-
Abstract: Artificial intelligence, as the forefront of science and technology, has been emerging in all walks of life, and has now become the main research direction of medical care. Many researchers have begun to research and develop this technology, and will use this technology to help clinical work. Due to otolaryngology head and neck surgery as a minimally invasive surgery with complex anatomy, artificial intelligence is bound to play a crucial role in otolaryngology. With the development of 5G network, artificial intelligence will develop with8 130 8432 double force.
-
Key words:
- artificial intelligence /
- machine learning /
- neural networks /
- robotic surgeon
-
-
[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
-
计量
- 文章访问数: 1616
- PDF下载数: 309
- 施引文献: 0