国际肿瘤学杂志››2024,Vol. 51››Issue (5): 303-307.doi:10.3760/cma.j.cn371439-20240318-00051
收稿日期:
2024-03-18修回日期:
2024-04-03出版日期:
2024-05-08发布日期:
2024-06-26通讯作者:
雷大鹏,Email:leidapeng@sdu.edu.cn基金资助:
Gu Fangmeng, Xu Chenyang, Lei Dapeng()
Received:
2024-03-18Revised:
2024-04-03Online:
2024-05-08Published:
2024-06-26Contact:
Lei Dapeng, Email:leidapeng@sdu.edu.cnSupported by:
摘要:
由于喉部重要的解剖位置和生理功能,喉部病变对患者的发音、生命质量甚至生存预后均有可能造成严重影响,对其早诊早治至关重要。电子喉镜是早期诊治喉癌及喉癌前病变最重要的辅助工具。近年来,人工智能技术的迅猛发展使其在喉镜检查领域的应用及研究日益增加,其在辅助诊断、质量控制和治疗后病情评估等方面展现出巨大的潜力和价值,未来有望成为内镜医师临床决策、早期诊治喉癌的得力助手。
顾芳萌, 徐晨阳, 雷大鹏. 人工智能辅助电子喉镜检查在喉癌及喉癌前病变诊治中的研究进展[J]. 国际肿瘤学杂志, 2024, 51(5): 303-307.
Gu Fangmeng, Xu Chenyang, Lei Dapeng. Research progress on artificial intelligence-assisted electronic laryngoscopy in the diagnosis and treatment of laryngeal cancer and laryngeal precancerous lesions[J]. Journal of International Oncology, 2024, 51(5): 303-307.
[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. |
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