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Li F, Wang D, Yang Z, Zhang Y, Jiang J, Liu X, Kong K, Zhou F, Tham CC, Medeiros F, Han Y, Grzybowski A, Zangwill LM, Lam DSC, Zhang X. The AI revolution in glaucoma: Bridging challenges with opportunities. Prog Retin Eye Res 2024; 103:101291. [PMID: 39186968 DOI: 10.1016/j.preteyeres.2024.101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Deming Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Zefeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Yinhang Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Jiaxuan Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Xiaoyi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Kangjie Kong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Fengqi Zhou
- Ophthalmology, Mayo Clinic Health System, Eau Claire, WI, USA.
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Felipe Medeiros
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Ying Han
- University of California, San Francisco, Department of Ophthalmology, San Francisco, CA, USA; The Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, CA, USA.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, CA, USA.
| | - Dennis S C Lam
- The International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
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Yonehara M, Nakagawa Y, Ayatsuka Y, Hara Y, Shoji J, Ebihara N, Inomata T, Huang T, Nagino K, Fukuda K, Kishimoto T, Sumi T, Fukushima A, Fujishima H, Kawai M, Takamura E, Uchio E, Namba K, Koyama A, Haruki T, Sasaki SI, Shimizu Y, Miyazaki D. Use of explainable AI on slit-lamp images of anterior surface of eyes to diagnose allergic conjunctival diseases. Allergol Int 2024:S1323-8930(24)00077-7. [PMID: 39155213 DOI: 10.1016/j.alit.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 06/19/2024] [Accepted: 07/03/2024] [Indexed: 08/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) is a promising new technology that has the potential of diagnosing allergic conjunctival diseases (ACDs). However, its development is slowed by the absence of a tailored image database and explainable AI models. Thus, the purpose of this study was to develop an explainable AI model that can not only diagnose ACDs but also present the basis for the diagnosis. METHODS A dataset of 4942 slit-lamp images from 10 ophthalmological institutions across Japan were used as the image database. A sequential pipeline of segmentation AI was constructed to identify 12 clinical findings in 1038 images of seasonal and perennial allergic conjunctivitis (AC), atopic keratoconjunctivitis (AKC), vernal keratoconjunctivitis (VKC), giant papillary conjunctivitis (GPC), and normal subjects. The performance of the pipeline was evaluated by determining its ability to obtain explainable results through the extraction of the findings. Its diagnostic accuracy was determined for 4 severity-based diagnosis classification of AC, AKC/VKC, GPC, and normal. RESULTS Segmentation AI pipeline efficiently extracted crucial ACD indicators including conjunctival hyperemia, giant papillae, and shield ulcer, and offered interpretable insights. The AI pipeline diagnosis had a high diagnostic accuracy of 86.2%, and that of the board-certified ophthalmologists was 60.0%. The pipeline had a high classification performance, and the area under the curve (AUC) was 0.959 for AC, 0.905 for normal subjects, 0.847 for GPC, 0.829 for VKC, and 0.790 for AKC. CONCLUSIONS An explainable AI model created by a comprehensive image database can be used for diagnosing ACDs with high degree of accuracy.
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Affiliation(s)
- Michiko Yonehara
- Division of Ophthalmology and Visual Science, Faculty of Medicine, Tottori University, Tottori, Japan
| | | | | | - Yuko Hara
- Department of Ophthalmology, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Jun Shoji
- Division of Ophthalmology, Department of Visual Sciences, Nihon University School of Medicine, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Juntendo University Urayasu Hospital, Chiba, Japan
| | - Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan; AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ken Nagino
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ken Fukuda
- Department of Ophthalmology, Kochi Medical School, Kochi University, Kochi, Japan
| | - Tatsuma Kishimoto
- Department of Ophthalmology, Kochi Medical School, Kochi University, Kochi, Japan
| | - Tamaki Sumi
- Department of Ophthalmology, Kochi Medical School, Kochi University, Kochi, Japan
| | | | - Hiroshi Fujishima
- Department of Ophthalmology, School of Dental Medicine, Tsurumi University, Kanagawa, Japan
| | - Moeko Kawai
- Department of Ophthalmology, School of Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Etsuko Takamura
- Department of Ophthalmology, School of Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Eiichi Uchio
- Department of Ophthalmology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Kenichi Namba
- Department of Ophthalmology, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Ayumi Koyama
- Division of Ophthalmology and Visual Science, Faculty of Medicine, Tottori University, Tottori, Japan
| | - Tomoko Haruki
- Division of Ophthalmology and Visual Science, Faculty of Medicine, Tottori University, Tottori, Japan
| | - Shin-Ich Sasaki
- Division of Ophthalmology and Visual Science, Faculty of Medicine, Tottori University, Tottori, Japan
| | - Yumiko Shimizu
- Division of Ophthalmology and Visual Science, Faculty of Medicine, Tottori University, Tottori, Japan
| | - Dai Miyazaki
- Division of Ophthalmology and Visual Science, Faculty of Medicine, Tottori University, Tottori, Japan.
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Wang MT, Cai YR, Jang V, Meng HJ, Sun LB, Deng LM, Liu YW, Zou WJ. Establishment of a corneal ulcer prognostic model based on machine learning. Sci Rep 2024; 14:16154. [PMID: 38997339 PMCID: PMC11245505 DOI: 10.1038/s41598-024-66608-7] [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: 06/24/2023] [Accepted: 07/02/2024] [Indexed: 07/14/2024] Open
Abstract
Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model's performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63-1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63-0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92-1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94-1.00) for visual impairment.
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Affiliation(s)
- Meng-Tong Wang
- Department of Ophthalmology, The First Affiliated Hospital of Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - You-Ran Cai
- Department of Ophthalmology, The First Affiliated Hospital of Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Vlon Jang
- Qi Dian Fu Liu Technology Co.Ltd, Beijing, China
| | - Hong-Jian Meng
- Department of Ophthalmology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Ling-Bo Sun
- Department of Ophthalmology, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China
| | - Li-Min Deng
- Department of Ophthalmology, Guangxi Zhuang Autonomous Region People's Hospital, Nanning, China
| | - Yu-Wen Liu
- School of Medicine, Eye Institute of Xiamen University, Xiamen University, Xiamen, Fujian, China
| | - Wen-Jin Zou
- Department of Ophthalmology, The First Affiliated Hospital of Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, China.
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Miller KD, Toiv A, Deng C, Lu MC, Niziol LM, Hart JN, Sherman E, Mian SI, Lephart PR, Sugar A, Kang L, Woodward MA. Factors Associated With Laboratory Test Negativity Following a Transition in Specimen Collection in Microbial Keratitis Cases. Curr Eye Res 2024; 49:339-344. [PMID: 38179803 PMCID: PMC10959676 DOI: 10.1080/02713683.2023.2294700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024]
Abstract
PURPOSE Negative laboratory results make targeting microbial keratitis treatment difficult. We investigated factors associated with laboratory negativity in patients with microbial keratitis in the context of a transition to a new specimen collection method. METHODS Microbial keratitis patients with associated laboratory tests were identified in the electronic health record of a tertiary care facility from August 2012 to April 2022. Patient demographics and laboratory results were obtained. Random sampling of 50% of charts was performed to assess the impact of the ocular history and pretreatment measures. The relationship between probability of negative laboratory results with demographics, ocular history, pretreatment measures, and utilization of a new specimen collection method (i.e. ESwab) was evaluated by multivariable logistic regression. RESULTS Of 3395 microbial keratitis patients identified, 31% (n = 1051) had laboratory tests. Laboratory testing increased over time (slope = 2.5% per year, p < 0.001; 19.6% in 2013 to 42.2% in 2021). Laboratory negative rate increased over time (slope = 2.2% per year, p = 0.022; 48.5% in 2013 to 62.3% in 2021). Almost one-third of patients (31.2%, n = 164) were pretreated with steroids. Over two-thirds of patients were pretreated with antibiotics (69.5%, n = 367). 56.5% (n = 297) of patients were outside referrals. In multivariable regression, patients with corticosteroid pretreatment had lower odds of negative laboratory results (odds ratio [OR] = 0.49, p = 0.001). There were higher odds of negative laboratory results for every additional antibiotic prescribed to a patient prior to presentation (OR = 1.30, p = 0.006) and for specimens collected using ESwabs (OR = 1.69, p = 0.005). Age, prior eye trauma, outside referrals, and contact lens wear were not significantly associated with negative laboratory results. CONCLUSION More microbial keratitis associated laboratory tests are being taken over time. Over 60% of tests were negative by 2022. Factors associated with negative laboratory test results included pretreatment with antibiotics and specimens collected with the new collection method.
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Affiliation(s)
- Keith D Miller
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Avi Toiv
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Callie Deng
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Ming-Chen Lu
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Leslie M Niziol
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jenna N Hart
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Eric Sherman
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Shahzad I Mian
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Paul R Lephart
- Department of Pathology, University of Michigan Medicine, Ann Arbor, MI, USA
| | - Alan Sugar
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Linda Kang
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maria A Woodward
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
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5
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Soleimani M, Cheraqpour K, Sadeghi R, Pezeshgi S, Koganti R, Djalilian AR. Artificial Intelligence and Infectious Keratitis: Where Are We Now? Life (Basel) 2023; 13:2117. [PMID: 38004257 PMCID: PMC10672455 DOI: 10.3390/life13112117] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/27/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
Infectious keratitis (IK), which is one of the most common and catastrophic ophthalmic emergencies, accounts for the leading cause of corneal blindness worldwide. Different pathogens, including bacteria, viruses, fungi, and parasites, can cause IK. The diagnosis and etiology detection of IK pose specific challenges, and delayed or incorrect diagnosis can significantly worsen the outcome. Currently, this process is mainly performed based on slit-lamp findings, corneal smear and culture, tissue biopsy, PCR, and confocal microscopy. However, these diagnostic methods have their drawbacks, including experience dependency, tissue damage, cost, and time consumption. Diagnosis and etiology detection of IK can be especially challenging in rural areas or in countries with limited resources. In recent years, artificial intelligence (AI) has opened new windows in medical fields such as ophthalmology. An increasing number of studies have utilized AI in the diagnosis of anterior segment diseases such as IK. Several studies have demonstrated that AI algorithms can diagnose and detect the etiology of IK accurately and fast, which can be valuable, especially in remote areas and in countries with limited resources. Herein, we provided a comprehensive update on the utility of AI in IK.
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Affiliation(s)
- Mohammad Soleimani
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran; (M.S.); (K.C.); (R.S.)
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA;
| | - Kasra Cheraqpour
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran; (M.S.); (K.C.); (R.S.)
| | - Reza Sadeghi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran; (M.S.); (K.C.); (R.S.)
| | - Saharnaz Pezeshgi
- School of Medicine, Tehran University of Medical Sciences, Tehran 1461884513, Iran;
| | - Raghuram Koganti
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA;
| | - Ali R. Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA;
- Cornea Service, Stem Cell Therapy and Corneal Tissue Engineering Laboratory, Illinois Eye and Ear Infirmary, Chicago, IL 60612, USA
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Shao Y, Jie Y, Liu ZG. Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023). Int J Ophthalmol 2023; 16:1373-1385. [PMID: 37724278 PMCID: PMC10475626 DOI: 10.18240/ijo.2023.09.03] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/14/2023] [Indexed: 09/20/2023] Open
Abstract
The landscape of ophthalmology has observed monumental shifts with the advent of artificial intelligence (AI) technologies. This article is devoted to elaborating on the nuanced application of AI in the diagnostic realm of anterior segment eye diseases, an area ripe with potential yet complex in its imaging characteristics. Historically, AI's entrenchment in ophthalmology was predominantly rooted in the posterior segment. However, the evolution of machine learning paradigms, particularly with the advent of deep learning methodologies, has reframed the focus. When combined with the exponential surge in available electronic image data pertaining to the anterior segment, AI's role in diagnosing corneal, conjunctival, lens, and eyelid pathologies has been solidified and has emerged from the realm of theoretical to practical. In light of this transformative potential, collaborations between the Ophthalmic Imaging and Intelligent Medicine Subcommittee of the China Medical Education Association and the Ophthalmology Committee of the International Translational Medicine Association have been instrumental. These eminent bodies mobilized a consortium of experts to dissect and assimilate advancements from both national and international quarters. Their mandate was not limited to AI's application in anterior segment pathologies like the cornea, conjunctiva, lens, and eyelids, but also ventured into deciphering the existing impediments and envisioning future trajectories. After iterative deliberations, the consensus synthesized herein serves as a touchstone, assisting ophthalmologists in optimally integrating AI into their diagnostic decisions and bolstering clinical research. Through this guideline, we aspire to offer a comprehensive framework, ensuring that clinical decisions are not merely informed but transformed by AI. By building upon existing literature yet maintaining the highest standards of originality, this document stands as a testament to both innovation and academic integrity, in line with the ethos of renowned journals such as Ophthalmology.
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Affiliation(s)
- Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Ying Jie
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University; Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing 100730, China
| | - Zu-Guo Liu
- Eye Institute of Xiamen University, Xiamen 361102, Fujian Province, China
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7
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Xu Z, Xu J, Shi C, Xu W, Jin X, Han W, Jin K, Grzybowski A, Yao K. Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications. Ophthalmol Ther 2023; 12:1439-1455. [PMID: 36884203 PMCID: PMC10164195 DOI: 10.1007/s40123-023-00690-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.
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Affiliation(s)
- Zhe Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Jia Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Ce Shi
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Wen Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Xiuming Jin
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Wei Han
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Kai Jin
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Ke Yao
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
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8
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Zhang Z, Wang Y, Zhang H, Samusak A, Rao H, Xiao C, Abula M, Cao Q, Dai Q. Artificial intelligence-assisted diagnosis of ocular surface diseases. Front Cell Dev Biol 2023; 11:1133680. [PMID: 36875760 PMCID: PMC9981656 DOI: 10.3389/fcell.2023.1133680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.
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Affiliation(s)
- Zuhui Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ying Wang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Hongzhen Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Arzigul Samusak
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Huimin Rao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Chun Xiao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Muhetaer Abula
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Qixin Cao
- Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China
| | - Qi Dai
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review. Diagnostics (Basel) 2022; 13:diagnostics13010130. [PMID: 36611422 PMCID: PMC9818762 DOI: 10.3390/diagnostics13010130] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/16/2022] [Accepted: 12/22/2022] [Indexed: 01/04/2023] Open
Abstract
In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.
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Qasmieh IA, Alquran H, Zyout A, Al-Issa Y, Mustafa WA, Alsalatie M. Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning. Diagnostics (Basel) 2022; 12:diagnostics12123204. [PMID: 36553211 PMCID: PMC9777193 DOI: 10.3390/diagnostics12123204] [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: 10/15/2022] [Revised: 11/26/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.
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Affiliation(s)
- Isam Abu Qasmieh
- Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Hiam Alquran
- Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Ala’a Zyout
- Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Yazan Al-Issa
- Department of Computer Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Campus Pauh Putra, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600, Malaysia
- Advanced Computing (AdvComp), Centre of Excellence (CoE), Campus Pauh Putra, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600, Malaysia
- Correspondence:
| | - Mohammed Alsalatie
- The Institute of Biomedical Technology, King Hussein Medical Center, Royal Jordanian Medical Service, Amman 11855, Jordan
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Abstract
PURPOSE OF REVIEW Artificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy. RECENT FINDINGS Most current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics. SUMMARY Artificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.
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Affiliation(s)
- Linda Kang
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI
| | - Dena Ballouz
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI
| | - Maria A. Woodward
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI
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Mayya V, Kamath Shevgoor S, Kulkarni U, Hazarika M, Barua PD, Acharya UR. Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis. J Fungi (Basel) 2021; 7:jof7100850. [PMID: 34682271 PMCID: PMC8540278 DOI: 10.3390/jof7100850] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 11/16/2022] Open
Abstract
Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.
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Affiliation(s)
- Veena Mayya
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India;
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, India
- Correspondence: (V.M.); (M.H.)
| | - Sowmya Kamath Shevgoor
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India;
| | - Uma Kulkarni
- Department of Ophthalmology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Mangalore 575018, India;
| | - Manali Hazarika
- Cornea and Anterior Segment Services, Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Manipal 576104, India
- Correspondence: (V.M.); (M.H.)
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore S599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Loo J, Woodward MA, Prajna V, Kriegel MF, Pawar M, Khan M, Niziol LM, Farsiu S. Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis. Transl Vis Sci Technol 2021; 10:2. [PMID: 34605877 PMCID: PMC8496413 DOI: 10.1167/tvst.10.12.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Purpose To assess clinical applicability of automatic image analysis in microbial keratitis (MK) by evaluating the relationship between biomarker measurements on slit-lamp photography (SLP) and best-corrected visual acuity (BCVA). Methods Seventy-six patients with MK with SLP images and same-day logarithm of the minimum angle of resolution (logMAR) BCVA were evaluated. MK biomarkers (stromal infiltrate, white blood cell infiltration, corneal edema, hypopyon, epithelial defect) were segmented manually by ophthalmologists and automatically by a novel, open-source, deep learning–based segmentation algorithm. Five measurements (presence, maximum width, total area, proportion of the corneal limbus area affected, centrality) were calculated. Correlations between the measurements and BCVA were calculated. An automatic regression model estimated BCVA from the measurements. Differences in performance between using manual and automatic measurements were evaluated using William's test (for correlation) and the paired-sample t-test (for absolute error). Results Measurements had high correlations of 0.86 (manual) and 0.84 (automatic) with true BCVA. Estimated BCVA had average (mean ± SD) absolute errors of 0.39 ± 0.27 logMAR (manual, median: 0.30) and 0.35 ± 0.28 logMAR (automatic, median: 0.30) and high correlations of 0.76 (manual) and 0.80 (automatic) with true BCVA. Differences between using manual and automatic measurements were not statistically significant for correlations of measurements with true BCVA (P = .66), absolute errors of estimated BCVA (P = .15), or correlations of estimated BCVA with true BCVA (P = .60). Conclusions The proposed algorithm measured MK biomarkers as accurately as ophthalmologists. Measurements were highly correlated with and estimative of visual acuity. Translational Relevance This study demonstrates the potential of developing fully automatic objective and standardized strategies to aid ophthalmologists in the clinical assessment of MK.
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Affiliation(s)
- Jessica Loo
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Maria A Woodward
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA.,Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
| | - Venkatesh Prajna
- Department of Cornea and Refractive Services, Aravind Eye Care System, Madurai, Tamil Nadu, India
| | - Matthias F Kriegel
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA.,Department of Ophthalmology, Augenzentrum am St. Franziskus Hospital Münster, Münster, Germany
| | - Mercy Pawar
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA
| | - Mariam Khan
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA
| | - Leslie M Niziol
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA
| | - Sina Farsiu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.,Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
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Preventing corneal blindness caused by keratitis using artificial intelligence. Nat Commun 2021; 12:3738. [PMID: 34145294 PMCID: PMC8213803 DOI: 10.1038/s41467-021-24116-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 06/01/2021] [Indexed: 12/14/2022] Open
Abstract
Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.
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