer: a propensity score matvhed analysis[J]. J Caediothorac Surg, 2018, 13(1): 52. doi: 10.1186/s13019-018-0727-4

    [16]

    Whipple ME, Mendez E, Farwell DG, et al. A genomic predictor of oral squamous cell carcinoma[J]. Laryngoscope, 2004, 114(8): 1346-1354. doi: 10.1097/00005537-200408000-00006

    [17]

    Bonadonna F. HyperShell: an expert system shell in a hypermedia environment--application in medical audiology[J]. Med Inform(Lond), 1990, 15(2): 105-114. doi: 10.3109/14639239008997662

    [18]

    Ossoff RH, Reinisch L. Computer-assisted surgical techniques: a vision for the future of otolaryngology-head and neck surgery[J]. J Otolaryngol, 1994, 23(5): 354-359. doi: 10.1016/0277-9536(94)90202-X

    [19]

    Buckingham RA, Buckingham RO. Robots in operating theatres[J]. BMJ, 1995, 311(7018): 1479-1482. doi: 10.1136/bmj.311.7018.1479

    [20]

    Kentala E, Pyykkö I, Auramo Y, et al. Otoneurological expert system[J]. Ann Otol Rhinol Laryngol, 1996, 105(8): 654-658. doi: 10.1177/000348949610500812

    [21]

    Juhola M, Viikki K, Laurikkala J, et al. Application of artificial intelligence in audiology[J]. Scand Audiol Suppl, 2001, 52: 97-99.

    [22]

    Ozer E, Waltonen J. Transoral robotic nasopharyngectomy: a novel approach for nasopharyngeal lesions[J]. Laryngoscope, 2008, 118(9): 1613-1616. doi: 10.1097/MLG.0b013e3181792490

    [23]

    Ghanem TA. Transoral robotic-assisted microvascular reconstruction of the oropharynx[J]. Laryngoscope, 2011, 121(3): 580-582. doi: 10.1002/lary.21428

    [24]

    Tülin Kayhan F, Hakan Kaya K, Altıntas A, et al. First successful transoral robotic resection of a laryngeal paraganglioma[J]. J Otolaryngol Head Neck Surg, 2012, 41(6): E54-57.

    [25]

    Sharma A, Albergotti WG, Duvvuri U. Applications of Evolving Robotic Technology for Head and Neck Surgery[J]. Ann Otol Rhinol Laryngol, 2016, 125(3): 207-212. doi: 10.1177/0003489415606448

    [26]

    Bur AM, Holcomb A, Goodwin S, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma[J]. Oral Oncol, 2019, 92: 20-25. doi: 10.1016/j.oraloncology.2019.03.011

    [27]

    Szaleniec J, Wiatr M, Szaleniec M, et al. Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitismedia patients[J]. Comput Biol Med, 2013, 43(1): 16-22. doi: 10.1016/j.compbiomed.2012.10.003

    [28]

    Bing D, Ying J, Miao J, et al. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models[J]. Clin Otolaryngol, 2018, 43(3): 868-874. doi: 10.1111/coa.13068

    [29]

    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

    [30]

    Somashekhar SP, Sepúlveda MJ, Puglielli S, et al. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board[J]. Ann Oncol, 2018, 29(2): 418-423. doi: 10.1093/annonc/mdx781

    [31]

    Halicek M, Lu G, Little JV, et al. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging[J]. J Biomed Opt, 2017, 22(6): 60503. doi: 10.1117/1.JBO.22.6.060503

    [32]

    Mahmood R, Babier A, McNiven A, et al. Automated treatment planning in radiation therapy using generative adversarial networks[J]. Proc Mach Learn Res, 2018, 85: 1-15. doi: 10.48550/arXiv.1807.06489

    [33]

    Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157.

    [34]

    Zacharaki EI, Wang S, Chawla S, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme[J]. Magn Reson Med, 2009, 62(6): 1609-1618. doi: 10.1002/mrm.22147

    [35]

    Kann BH, Aneja S, Loganadane GV, et al. Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks[J]. Sci Rep, 2018, 8(1): 14036. doi: 10.1038/s41598-018-32441-y

    [36]

    Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. doi: 10.1038/nature21056

    [37]

    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

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出版历程
收稿日期:  2019-09-23
刊出日期:  2020-12-05

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