Expert recommendations on collection and annotation of otoscopy images for intelligent medicine
Middle and outer ear diseases are common otological diseases worldwide. Otoscopy and otoendoscopy examinations are essential first steps in the evaluation of patients with otological diseases. Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoen...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Intelligent Medicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667102622000043 |
| _version_ | 1811196812599492608 |
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| author | Yuexin Cai Junbo Zeng Liping Lan Suijun Chen Yongkang Ou Linqi Zeng Qintai Yang Peng Li Yubin Chen Qi Li Hongzheng Zhang Fan Shu Guoping Chen Wenben Chen Yahan Yang Ruiyang Li Anqi Yan Haotian Lin Yiqing Zheng |
| author_facet | Yuexin Cai Junbo Zeng Liping Lan Suijun Chen Yongkang Ou Linqi Zeng Qintai Yang Peng Li Yubin Chen Qi Li Hongzheng Zhang Fan Shu Guoping Chen Wenben Chen Yahan Yang Ruiyang Li Anqi Yan Haotian Lin Yiqing Zheng |
| author_sort | Yuexin Cai |
| collection | DOAJ |
| description | Middle and outer ear diseases are common otological diseases worldwide. Otoscopy and otoendoscopy examinations are essential first steps in the evaluation of patients with otological diseases. Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy, leading to delays in treatment or complications. Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future. However, the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems, and no standardized process for data acquisition, and annotation of otoscopy images in intelligent medicine has yet been fully established. The standards for data storage and data management are unified with those of other specialties and are introduced in detail here. This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine; it would thus lay a solid foundation for the standardized collection, storage, and annotation of otoscopy images and the application of training algorithms, and promote the development of automatic diagnosis and treatment for otological diseases. The full text introduced image collection (including patient preparation, equipment standards, and image storage), image annotation standards, and quality control. |
| first_indexed | 2024-04-12T01:05:26Z |
| format | Article |
| id | doaj.art-3c33c351733941bead58807c188263cd |
| institution | Directory Open Access Journal |
| issn | 2667-1026 |
| language | English |
| last_indexed | 2024-04-12T01:05:26Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Medicine |
| spelling | doaj.art-3c33c351733941bead58807c188263cd2022-12-22T03:54:18ZengElsevierIntelligent Medicine2667-10262022-11-0124230234Expert recommendations on collection and annotation of otoscopy images for intelligent medicineYuexin Cai0Junbo Zeng1Liping Lan2Suijun Chen3Yongkang Ou4Linqi Zeng5Qintai Yang6Peng Li7Yubin Chen8Qi Li9Hongzheng Zhang10Fan Shu11Guoping Chen12Wenben Chen13Yahan Yang14Ruiyang Li15Anqi Yan16Haotian Lin17Yiqing Zheng18Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China; Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong 516600, ChinaDepartment of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, ChinaDepartment of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, ChinaDepartment of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, ChinaDepartment of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, ChinaDepartment of Otolaryngology Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, ChinaDepartment of Otolaryngology Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, ChinaDepartment of Otolaryngology Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, ChinaDepartment of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, South Medical University, Guangzhou, Guangdong 510515, ChinaDepartment of Otolaryngology-Head & Neck Surgery, Zhujiang Hospital, South Medical University, Guangzhou, Guangdong 510280, ChinaDepartment of Otolaryngology-Head & Neck Surgery, Zhujiang Hospital, South Medical University, Guangzhou, Guangdong 510280, ChinaDepartment of Otolaryngology, Zhongshan City People's Hospital, Zhongshan Affiliated Hospital of Sun Yat-sen University, Zhongshan, Guangdong 528403, ChinaZhongshan Ophthalmic Center, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou, Guangdong 510623, ChinaZhongshan Ophthalmic Center, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou, Guangdong 510623, ChinaZhongshan Ophthalmic Center, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou, Guangdong 510623, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, ChinaZhongshan Ophthalmic Center, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou, Guangdong 510623, China; Corresponding authors: Yiqing Zheng, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107# Yanjiang West Road, Guangzhou, Guangdong 510120, China (Email: zhengyiq@mail.sysu.edu.cn); Haotian Lin, Zhongshan Ophthalmic Center, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou, Guangdong 510623, China (Email: linht5@mail.sysu.edu.cn).Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China; Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong 516600, China; Corresponding authors: Yiqing Zheng, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107# Yanjiang West Road, Guangzhou, Guangdong 510120, China (Email: zhengyiq@mail.sysu.edu.cn); Haotian Lin, Zhongshan Ophthalmic Center, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou, Guangdong 510623, China (Email: linht5@mail.sysu.edu.cn).Middle and outer ear diseases are common otological diseases worldwide. Otoscopy and otoendoscopy examinations are essential first steps in the evaluation of patients with otological diseases. Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy, leading to delays in treatment or complications. Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future. However, the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems, and no standardized process for data acquisition, and annotation of otoscopy images in intelligent medicine has yet been fully established. The standards for data storage and data management are unified with those of other specialties and are introduced in detail here. This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine; it would thus lay a solid foundation for the standardized collection, storage, and annotation of otoscopy images and the application of training algorithms, and promote the development of automatic diagnosis and treatment for otological diseases. The full text introduced image collection (including patient preparation, equipment standards, and image storage), image annotation standards, and quality control.http://www.sciencedirect.com/science/article/pii/S2667102622000043Otoscopy imageIntelligent medicineAnnotationCollection |
| spellingShingle | Yuexin Cai Junbo Zeng Liping Lan Suijun Chen Yongkang Ou Linqi Zeng Qintai Yang Peng Li Yubin Chen Qi Li Hongzheng Zhang Fan Shu Guoping Chen Wenben Chen Yahan Yang Ruiyang Li Anqi Yan Haotian Lin Yiqing Zheng Expert recommendations on collection and annotation of otoscopy images for intelligent medicine Intelligent Medicine Otoscopy image Intelligent medicine Annotation Collection |
| title | Expert recommendations on collection and annotation of otoscopy images for intelligent medicine |
| title_full | Expert recommendations on collection and annotation of otoscopy images for intelligent medicine |
| title_fullStr | Expert recommendations on collection and annotation of otoscopy images for intelligent medicine |
| title_full_unstemmed | Expert recommendations on collection and annotation of otoscopy images for intelligent medicine |
| title_short | Expert recommendations on collection and annotation of otoscopy images for intelligent medicine |
| title_sort | expert recommendations on collection and annotation of otoscopy images for intelligent medicine |
| topic | Otoscopy image Intelligent medicine Annotation Collection |
| url | http://www.sciencedirect.com/science/article/pii/S2667102622000043 |
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