Application of artificial intelligence in otolaryngology teaching_West China Medical Journal

Authors：

ZHANG Yuyang ^1,2 , ZHAO Yu ¹ ,  REN Jianjun ¹

1. Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

REN Jianjun, Email: Jianjun.Ren@scu.edu.cn

Keywords：

Artificial intelligence; otolaryngology; medical education

DOI：

10.7507/1002-0179.202201046

Video：

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With the development of computer technology, artificial intelligence (AI) has gradually been applied to various industries in society. In the healthcare industry, AI provides more choices for disease diagnosis and treatment, and also brings new vitality to the development of clinical medicine. In order to better promote the use of AI technology to improve the quality of otolaryngology teaching, this article provides a brief overview of the application of AI in otolaryngology, including the use of neural networks, deep learning for image analysis, disease diagnosis and treatment. It also discusses the significance and implementation methods of AI application in otolaryngology teaching from several aspects such as course design, teaching practice, and effectiveness assessment.

Citation： ZHANG Yuyang, ZHAO Yu, REN Jianjun. Application of artificial intelligence in otolaryngology teaching. West China Medical Journal, 2023, 38(4): 587-590. doi: 10.7507/1002-0179.202201046 Copy

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2.	王迪, 程金章. 基于机器学习的人工智能技术在耳鼻喉科临床诊疗中的应用进展. 山东大学耳鼻喉眼学报, 2021, 35(6): 125-131.
3.	Crowson MG, Ranisau J, Eskander A, et al. A contemporary review of machine learning in otolaryngology-head and neck surgery. Laryngoscope, 2020, 130(1): 45-51.
4.	齐静怀, 张良. 人工智能时代的耳鼻咽喉头颈外科. 临床耳鼻咽喉头颈外科杂志, 2020, 34(12): 1137-1140.
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6.	Cai Y, Yu JG, Chen Y, et al. Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study. BMJ Open, 2021, 11(1): e041139.
7.	Bing D, Ying J, Miao J, et al. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol, 2018, 43(3): 868-874.
8.	Wang YM, Li Y, Cheng YS, et al. Deep learning in automated region proposal and diagnosis of chronic otitis media based on computed tomography. Ear Hear, 2020, 41(3): 669-677.
9.	王江, 柯嘉, 马芙蓉. 耳科手术机器人的研究进展. 中华耳鼻咽喉头颈外科杂志, 2019, 54(12): 944-948.
10.	Ozer E, Waltonen J. Transoral robotic nasopharyngectomy: a novel approach for nasopharyngeal lesions. Laryngoscope, 2008, 118(9): 1613-1616.
11.	Girdler B, Moon H, Bae MR, et al. Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images. Int Forum Allergy Rhinol, 2021, 11(12): 1637-1646.
12.	Wu Q, Chen J, Ren Y, et al. Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging. EBioMedicine, 2021, 66: 103336.
13.	Fang SH, Tsao Y, Hsiao MJ, et al. Detection of pathological voice using cepstrum vectors: a deep learning approach. J Voice, 2019, 33(5): 634-641.
14.	胡蓉, 钟琦, 徐文, 等. 基于深度卷积神经网络的人工智能在喉鳞状细胞癌窄带成像辅助诊断中的应用. 中华耳鼻咽喉头颈外科杂志, 2021, 56(5): 454-458.
15.	Ren J, Jing X, Wang J, et al. Automatic recognition of laryngoscopic images using a deep-learning technique. Laryngoscope, 2020, 130(11): E686-E693.
16.	高颖娜, 陈世彩, 王伟, 等. 耳鼻咽喉头颈外科教学中三维虚拟解剖系统设计. 解放军医院管理杂志, 2021, 28(5): 455-456.
17.	孙秀伟, 阎丽, 李彦锋. 虚拟现实技术(VR)在医疗中的应用展望. 医疗保健器具, 2007(5): 17-20.
18.	钟诚, 张学渊, 姜振东, 等. 以学生为中心的耳鼻咽喉头颈外科学本科临床教学体系构建及意义. 中华医学教育探索杂志, 2013, 12(2): 173-175.
19.	王承龙, 田勇泉, 梅凌云, 等. 耳鼻咽喉头颈外科学课堂教学质量的探讨与思考. 中国耳鼻咽喉颅底外科杂志, 2013, 19(6): 562-563, 567.
20.	Sperry SM, O’Malley BW Jr, Weinstein GS. The university of pennsylvania curriculum for training otorhinolaryngology residents in transoral robotic surgery. ORL J Otorhinolaryngol Relat Spec, 2014, 76(6): 342-352.
21.	Curry M, Malpani A, Li R, et al. Objective assessment in residency-based training for transoral robotic surgery. Laryngoscope, 2012, 122(10): 2184-2192.
22.	Tseng WH, Lee MS, Wang CC, et al. Objective evaluation of biomaterial effects after injection laryngoplasty - Introduction of artificial intelligence-based ultrasonic image analysis. Clin Otolaryngol, 2021, 46(5): 1028-1036.
23.	Ryu S, Kim JH, Yu H, et al. Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: computational fluid dynamics and artificial intelligence approach. Comput Methods Programs Biomed, 2021, 208: 106243.
24.	Jeon Y, Lee K, Sunwoo L, et al. Deep learning for diagnosis of paranasal sinusitis using multi-view radiographs. Diagnostics (Basel), 2021, 11(2): 250.
25.	Parmar P, Habib AR, Mendis D, et al. An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans. J Laryngol Otol, 2020, 134(4): 328-331.
26.	Cho YS, Cho K, Park CJ, et al. Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation. Sci Rep, 2020, 10(1): 7003.
27.	李熠, 匡双玉, 桂庆军, 等. 人工智能在医学生临床技能培养中的应用探讨. 医学教育研究与实践, 2018, 26(6): 908-910, 992.
28.	Bur AM, Shew M, New J. Artificial intelligence for the otolaryngologist: a state of the art review. Otolaryngol Head Neck Surg, 2019, 160(4): 603-611.
29.	Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA, 2017, 318(6): 517-518.

1. Sniecinski I, Seghatchian J. Artificial intelligence: a joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfus Apher Sci, 2018, 57(3): 422-424.
2. 王迪, 程金章. 基于机器学习的人工智能技术在耳鼻喉科临床诊疗中的应用进展. 山东大学耳鼻喉眼学报, 2021, 35(6): 125-131.
3. Crowson MG, Ranisau J, Eskander A, et al. A contemporary review of machine learning in otolaryngology-head and neck surgery. Laryngoscope, 2020, 130(1): 45-51.
4. 齐静怀, 张良. 人工智能时代的耳鼻咽喉头颈外科. 临床耳鼻咽喉头颈外科杂志, 2020, 34(12): 1137-1140.
5. Wu Z, Lin Z, Li L, et al. Deep learning for classification of pediatric otitis media. Laryngoscope, 2021, 131(7): E2344-E2351.
6. Cai Y, Yu JG, Chen Y, et al. Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study. BMJ Open, 2021, 11(1): e041139.
7. Bing D, Ying J, Miao J, et al. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol, 2018, 43(3): 868-874.
8. Wang YM, Li Y, Cheng YS, et al. Deep learning in automated region proposal and diagnosis of chronic otitis media based on computed tomography. Ear Hear, 2020, 41(3): 669-677.
9. 王江, 柯嘉, 马芙蓉. 耳科手术机器人的研究进展. 中华耳鼻咽喉头颈外科杂志, 2019, 54(12): 944-948.
10. Ozer E, Waltonen J. Transoral robotic nasopharyngectomy: a novel approach for nasopharyngeal lesions. Laryngoscope, 2008, 118(9): 1613-1616.
11. Girdler B, Moon H, Bae MR, et al. Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images. Int Forum Allergy Rhinol, 2021, 11(12): 1637-1646.
12. Wu Q, Chen J, Ren Y, et al. Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging. EBioMedicine, 2021, 66: 103336.
13. Fang SH, Tsao Y, Hsiao MJ, et al. Detection of pathological voice using cepstrum vectors: a deep learning approach. J Voice, 2019, 33(5): 634-641.
14. 胡蓉, 钟琦, 徐文, 等. 基于深度卷积神经网络的人工智能在喉鳞状细胞癌窄带成像辅助诊断中的应用. 中华耳鼻咽喉头颈外科杂志, 2021, 56(5): 454-458.
15. Ren J, Jing X, Wang J, et al. Automatic recognition of laryngoscopic images using a deep-learning technique. Laryngoscope, 2020, 130(11): E686-E693.
16. 高颖娜, 陈世彩, 王伟, 等. 耳鼻咽喉头颈外科教学中三维虚拟解剖系统设计. 解放军医院管理杂志, 2021, 28(5): 455-456.
17. 孙秀伟, 阎丽, 李彦锋. 虚拟现实技术(VR)在医疗中的应用展望. 医疗保健器具, 2007(5): 17-20.
18. 钟诚, 张学渊, 姜振东, 等. 以学生为中心的耳鼻咽喉头颈外科学本科临床教学体系构建及意义. 中华医学教育探索杂志, 2013, 12(2): 173-175.
19. 王承龙, 田勇泉, 梅凌云, 等. 耳鼻咽喉头颈外科学课堂教学质量的探讨与思考. 中国耳鼻咽喉颅底外科杂志, 2013, 19(6): 562-563, 567.
20. Sperry SM, O’Malley BW Jr, Weinstein GS. The university of pennsylvania curriculum for training otorhinolaryngology residents in transoral robotic surgery. ORL J Otorhinolaryngol Relat Spec, 2014, 76(6): 342-352.
21. Curry M, Malpani A, Li R, et al. Objective assessment in residency-based training for transoral robotic surgery. Laryngoscope, 2012, 122(10): 2184-2192.
22. Tseng WH, Lee MS, Wang CC, et al. Objective evaluation of biomaterial effects after injection laryngoplasty - Introduction of artificial intelligence-based ultrasonic image analysis. Clin Otolaryngol, 2021, 46(5): 1028-1036.
23. Ryu S, Kim JH, Yu H, et al. Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: computational fluid dynamics and artificial intelligence approach. Comput Methods Programs Biomed, 2021, 208: 106243.
24. Jeon Y, Lee K, Sunwoo L, et al. Deep learning for diagnosis of paranasal sinusitis using multi-view radiographs. Diagnostics (Basel), 2021, 11(2): 250.
25. Parmar P, Habib AR, Mendis D, et al. An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans. J Laryngol Otol, 2020, 134(4): 328-331.
26. Cho YS, Cho K, Park CJ, et al. Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation. Sci Rep, 2020, 10(1): 7003.
27. 李熠, 匡双玉, 桂庆军, 等. 人工智能在医学生临床技能培养中的应用探讨. 医学教育研究与实践, 2018, 26(6): 908-910, 992.
28. Bur AM, Shew M, New J. Artificial intelligence for the otolaryngologist: a state of the art review. Otolaryngol Head Neck Surg, 2019, 160(4): 603-611.
29. Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA, 2017, 318(6): 517-518.

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Application of artificial intelligence in otolaryngology teaching

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