[1] |
Mannelli G, Cecconi L, Gallo O. Laryngeal preneoplastic lesions and cancer: challenging diagnosis. Qualitative literature review and meta-analysis[J]. Crit Rev Oncol Hematol, 2016, 106: 64-90. DOI: 10.1016/j.critrevonc.2016.07.004.
pmid: 27637353
|
[2] |
Brandstorp-Boesen J, Sørum Falk R, Boysen M, et al. Impact of stage, management and recurrence on survival rates in laryngeal cancer[J]. PLoS One, 2017, 12(7): e0179371. DOI: 10.1371/journal.pone.0179371.
|
[3] |
Kim DH, Kim Y, Kim SW, et al. Use of narrowband imaging for the diagnosis and screening of laryngeal cancer: a systematic review and meta-analysis[J]. Head Neck, 2020, 42(9): 2635-2643. DOI: 10.1002/hed.26186.
|
[4] |
Paderno A, Holsinger FC, Piazza C. Videomics: bringing deep learning to diagnostic endoscopy[J]. Curr Opin Otolaryngol Head Neck Surg, 2021, 29(2): 143-148. DOI: 10.1097/MOO.0000000000000697.
pmid: 33595977
|
[5] |
Esmaeili N, Davaris N, Boese A, et al. Contact endoscopy-narrow band imaging (CE-NBI) data set for laryngeal lesion assessment[J]. Sci Data, 2023, 10(1): 733. DOI: 10.1038/s41597-023-02629-7.
pmid: 37865668
|
[6] |
张岩. 基于深度学习的喉内镜早期诊断研究[D]. 呼和浩特: 内蒙古医科大学, 2023. DOI: 10.27231/d.cnki.gnmyc.2023.000481.
|
[7] |
Kim GH, Hwang YJ, Lee H, et al. Convolutional neural network-based vocal cord tumor classification technique for home-based self-prescreening purpose[J]. Biomed Eng Online, 2023, 22(1): 81. DOI: 10.1186/s12938-023-01139-2.
pmid: 37596652
|
[8] |
You Z, Han B, Shi Z, et al. Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images[J]. Head Neck, 2023, 45(12): 3129-3145. DOI: 10.1002/hed.27543.
|
[9] |
You Z, Han B, Shi Z, et al. Vocal cord leukoplakia classification using siamese network under small samples of white light endoscopy images[J]. Otolaryngol Head Neck Surg, 2024, 170(4): 1099-1108. DOI: 10.1002/ohn.591.
|
[10] |
Lubrano M, Bellahsen-Harrar Y, Berlemont S, et al. Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia[J]. Histopathology, 2024, 84(2): 343-355. DOI: 10.1111/his.15067.
|
[11] |
Eckel HE, Simo R, Quer M, et al. European laryngological society position paper on laryngeal dysplasia part Ⅱ: diagnosis, treatment, and follow-up[J]. Eur Arch Otorhinolaryngol, 2021, 278(6): 1723-1732. DOI: 10.1007/s00405-020-06406-9.
|
[12] |
Lin K, Cheng DLP, Huang Z. Optical diagnosis of laryngeal cancer using high wavenumber Raman spectroscopy[J]. Biosens Bioelectron, 2012, 35(1): 213-217. DOI: 10.1016/j.bios.2012.02.050.
pmid: 22465448
|
[13] |
Hancock S, Bowman E, Prabakaran J, et al. Use of i-scan endoscopic image enhancement technology in clinical practice to assist in diagnostic and therapeutic endoscopy: a case series and review of the literature[J]. Diagn Ther Endosc, 2012, 2012: 193570. DOI: 10.1155/2012/193570.
|
[14] |
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.
|
[15] |
Xiong H, Lin P, Yu JG, et al. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images[J]. EBioMedicine, 2019, 48: 92-99. DOI: 10.1016/j.ebiom.2019.08.075.
pmid: 31594753
|
[16] |
Yan PK, Li SH, Zhou Z, et al. Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network[J]. Clin Otolaryngol, 2023, 48(3): 436-441. DOI: 10.1111/coa.14029.
|
[17] |
Zhao Q, He YQ, Wu YD, et al. Vocal cord lesions classification based on deep convolutional neural network and transfer learning[J]. Med Phys, 2022, 49(1): 432-442. DOI: 10.1002/mp.15371.
|
[18] |
Dunham ME, Kong KA, McWhorter AJ, et al. Optical biopsy: automated classification of airway endoscopic findings using a convolutional neural network[J]. Laryngoscope, 2022, 132 Suppl 4: S1-S8. DOI: 10.1002/lary.28708.
|
[19] |
Kim HB, 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.
|
[20] |
Kwon I, Wang SG, Shin SC, et al. Diagnosis of early glottic cancer using laryngeal image and voice based on ensemble learning of convolutional neural network classifiers[J/OL]. J Voice, 2022: S0892- 1997(22)00209. DOI: 10.1016/j.jvoice.2022.07.007. https://pubmed.ncbi.nlm.nih.gov/36075802/.
|
[21] |
Unger J, Lohscheller J, Reiter M, et al. A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis[J]. Cancer Res, 2015, 75(1): 31-39. DOI: 10.1158/0008-5472.CAN-14-1458.
pmid: 25371410
|
[22] |
Matava C, Pankiv E, Raisbeck S, et al. A convolutional neural network for real time classification, identification, and labelling of vocal cord and tracheal using laryngoscopy and bronchoscopy video[J]. J Med Syst, 2020, 44(2): 44. DOI: 10.1007/s10916-019-1481-4.
pmid: 31897740
|
[23] |
Azam MA, Sampieri C, Ioppi A, et al. Videomics of the upper aero-digestive tract cancer: deep learning applied to white light and narrow band imaging for automatic segmentation of endoscopic images[J]. Front Oncol, 2022, 12: 900451. DOI: 10.3389/fonc.2022.900451.
|
[24] |
Nakayama MJ, Katada C, Mikami TT, et al. A clinical study of transoral pharyngectomies to treat superficial hypopharyngeal cancers[J]. Jpn J Clin Oncol, 2013, 43(8): 782-787. DOI: 10.1093/jjco/hyt081.
pmid: 23749982
|
[25] |
Yumii K, Ueda T, Kawahara D, et al. Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer[J]. Auris Nasus Larynx, 2024, 51(2): 417-424. DOI: 10.1016/j.anl.2023.09.001.
|
[26] |
Nakajo K, Ninomiya Y, Kondo H, et al. Anatomical classification of pharyngeal and laryngeal endoscopic images using artificial intelligence[J]. Head Neck, 2023, 45(6): 1549-1557. DOI: 10.1002/hed.27370.
|
[27] |
Zhu JQ, Wang ML, Li Y, et al. Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study[J]. Am J Otolaryngol, 2023, 44(2): 103695. DOI: 10.1016/j.amjoto.2022.103695.
|
[28] |
王美玲, 朱继庆, 李莹, 等. 基于卷积神经网络的喉镜图像解剖部位自动识别的研究[J]. 临床耳鼻咽喉头颈外科杂志, 2023, 37(1): 6-12. DOI: 10.13201/j.issn.2096-7993.2023.01.002.
|
[29] |
Wellenstein DJ, Woodburn J, Marres HAM, et al. Detection of laryngeal carcinoma during endoscopy using artificial intelligence[J]. Head Neck, 2023, 45(9): 2217-2226. DOI: 10.1002/hed.27441.
|
[30] |
Kim GH, Sung ES, Nam KW. Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network[J]. Biomed Eng Online, 2021, 20(1): 51. DOI: 10.1186/s12938-021-00886-4.
pmid: 34034766
|
[31] |
Tsilivigkos C, Athanasopoulos M, Micco RD, et al. Deep learning techniques and imaging in otorhinolaryngology-a state-of-the-art review[J]. J Clin Med, 2023, 12(22): 6973. DOI: 10.3390/jcm12226973.
|
[32] |
Yao P, Witte D, Gimonet H, et al. Automatic classification of informative laryngoscopic images using deep learning[J]. Laryngoscope Investig Otolaryngol, 2022, 7(2): 460-466. DOI: 10.1002/lio2.754.
pmid: 35434326
|
[33] |
Azam MA, Sampieri C, Ioppi A, et al. Deep learning applied to white light and narrow band imaging videolaryngoscopy: toward real-time laryngeal cancer detection[J]. Laryngoscope, 2022, 132(9): 1798-1806. DOI: 10.1002/lary.29960.
|
[34] |
Paderno A, Gennarini F, Sordi A, et al. Artificial intelligence in clinical endoscopy: insights in the field of videomics[J]. Front Surg, 2022, 9: 933297. DOI: 10.3389/fsurg.2022.933297.
|
[35] |
Patrini I, Ruperti M, Moccia S, et al. Transfer learning for informative-frame selection in laryngoscopic videos through learned features[J]. Med Biol Eng Comput, 2020, 58(6): 1225-1238. DOI: 10.1007/s11517-020-02127-7.
pmid: 32212052
|
[36] |
Esteva A, Chou K, Yeung S, et al. Deep learning-enabled medical computer vision[J]. NPJ Digit Med, 2021, 4(1): 5. DOI: 10.1038/s41746-020-00376-2.
pmid: 33420381
|
[37] |
吴佩燕. 基于人工智能技术辅助诊断喉癌术后局部复发的初步研究[D]. 广州: 南方医科大学, 2023. DOI: 10.27003/d.cnki.gojyu.2023.000928.
|
[38] |
Bensoussan Y, Vanstrum EB, Johns MM3, et al. Artificial intelligence and laryngeal cancer: from screening to prognosis: a state of the art review[J]. Otolaryngol Head Neck Surg, 2023, 168(3): 319-329. DOI: 10.1177/01945998221110839.
|
[39] |
严萍, 袁湘蕾, 张琼英, 等. 人工智能在浅表食管鳞状细胞癌及癌前病变内镜诊断中的应用进展[J]. 中国胸心血管外科临床杂志, 2022, 29(9): 1217-1222. DOI: 10.7507/1007-4848.202203047.
|