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Cai Y, Zhang X, Cao J, Grzybowski A, Ye J, Lou L. Application of artificial intelligence in oculoplastics. Clin Dermatol 2024; 42:259-267. [PMID: 38184122 DOI: 10.1016/j.clindermatol.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Abstract
Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.
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Affiliation(s)
- Yilu Cai
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Xuan Zhang
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Jing Cao
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Lixia Lou
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
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Bao XL, Sun YJ, Zhan X, Li GY. Orbital and eyelid diseases: The next breakthrough in artificial intelligence? Front Cell Dev Biol 2022; 10:1069248. [PMID: 36467418 PMCID: PMC9716028 DOI: 10.3389/fcell.2022.1069248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/08/2022] [Indexed: 12/07/2023] Open
Abstract
Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.
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Affiliation(s)
- Xiao-Li Bao
- Department of Ophthalmology, Second Hospital of Jilin University, Changchun, China
| | - Ying-Jian Sun
- Department of Ophthalmology, Second Hospital of Jilin University, Changchun, China
| | - Xi Zhan
- Department of Engineering, The Army Engineering University of PLA, Nanjing, China
| | - Guang-Yu Li
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, China
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Tabuchi H. Understanding required to consider AI applications to the field of ophthalmology. Taiwan J Ophthalmol 2022; 12:123-129. [PMID: 35813809 PMCID: PMC9262026 DOI: 10.4103/tjo.tjo_8_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/28/2022] [Indexed: 11/04/2022] Open
Abstract
Applications of artificial intelligence technology, especially deep learning, in ophthalmology research have started with the diagnosis of diabetic retinopathy and have now expanded to all areas of ophthalmology, mainly in the identification of fundus diseases such as glaucoma and age-related macular degeneration. In addition to fundus photography, optical coherence tomography is often used as an imaging device. In addition to simple binary classification, region identification (segmentation model) is used as an identification method for interpretability. Furthermore, there have been AI applications in the area of regression estimation, which is different from diagnostic identification. While expectations for deep learning AI are rising, regulatory agencies have begun issuing guidance on the medical applications of AI. The reason behind this trend is that there are a number of existing issues regarding the application of AI that need to be considered, including, but not limited to, the handling of personal information by large technology companies, the black-box issue, the flaming issue, the theory of responsibility, and issues related to improving the performance of commercially available AI. Furthermore, researchers have reported that there are a plethora of issues that simply cannot be solved by the high performance of artificial intelligence models, such as educating users and securing the communication environment, which are just a few of the necessary steps toward the actual implementation process of an AI society. Multifaceted perspectives and efforts are needed to create better ophthalmology care through AI.
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