1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Yi C, Niu G, Zhang Y, Rao J, Liu G, Yang W, Fei X. Advances in artificial intelligence in thyroid-associated ophthalmopathy. Front Endocrinol (Lausanne) 2024; 15:1356055. [PMID: 38715793 PMCID: PMC11075148 DOI: 10.3389/fendo.2024.1356055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/10/2024] [Indexed: 05/23/2024] Open
Abstract
Thyroid-associated ophthalmopathy (TAO), also referred to as Graves' ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.
Collapse
Affiliation(s)
- Chenyuan Yi
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Geng Niu
- School of Medical Technology and Nursing, Shenzhen Polytechnic University, Shenzhen, China
| | - Yinghuai Zhang
- Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Jing Rao
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Guiqin Liu
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Weihua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - XingZhen Fei
- Department of Endocrinology, First People’s Hospital of Huzhou, Huzhou University, Huzhou, China
| |
Collapse
|
3
|
Wu KY, Kulbay M, Daigle P, Nguyen BH, Tran SD. Nonspecific Orbital Inflammation (NSOI): Unraveling the Molecular Pathogenesis, Diagnostic Modalities, and Therapeutic Interventions. Int J Mol Sci 2024; 25:1553. [PMID: 38338832 PMCID: PMC10855920 DOI: 10.3390/ijms25031553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/21/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
Nonspecific orbital inflammation (NSOI), colloquially known as orbital pseudotumor, sometimes presents a diagnostic and therapeutic challenge in ophthalmology. This review aims to dissect NSOI through a molecular lens, offering a comprehensive overview of its pathogenesis, clinical presentation, diagnostic methods, and management strategies. The article delves into the underpinnings of NSOI, examining immunological and environmental factors alongside intricate molecular mechanisms involving signaling pathways, cytokines, and mediators. Special emphasis is placed on emerging molecular discoveries and approaches, highlighting the significance of understanding molecular mechanisms in NSOI for the development of novel diagnostic and therapeutic tools. Various diagnostic modalities are scrutinized for their utility and limitations. Therapeutic interventions encompass medical treatments with corticosteroids and immunomodulatory agents, all discussed in light of current molecular understanding. More importantly, this review offers a novel molecular perspective on NSOI, dissecting its pathogenesis and management with an emphasis on the latest molecular discoveries. It introduces an integrated approach combining advanced molecular diagnostics with current clinical assessments and explores emerging targeted therapies. By synthesizing these facets, the review aims to inform clinicians and researchers alike, paving the way for molecularly informed, precision-based strategies for managing NSOI.
Collapse
Affiliation(s)
- Kevin Y. Wu
- Department of Surgery, Division of Ophthalmology, University of Sherbrooke, Sherbrooke, QC J1G 2E8, Canada; (K.Y.W.)
| | - Merve Kulbay
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada
| | - Patrick Daigle
- Department of Surgery, Division of Ophthalmology, University of Sherbrooke, Sherbrooke, QC J1G 2E8, Canada; (K.Y.W.)
| | - Bich H. Nguyen
- CHU Sainte Justine Hospital, Montreal, QC H3T 1C5, Canada
| | - Simon D. Tran
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC H3A 1G1, Canada
| |
Collapse
|
4
|
Lin LY, Zhou P, Shi M, Lu JE, Jeon S, Kim D, Liu JM, Wang M, Do S, Lee NG. A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy. OPHTHALMOLOGY SCIENCE 2024; 4:100412. [PMID: 38046559 PMCID: PMC10692956 DOI: 10.1016/j.xops.2023.100412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 12/05/2023]
Abstract
Purpose Thyroid eye disease (TED) is an autoimmune condition with an array of clinical manifestations, which can be complicated by compressive optic neuropathy. It is important to identify patients with TED early to ensure close monitoring and treatment to prevent potential permanent disability or vision loss. Deep learning artificial intelligence (AI) algorithms have been utilized in ophthalmology and in other fields of medicine to detect disease. This study aims to introduce a deep learning model to evaluate orbital computed tomography (CT) images for the presence of TED and potential compressive optic neuropathy. Design Retrospective review and deep learning algorithm modeling. Subjects Patients with TED with dedicated orbital CT scans and with an examination by an oculoplastic surgeon over a 10-year period at a single academic institution. Patients with no TED and normal CTs were used as normal controls. Those with other diagnoses, such as tumors or other inflammatory processes, were excluded. Methods Orbital CTs were preprocessed and adopted for the Visual Geometry Group-16 network to distinguish patients with no TED, mild TED, and severe TED with compressive optic neuropathy. The primary model included training and testing of all 3 conditions. Binary model performance was also evaluated. An oculoplastic surgeon was also similarly tested with single and serial images for comparison. Main Outcome Measures Accuracy of deep learning model discernment of region of interest for CT scans to distinguish TED versus normal control, as well as TED with clinical signs of optic neuropathy. Results A total of 1187 photos from 141 patients were used to develop the AI model. The primary model trained on patients with no TED, mild TED, and severe TED had 89.5% accuracy (area under the curve: range, 0.96-0.99) in distinguishing patients with these clinical categories. In comparison, testing of an oculoplastic surgeon in these 3 categories showed decreased accuracy (70.0% accuracy in serial image testing). Conclusions The deep learning model developed in the study can accurately detect TED and further detect TED with clinical signs of optic neuropathy based on orbital CT. The model proved superior compared with human expert grading. With further optimization and validation, this TED deep learning model could help guide frontline health care providers in the detection of TED and help stratify the urgency of a referral to an oculoplastic surgeon and endocrinologist. Financial Disclosures The authors have no proprietary or commercial interest in any materials discussed in this article.
Collapse
Affiliation(s)
- Lisa Y. Lin
- Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Paul Zhou
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, California
| | - Min Shi
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Jonathan E. Lu
- Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Soomin Jeon
- Department of Information Sciences and Mathematics, Dong-A University, Busan, Republic of Korea
| | - Doyun Kim
- Data Science, Athenahealth, Watertown, Massachusetts
| | - Josephine M. Liu
- Department of Radiology, Lab of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, Massachusetts
| | - Mengyu Wang
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Synho Do
- Department of Radiology, Lab of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, Massachusetts
| | - Nahyoung Grace Lee
- Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
5
|
Chng CL, Zheng K, Kwee AK, Lee MHH, Ting D, Wong CP, Hu G, Ooi BC, Kheok SW. Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review. Front Endocrinol (Lausanne) 2023; 14:1300196. [PMID: 38174334 PMCID: PMC10761414 DOI: 10.3389/fendo.2023.1300196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/21/2023] [Indexed: 01/05/2024] Open
Abstract
Background There is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area. Methods This review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool. Results A total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing. Conclusions While the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.
Collapse
Affiliation(s)
- Chiaw-Ling Chng
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Kaiping Zheng
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Ann Kerwen Kwee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | | | - Daniel Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Chen Pong Wong
- Department of Neuroradiology, Singapore General Hospital, Singapore, Singapore
| | - Guoyu Hu
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Beng Chin Ooi
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Si Wei Kheok
- Department of Neuroradiology, Singapore General Hospital, Singapore, Singapore
| |
Collapse
|
6
|
Diao J, Chen X, Shen Y, Li J, Chen Y, He L, Chen S, Mou P, Ma X, Wei R. Research progress and application of artificial intelligence in thyroid associated ophthalmopathy. Front Cell Dev Biol 2023; 11:1124775. [PMID: 36760363 PMCID: PMC9903073 DOI: 10.3389/fcell.2023.1124775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 01/12/2023] [Indexed: 01/25/2023] Open
Abstract
Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|