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Arboleda A, Ta CN. Overview of Mycotic Keratitis. Cornea 2024; 43:1065-1071. [PMID: 39102310 PMCID: PMC11300963 DOI: 10.1097/ico.0000000000003559] [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: 01/13/2024] [Accepted: 03/19/2024] [Indexed: 08/07/2024]
Abstract
ABSTRACT Keratomycosis is a serious corneal infection associated with high ocular morbidity that can lead to severe vision loss. It is estimated to affect more than 1 million patients annually, most commonly occurring in tropical climates, and represents a growing threat to patients worldwide. Despite aggressive medical management, fungal infections have a higher rate of perforation requiring surgical intervention compared with other infectious etiologies. Early diagnosis and appropriate treatment are keys to preserving vision and saving patients' eyes.Timely diagnosis of fungal keratitis helps minimize corneal damage and scarring and increases the likelihood of a favorable outcome. Studies have shown that correct identification of fungal infections is often delayed up to 2 to 3 weeks after initial presentation. This leads to incorrect or ineffective treatment for many patients. Diagnostic techniques explored in this study include corneal scrapings with staining and culture, visualization with in vivo confocal microscopy, molecular diagnostic techniques including polymerase chain reaction, and recently developed omics-based technologies.Treatment of fungal keratitis begins with topical antifungals. Medical management has been proven to be effective, but with limitations including poor drug penetration and low bioavailability. Cases that do not respond to topical therapy require more invasive and novel treatments to control the infection. We review the clinical trials that have shaped current practice patterns, with focus on the efficacy of topical natamycin as the primary therapy for filamentous fungal keratitis. We explore additional management strategies such as localized intrastromal and intracameral injections of antifungal medications, photodynamic therapy, and surgical intervention.
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Affiliation(s)
- Alejandro Arboleda
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA
- Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, San Francisco, CA
| | - Christopher N. Ta
- Byers Eye Institute, Department of Ophthalmology, Stanford University, Palo Alto, CA
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Li CP, Dai W, Xiao YP, Qi M, Zhang LX, Gao L, Zhang FL, Lai YK, Liu C, Lu J, Chen F, Chen D, Shi S, Li S, Zeng Q, Chen Y. Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images. Sci Rep 2024; 14:18432. [PMID: 39117709 PMCID: PMC11310506 DOI: 10.1038/s41598-024-68768-y] [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: 03/23/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis.
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Affiliation(s)
- Chun-Peng Li
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Weiwei Dai
- Changsha Aier Eye Hospital, Hunan, China
| | - Yun-Peng Xiao
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Mengying Qi
- Wuhan Aier Hankou Eye Hospital, Wuhan, China
| | - Ling-Xiao Zhang
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lin Gao
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fang-Lue Zhang
- Victoria University of Wellington, Wellington, New Zealand
| | | | - Chang Liu
- Beijing Aier Intech Eye Hospital, Beijing, China
| | - Jing Lu
- Chengdu Aier East Eye Hospital, Chengdu, China
| | - Fen Chen
- Wuhan Aier Hankou Eye Hospital, Wuhan, China
| | - Dan Chen
- Wuhan Aier Hankou Eye Hospital, Wuhan, China
| | - Shuai Shi
- Beijing Aier Intech Eye Hospital, Beijing, China
| | - Shaowei Li
- Beijing Aier Intech Eye Hospital, Beijing, China
| | - Qingyan Zeng
- Wuhan Aier Hankou Eye Hospital, Wuhan, China.
- Aier Eye Hospital of Wuhan University, Wuhan, China.
- Hubei University of Science and Technology, Xianning, China.
- Aier Eye Hospital, Jinan University, Guangzhou, China.
| | - Yiqiang Chen
- Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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Gurnani B, Kaur K, Lalgudi VG, Kundu G, Mimouni M, Liu H, Jhanji V, Prakash G, Roy AS, Shetty R, Gurav JS. Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review. J Fr Ophtalmol 2024; 47:104242. [PMID: 39013268 DOI: 10.1016/j.jfo.2024.104242] [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: 12/18/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024]
Abstract
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
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Affiliation(s)
- B Gurnani
- Department of Cataract, Cornea, External Disease, Trauma, Ocular Surface and Refractive Surgery, ASG Eye Hospital, Jodhpur, Rajasthan, India.
| | - K Kaur
- Department of Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, Rajasthan, India
| | - V G Lalgudi
- Department of Cornea, Refractive surgery, Ira G Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, USA
| | - G Kundu
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - M Mimouni
- Department of Ophthalmology, Rambam Health Care Campus affiliated with the Bruce and Ruth Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - H Liu
- Department of Ophthalmology, University of Ottawa Eye Institute, Ottawa, Canada
| | - V Jhanji
- UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - G Prakash
- Department of Ophthalmology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - A S Roy
- Narayana Nethralaya Foundation, Bangalore, India
| | - R Shetty
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - J S Gurav
- Department of Opthalmology, Armed Forces Medical College, Pune, India
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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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Kryszan K, Wylęgała A, Kijonka M, Potrawa P, Walasz M, Wylęgała E, Orzechowska-Wylęgała B. Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review. Diagnostics (Basel) 2024; 14:694. [PMID: 38611606 PMCID: PMC11011861 DOI: 10.3390/diagnostics14070694] [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: 02/08/2024] [Revised: 03/13/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the "black-box" nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.
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Affiliation(s)
- Katarzyna Kryszan
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Adam Wylęgała
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Health Promotion and Obesity Management, Pathophysiology Department, Medical University of Silesia in Katowice, 40-752 Katowice, Poland
| | - Magdalena Kijonka
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Patrycja Potrawa
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Mateusz Walasz
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Edward Wylęgała
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Bogusława Orzechowska-Wylęgała
- Department of Pediatric Otolaryngology, Head and Neck Surgery, Chair of Pediatric Surgery, Medical University of Silesia, 40-760 Katowice, Poland;
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Tey KY, Cheong EZK, Ang M. Potential applications of artificial intelligence in image analysis in cornea diseases: a review. EYE AND VISION (LONDON, ENGLAND) 2024; 11:10. [PMID: 38448961 PMCID: PMC10919022 DOI: 10.1186/s40662-024-00376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/09/2024] [Indexed: 03/08/2024]
Abstract
Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology-primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.
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Affiliation(s)
- Kai Yuan Tey
- Singapore National Eye Centre, 11 Third Hospital Ave, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | | | - Marcus Ang
- Singapore National Eye Centre, 11 Third Hospital Ave, Singapore, 168751, Singapore.
- Singapore Eye Research Institute, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
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Cabrera-Aguas M, Watson SL. Updates in Diagnostic Imaging for Infectious Keratitis: A Review. Diagnostics (Basel) 2023; 13:3358. [PMID: 37958254 PMCID: PMC10647798 DOI: 10.3390/diagnostics13213358] [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: 08/16/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
Infectious keratitis (IK) is among the top five leading causes of blindness globally. Early diagnosis is needed to guide appropriate therapy to avoid complications such as vision impairment and blindness. Slit lamp microscopy and culture of corneal scrapes are key to diagnosing IK. Slit lamp photography was transformed when digital cameras and smartphones were invented. The digital camera or smartphone camera sensor's resolution, the resolution of the slit lamp and the focal length of the smartphone camera system are key to a high-quality slit lamp image. Alternative diagnostic tools include imaging, such as optical coherence tomography (OCT) and in vivo confocal microscopy (IVCM). OCT's advantage is its ability to accurately determine the depth and extent of the corneal ulceration, infiltrates and haze, therefore characterizing the severity and progression of the infection. However, OCT is not a preferred choice in the diagnostic tool package for infectious keratitis. Rather, IVCM is a great aid in the diagnosis of fungal and Acanthamoeba keratitis with overall sensitivities of 66-74% and 80-100% and specificity of 78-100% and 84-100%, respectively. Recently, deep learning (DL) models have been shown to be promising aids for the diagnosis of IK via image recognition. Most of the studies that have developed DL models to diagnose the different types of IK have utilised slit lamp photographs. Some studies have used extremely efficient single convolutional neural network algorithms to train their models, and others used ensemble approaches with variable results. Limitations of DL models include the need for large image datasets to train the models, the difficulty in finding special features of the different types of IK, the imbalance of training models, the lack of image protocols and misclassification bias, which need to be overcome to apply these models into real-world settings. Newer artificial intelligence technology that generates synthetic data, such as generative adversarial networks, may assist in overcoming some of these limitations of CNN models.
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Affiliation(s)
- Maria Cabrera-Aguas
- Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2000, Australia;
- Sydney Eye Hospital, Sydney, NSW 2000, Australia
| | - Stephanie L Watson
- Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2000, Australia;
- Sydney Eye Hospital, Sydney, NSW 2000, Australia
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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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Gibson D, Tran T, Raveendran V, Bonnet C, Siu N, Vinet M, Stoddard-Bennett T, Arnold C, Deng SX, Speier W. Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency. Front Med (Lausanne) 2023; 10:1270570. [PMID: 37908848 PMCID: PMC10613638 DOI: 10.3389/fmed.2023.1270570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction Limbal Stem Cell Deficiency (LSCD) is a blinding corneal disease characterized by the loss of function or deficiency in adult stem cells located at the junction between the cornea and the sclera (i.e., the limbus), namely the limbal stem cells (LSCs). Recent advances in in vivo imaging technology have improved disease diagnosis and staging to quantify several biomarkers of in vivo LSC function including epithelial thickness measured by anterior segment optical coherence tomography, and basal epithelial cell density and subbasal nerve plexus by in vivo confocal microscopy. A decrease in central corneal sub-basal nerve density and nerve fiber and branching number has been shown to correlate with the severity of the disease in parallel with increased nerve tortuosity. Yet, image acquisition and manual quantification require a high level of expertise and are time-consuming. Manual quantification presents inevitable interobserver variability. Methods The current study employs a novel deep learning approach to classify neuron morphology in various LSCD stages and healthy controls, by integrating images created through latent diffusion augmentation. The proposed model, a residual U-Net, is based in part on the InceptionResNetV2 transfer learning model. Results Deep learning was able to determine fiber number, branching, and fiber length with high accuracy (R2 of 0.63, 0.63, and 0.80, respectively). The model trained on images generated through latent diffusion on average outperformed the same model when trained on solely original images. The model was also able to detect LSCD with an AUC of 0.867, which showed slightly higher performance compared to classification using manually assessed metrics. Discussion The results suggest that utilizing latent diffusion to supplement training data may be effective in bolstering model performance. The results of the model emphasize the ability as well as the shortcomings of this novel deep learning approach to predict various nerve morphology metrics as well as LSCD disease severity.
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Affiliation(s)
- David Gibson
- Medical Informatics Home Area, Graduate Programs in Bioscience, University of California, Los Angeles, Los Angeles, CA, United States
| | - Thai Tran
- Medical Informatics Home Area, Graduate Programs in Bioscience, University of California, Los Angeles, Los Angeles, CA, United States
| | - Vidhur Raveendran
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
| | - Clémence Bonnet
- Ophthalmology Department, Cochin Hospital and Paris Cité University, AP-HP, Paris, France
- Stein Eye Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nathan Siu
- Medical Informatics Home Area, Graduate Programs in Bioscience, University of California, Los Angeles, Los Angeles, CA, United States
- Stein Eye Institute, University of California, Los Angeles, Los Angeles, CA, United States
- Computational Diagnostics Lab, University of California, Los Angeles, Los Angeles, CA, United States
| | - Micah Vinet
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
- Computational Diagnostics Lab, University of California, Los Angeles, Los Angeles, CA, United States
| | - Theo Stoddard-Bennett
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Corey Arnold
- Medical Informatics Home Area, Graduate Programs in Bioscience, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
- Computational Diagnostics Lab, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sophie X. Deng
- Stein Eye Institute, University of California, Los Angeles, Los Angeles, CA, United States
- Computational Diagnostics Lab, University of California, Los Angeles, Los Angeles, CA, United States
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - William Speier
- Medical Informatics Home Area, Graduate Programs in Bioscience, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
- Computational Diagnostics Lab, University of California, Los Angeles, Los Angeles, CA, United States
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10
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Azzopardi M, Chong YJ, Ng B, Recchioni A, Logeswaran A, Ting DSJ. Diagnosis of Acanthamoeba Keratitis: Past, Present and Future. Diagnostics (Basel) 2023; 13:2655. [PMID: 37627913 PMCID: PMC10453105 DOI: 10.3390/diagnostics13162655] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/04/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Acanthamoeba keratitis (AK) is a painful and sight-threatening parasitic corneal infection. In recent years, the incidence of AK has increased. Timely and accurate diagnosis is crucial during the management of AK, as delayed diagnosis often results in poor clinical outcomes. Currently, AK diagnosis is primarily achieved through a combination of clinical suspicion, microbiological investigations and corneal imaging. Historically, corneal scraping for microbiological culture has been considered to be the gold standard. Despite its technical ease, accessibility and cost-effectiveness, the long diagnostic turnaround time and variably low sensitivity of microbiological culture limit its use as a sole diagnostic test for AK in clinical practice. In this review, we aim to provide a comprehensive overview of the diagnostic modalities that are currently used to diagnose AK, including microscopy with staining, culture, corneal biopsy, in vivo confocal microscopy, polymerase chain reaction and anterior segment optical coherence tomography. We also highlight emerging techniques, such as next-generation sequencing and artificial intelligence-assisted models, which have the potential to transform the diagnostic landscape of AK.
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Affiliation(s)
- Matthew Azzopardi
- Department of Ophthalmology, Royal London Hospital, London E1 1BB, UK;
| | - Yu Jeat Chong
- Birmingham and Midland Eye Centre, Birmingham B18 7QH, UK; (B.N.); (A.R.)
| | - Benjamin Ng
- Birmingham and Midland Eye Centre, Birmingham B18 7QH, UK; (B.N.); (A.R.)
| | - Alberto Recchioni
- Birmingham and Midland Eye Centre, Birmingham B18 7QH, UK; (B.N.); (A.R.)
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham B15 2TT, UK
| | | | - Darren S. J. Ting
- Birmingham and Midland Eye Centre, Birmingham B18 7QH, UK; (B.N.); (A.R.)
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham B15 2TT, UK
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
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11
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Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
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12
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Fang W, Wu J, Cheng M, Zhu X, Du M, Chen C, Liao W, Zhi K, Pan W. Diagnosis of invasive fungal infections: challenges and recent developments. J Biomed Sci 2023; 30:42. [PMID: 37337179 DOI: 10.1186/s12929-023-00926-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/13/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow. MAIN TEXT A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections. CONCLUSION The review will undoubtedly assist in updating the scientific community's understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.
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Affiliation(s)
- Wenjie Fang
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Junqi Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
- Shanghai Engineering Research Center of Lung Transplantation, Shanghai, 200433, China
| | - Mingrong Cheng
- Department of Anorectal Surgery, The Third Affiliated Hospital of Guizhou Medical University, Guizhou, 558000, China
| | - Xinlin Zhu
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Mingwei Du
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
- Shanghai Engineering Research Center of Lung Transplantation, Shanghai, 200433, China
| | - Wanqing Liao
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Kangkang Zhi
- Department of Vascular and Endovascular Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.
| | - Weihua Pan
- Department of Dermatology, Shanghai Key Laboratory of Molecular Medical Mycology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.
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13
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Essalat M, Abolhosseini M, Le TH, Moshtaghion SM, Kanavi MR. Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images. Sci Rep 2023; 13:8953. [PMID: 37268665 DOI: 10.1038/s41598-023-35085-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 05/12/2023] [Indexed: 06/04/2023] Open
Abstract
Infectious keratitis refers to a group of corneal disorders in which corneal tissues suffer inflammation and damage caused by pathogenic infections. Among these disorders, fungal keratitis (FK) and acanthamoeba keratitis (AK) are particularly severe and can cause permanent blindness if not diagnosed early and accurately. In Vivo Confocal Microscopy (IVCM) allows for imaging of different corneal layers and provides an important tool for an early and accurate diagnosis. In this paper, we introduce the IVCM-Keratitis dataset, which comprises of a total of 4001 sample images of AK and FK, as well as non-specific keratitis (NSK) and healthy corneas classes. We use this dataset to develop multiple deep-learning models based on Convolutional Neural Networks (CNNs) to provide automated assistance in enhancing the diagnostic accuracy of confocal microscopy in infectious keratitis. Densenet161 had the best performance among these models, with an accuracy, precision, recall, and F1 score of 93.55%, 92.52%, 94.77%, and 96.93%, respectively. Our study highlights the potential of deep learning models to provide automated diagnostic assistance for infectious keratitis via confocal microscopy images, particularly in the early detection of AK and FK. The proposed model can provide valuable support to both experienced and inexperienced eye-care practitioners in confocal microscopy image analysis, by suggesting the most likely diagnosis. We further demonstrate that these models can highlight the areas of infection in the IVCM images and explain the reasons behind their diagnosis by utilizing saliency maps, a technique used in eXplainable Artificial Intelligence (XAI) to interpret these models.
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Affiliation(s)
- Mahmoud Essalat
- Department of Electrical and Computer Engineering, University of California, Los Angeles, 56-125B Engineering IV Building, UCLA, 420 Westwood Plaza, Los Angeles, CA, 90095-1594, USA.
| | - Mohammad Abolhosseini
- Ocular Tissue Engineering Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, No.23, Paidarfard St., Boostan 9 St., Pasdaran Ave., Tehran, 1666673111, Iran
- Department of Confocal Scan, Central Eye Bank of Iran, Tehran, Iran
| | - Thanh Huy Le
- Department of Computer Science, University of California, San Diego, CA, USA
| | - Seyed Mohamadmehdi Moshtaghion
- Ocular Tissue Engineering Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, No.23, Paidarfard St., Boostan 9 St., Pasdaran Ave., Tehran, 1666673111, Iran
- Department of Confocal Scan, Central Eye Bank of Iran, Tehran, Iran
| | - Mozhgan Rezaei Kanavi
- Ocular Tissue Engineering Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, No.23, Paidarfard St., Boostan 9 St., Pasdaran Ave., Tehran, 1666673111, Iran.
- Department of Confocal Scan, Central Eye Bank of Iran, Tehran, Iran.
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14
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Kv R, Prasad K, Peralam Yegneswaran P. Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review. J Med Syst 2023; 47:40. [PMID: 36971852 PMCID: PMC10042761 DOI: 10.1007/s10916-023-01927-2] [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: 12/05/2022] [Accepted: 02/25/2023] [Indexed: 03/29/2023]
Abstract
Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.
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Affiliation(s)
- Rajitha Kv
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
| | - Prakash Peralam Yegneswaran
- Department of Microbiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
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15
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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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16
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023; 6:1017420. [PMID: 36818823 PMCID: PMC9929069 DOI: 10.3389/fdata.2023.1017420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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Affiliation(s)
- Darren S. J. Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom,Birmingham and Midland Eye Centre, Birmingham, United Kingdom,Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,*Correspondence: Darren S. J. Ting ✉
| | - Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Daniel S. W. Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
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Tang N, Huang G, Lei D, Jiang L, Chen Q, He W, Tang F, Hong Y, Lv J, Qin Y, Lin Y, Lan Q, Qin Y, Lan R, Pan X, Li M, Xu F, Lu P. An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images. Int Ophthalmol 2023:10.1007/s10792-022-02616-8. [PMID: 36595127 DOI: 10.1007/s10792-022-02616-8] [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: 09/13/2022] [Accepted: 12/09/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE Fungal keratitis is a common cause of blindness worldwide. Timely identification of the causative fungal genera is essential for clinical management. In vivo confocal microscopy (IVCM) provides useful information on pathogenic genera. This study attempted to apply deep learning (DL) to establish an automated method to identify pathogenic fungal genera using IVCM images. METHODS Deep learning networks were trained, validated, and tested using a data set of 3364 IVCM images that collected from 100 eyes of 100 patients with culture-proven filamentous fungal keratitis. Two transfer learning approaches were investigated: one was a combined framework that extracted features by a DL network and adopted decision tree (DT) as a classifier; another was a complete supervised DL model which used DL-based fully connected layers to implement the classification. RESULTS The DL classifier model revealed better performance compared with the DT classifier model in an independent testing set. The DL classifier model showed an area under the receiver operating characteristic curves (AUC) of 0.887 with an accuracy of 0.817, sensitivity of 0.791, specificity of 0.831, G-mean of 0.811, and F1 score of 0.749 in identifying Fusarium, and achieved an AUC of 0.827 with an accuracy of 0.757, sensitivity of 0.756, specificity of 0.759, G-mean of 0.757, and F1 score of 0.716 in identifying Aspergillus. CONCLUSION The DL model can classify Fusarium and Aspergillus by learning effective features in IVCM images automatically. The automated IVCM image analysis suggests a noninvasive identification of Fusarium and Aspergillus with clear potential application in early diagnosis and management of fungal keratitis.
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Affiliation(s)
- Ningning Tang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Guangyi Huang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Daizai Lei
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Li Jiang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Qi Chen
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Wenjing He
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Fen Tang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Yiyi Hong
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Jian Lv
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Yuanjun Qin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Yunru Lin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Qianqian Lan
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Yikun Qin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Rushi Lan
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, People's Republic of China
| | - Xipeng Pan
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, People's Republic of China
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, 510080, People's Republic of China
| | - Min Li
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China
| | - Fan Xu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China.
| | - Peng Lu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, People's Republic of China.
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18
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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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19
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Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022; 10:1107689. [PMID: 36605721 PMCID: PMC9808405 DOI: 10.3389/fcell.2022.1107689] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
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20
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Li J, Wang S, Hu S, Sun Y, Wang Y, Xu P, Ye J. Class-Aware Attention Network for infectious keratitis diagnosis using corneal photographs. Comput Biol Med 2022; 151:106301. [PMID: 36403354 DOI: 10.1016/j.compbiomed.2022.106301] [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: 06/10/2022] [Revised: 10/18/2022] [Accepted: 11/06/2022] [Indexed: 11/11/2022]
Abstract
Infectious keratitis is one of the common ophthalmic diseases and also one of the main blinding eye diseases in China, hence rapid and accurate diagnosis and treatment for infectious keratitis are urgent to prevent the progression of the disease and limit the degree of corneal injury. Unfortunately, the traditional manual diagnosis accuracy is usually unsatisfactory due to the indistinguishable visual features. In this paper, we propose a novel end-to-end fully convolutional network, named Class-Aware Attention Network (CAA-Net), for automatically diagnosing infectious keratitis (normal, viral keratitis, fungal keratitis, and bacterial keratitis) using corneal photographs. In CAA-Net, a class-aware classification module is first trained to learn class-related discriminative features using separate branches for each class. Then, the learned class-aware discriminative features are fed into the main branch and fused with other feature maps using two attention strategies to assist the final multi-class classification performance. For the experiments, we have built a new corneal photograph dataset with 1886 images from 519 patients and conducted comprehensive experiments to verify the effectiveness of our proposed method. The code is available at https://github.com/SWF-hao/CAA-Net_Pytorch.
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Affiliation(s)
- Jinhao Li
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, China.
| | - Shuai Wang
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, China; Suzhou Research Institute of Shandong University, Suzhou, 215123, Jiangsu, China.
| | - Shaodan Hu
- Eye Center, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Yiming Sun
- Eye Center, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, 310018, Zhejiang, China.
| | - Peifang Xu
- Eye Center, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, 310009, Zhejiang, China.
| | - Juan Ye
- Eye Center, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, 310009, Zhejiang, China.
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21
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Jin X, Jin H, Shi Y, Zhang N, Zhang H. Clinical Observation of Corneal Endothelial Plaques With Fungal and Bacterial Keratitis by Anterior Segment Optical Coherence Tomography and In Vivo Confocal Microscopy. Cornea 2022; 41:1426-1432. [PMID: 34759198 PMCID: PMC9555760 DOI: 10.1097/ico.0000000000002912] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/02/2021] [Accepted: 09/13/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE Endothelial plaque is an important sign of fungal keratitis and is related to diagnosis, surgical indications, and prognosis. However, bacterial keratitis sometimes involves fibrin formation on the back corneal surface, similar to endothelial plaques. Because corneal infiltration interferes with precise observation of the posterior corneal plaque, distinguishing pathogens with a slitlamp is difficult. We hope to assist clinicians in early diagnosis and timely treatment by observing the connection state of endothelial plaques and the corneal endothelium through anterior segment optical coherence tomography (AS-OCT) and the different forms of endothelial plaques in infectious keratopathy through in vivo confocal microscopy (IVCM). METHODS We analyzed 52 patients in the Eye Hospital of the First Affiliated Hospital of Harbin Medical University who were clearly diagnosed with fungal or bacterial keratitis with endothelial plaques. All patients underwent AS-OCT and IVCM on admission. RESULTS According to the smear, IVCM, or fungal and bacterial culture results, the patients were diagnosed with fungal (28 patients) or bacterial keratitis (24 patients). AS-OCT in 25 patients diagnosed with fungal keratitis revealed that the corneal endothelium-endothelial plaque boundary was unclear and wavy, and 24 patients had unclear cell boundaries and a large number of compactly distributed inflammatory cells in the endothelial layer according to IVCM. AS-OCT in 23 patients diagnosed with bacterial keratitis revealed clear corneal endothelium-endothelial plaque boundaries, and insufficient endothelial cell boundaries with a large number of visible and scattered inflammatory cell structures were observed through IVCM in 22 patients. CONCLUSIONS Corneal endothelial plaque detection by AS-OCT and IVCM can be used for early diagnosis of infectious keratitis.
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Affiliation(s)
- Xin Jin
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, Heilongjiang Province, People's Republic of China; and
| | - Hao Jin
- Departments of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China
| | - Yan Shi
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, Heilongjiang Province, People's Republic of China; and
| | - Nan Zhang
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, Heilongjiang Province, People's Republic of China; and
| | - Hong Zhang
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, Heilongjiang Province, People's Republic of China; and
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22
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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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23
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Kim CB, Armstrong GW. Characterizing Infectious Keratitis Using Artificial Intelligence. Int Ophthalmol Clin 2022; 62:41-53. [PMID: 35325909 DOI: 10.1097/iio.0000000000000405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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Bakken IM, Jackson CJ, Utheim TP, Villani E, Hamrah P, Kheirkhah A, Nielsen E, Hau S, Lagali NS. The use of in vivo confocal microscopy in fungal keratitis - Progress and challenges. Ocul Surf 2022; 24:103-118. [PMID: 35278721 DOI: 10.1016/j.jtos.2022.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/02/2022] [Accepted: 03/02/2022] [Indexed: 01/02/2023]
Abstract
Fungal keratitis (FK) is a serious and sight-threatening corneal infection with global reach. The need for prompt diagnosis is paramount, as a delay in initiation of treatment could lead to irreversible vision loss. Current "gold standard" diagnostic methods, namely corneal smear and culture, have limitations due to diagnostic insensitivity and their time-consuming nature. PCR is a newer, complementary method used in the diagnosis of fungal keratitis, whose results are also sample-dependent. In vivo confocal microscopy (IVCM) is a promising complementary diagnostic method of increasing importance as it allows non-invasive real-time direct visualization of potential fungal pathogens and manifesting infection directly in the patient's cornea. In numerous articles and case reports, FK diagnosis by IVCM has been evaluated, and different features, approaches, sensitivity/specificity, and limitations have been noted. Here, we provide an up-to-date, comprehensive review of the current literature and present the authors' combined recommendations for fungal identification in IVCM images, while also looking to the future of FK assessment by IVCM using artificial intelligence methods.
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Affiliation(s)
- Ingvild M Bakken
- Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway
| | - Catherine J Jackson
- Ifocus Eye Clinic, Haugesund, Norway; Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway
| | - Tor P Utheim
- Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway; Department of Ophthalmology, Oslo University Hospital, Oslo, Norway; Department of Health and Nursing Science, The Faculty of Health and Sport Sciences, University of Agder, Grimstad, Norway
| | - Edoardo Villani
- Department of Clinical Science and Community Health, University of Milan, Italy; Eye Clinic San Giuseppe Hospital, IRCCS Multimedica, Milan, Italy
| | - Pedram Hamrah
- Cornea Service, New England Eye Center, Department of Ophthalmology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Ahmad Kheirkhah
- Department of Ophthalmology, Long School of Medicine, UT Health San Antonio, San Antonio, TX, USA
| | - Esben Nielsen
- Department of Ophthalmology, Aarhus University Hospital, Aarhus, Denmark
| | - Scott Hau
- Department of External Disease, NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom; UCL Institute of Ophthalmology, London, United Kingdom
| | - Neil S Lagali
- Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway; Division of Ophthalmology, Institute for Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
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25
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Xu F, Jiang L, He W, Huang G, Hong Y, Tang F, Lv J, Lin Y, Qin Y, Lan R, Pan X, Zeng S, Li M, Chen Q, Tang N. The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis Using in vivo Confocal Microscopy Images. Front Med (Lausanne) 2021; 8:797616. [PMID: 34970572 PMCID: PMC8712475 DOI: 10.3389/fmed.2021.797616] [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: 10/19/2021] [Accepted: 11/25/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Artificial intelligence (AI) has great potential to detect fungal keratitis using in vivo confocal microscopy images, but its clinical value remains unclarified. A major limitation of its clinical utility is the lack of explainability and interpretability. Methods: An explainable AI (XAI) system based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided Grad-CAM was established. In this randomized controlled trial, nine ophthalmologists (three expert ophthalmologists, three competent ophthalmologists, and three novice ophthalmologists) read images in each of the conditions: unassisted, AI-assisted, or XAI-assisted. In unassisted condition, only the original IVCM images were shown to the readers. AI assistance comprised a histogram of model prediction probability. For XAI assistance, explanatory maps were additionally shown. The accuracy, sensitivity, and specificity were calculated against an adjudicated reference standard. Moreover, the time spent was measured. Results: Both forms of algorithmic assistance increased the accuracy and sensitivity of competent and novice ophthalmologists significantly without reducing specificity. The improvement was more pronounced in XAI-assisted condition than that in AI-assisted condition. Time spent with XAI assistance was not significantly different from that without assistance. Conclusion: AI has shown great promise in improving the accuracy of ophthalmologists. The inexperienced readers are more likely to benefit from the XAI system. With better interpretability and explainability, XAI-assistance can boost ophthalmologist performance beyond what is achievable by the reader alone or with black-box AI assistance.
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Affiliation(s)
- Fan Xu
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Li Jiang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Wenjing He
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Guangyi Huang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yiyi Hong
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Fen Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Jian Lv
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yunru Lin
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yikun Qin
- China-ASEAN Information Harbor Co., Ltd., Nanning, China
| | - Rushi Lan
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China
| | - Xipeng Pan
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Siming Zeng
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Min Li
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Qi Chen
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ningning Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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26
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Koo T, Kim MH, Jue MS. Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network. PLoS One 2021; 16:e0256290. [PMID: 34403443 PMCID: PMC8370604 DOI: 10.1371/journal.pone.0256290] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 08/04/2021] [Indexed: 11/25/2022] Open
Abstract
Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer’s skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.
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Affiliation(s)
- Taehan Koo
- Department of Dermatology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Moon Hwan Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
| | - Mihn-Sook Jue
- Department of Dermatology, Veterans Health Service Medical Center, Seoul, Republic of Korea
- * E-mail:
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27
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Song A, Deshmukh R, Lin H, Ang M, Mehta JS, Chodosh J, Said DG, Dua HS, Ting DSJ. Post-keratoplasty Infectious Keratitis: Epidemiology, Risk Factors, Management, and Outcomes. Front Med (Lausanne) 2021; 8:707242. [PMID: 34307431 PMCID: PMC8292647 DOI: 10.3389/fmed.2021.707242] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 06/09/2021] [Indexed: 12/26/2022] Open
Abstract
Post-keratoplasty infectious keratitis (PKIK) represents a unique clinical entity that often poses significant diagnostic and therapeutic challenges. It carries a high risk of serious complications such as graft rejection and failure, and less commonly endophthalmitis. Topical corticosteroids are often required to reduce the risk of graft rejection but their use in PKIK may act as a double-edged sword, particularly in fungal infection. The increased uptake in lamellar keratoplasty in the recent years has also led to complications such as graft-host interface infectious keratitis (IIK), which is particularly difficult to manage. The reported incidence of PKIK differs considerably across different countries, with a higher incidence observed in developing countries (9.2-11.9%) than developed countries (0.02-7.9%). Common risk factors for PKIK include the use of topical corticosteroids, suture-related problems, ocular surface diseases and previous corneal infection. PKIK after penetrating keratoplasty or (deep) anterior lamellar keratoplasty is most commonly caused by ocular surface commensals, particularly Gramme-positive bacteria, whereas PKIK after endothelial keratoplasty is usually caused by Candida spp. Empirical broad-spectrum antimicrobial treatment is the mainstay of treatment for both PKIK, though surgical interventions are required in medically refractory cases (during the acute phase) and those affected by visually significant scarring (during the late phase). In this paper, we aim to provide a comprehensive overview on PKIK, encompassing the epidemiology, risk factors, causes, management and outcomes, and to propose a treatment algorithm for systematically managing this challenging condition.
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Affiliation(s)
- Anna Song
- Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rashmi Deshmukh
- Department of Ophthalmology, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, United Kingdom
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Jodhbir S. Mehta
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - James Chodosh
- Harvard Medical School, Massachusetts Eye and Ear, Boston, MA, United States
| | - Dalia G. Said
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, United Kingdom
| | - Harminder S. Dua
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, United Kingdom
| | - Darren S. J. Ting
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, United Kingdom
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28
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Rampat R, Deshmukh R, Chen X, Ting DSW, Said DG, Dua HS, Ting DSJ. Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions. Asia Pac J Ophthalmol (Phila) 2021; 10:268-281. [PMID: 34224467 PMCID: PMC7611495 DOI: 10.1097/apo.0000000000000394] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
ABSTRACT Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of blindness globally. The number of refractive surgeries, either cornea- or lens-based, is also on the rise as the demand for perfect vision continues to increase. With the recent advancement and potential promises of artificial intelligence (AI) technologies demonstrated in the realm of ophthalmology, particularly retinal diseases and glaucoma, AI researchers and clinicians are now channeling their focus toward the less explored ophthalmic areas related to the anterior segment of the eye. Conditions that rely on anterior segment imaging modalities, including slit-lamp photography, anterior segment optical coherence tomography, corneal tomography, in vivo confocal microscopy and/or optical biometers, are the most commonly explored areas. These include infectious keratitis, keratoconus, corneal grafts, ocular surface pathologies, preoperative screening before refractive surgery, intraocular lens calculation, and automated refraction, among others. In this review, we aimed to provide a comprehensive update on the utilization of AI in anterior segment diseases, with particular emphasis on the recent advancement in the past few years. In addition, we demystify some of the basic principles and terminologies related to AI, particularly machine learning and deep learning, to help improve the understanding, research and clinical implementation of these AI technologies among the ophthalmologists and vision scientists. As we march toward the era of digital health, guidelines such as CONSORT-AI, SPIRIT-AI, and STARD-AI will play crucial roles in guiding and standardizing the conduct and reporting of AI-related trials, ultimately promoting their potential for clinical translation.
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Affiliation(s)
| | - Rashmi Deshmukh
- Department of Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Xin Chen
- School of Computer Science, University of Nottingham, Nottingham, UK
| | - Daniel S. W. Ting
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore National Eye Centre / Singapore Eye Research Institute, Singapore
| | - Dalia G. Said
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
| | - Harminder S. Dua
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
| | - Darren S. J. Ting
- Singapore National Eye Centre / Singapore Eye Research Institute, Singapore
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
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29
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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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Xu F, Qin Y, He W, Huang G, Lv J, Xie X, Diao C, Tang F, Jiang L, Lan R, Cheng X, Xiao X, Zeng S, Chen Q, Cui L, Li M, Tang N. A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images. PLoS One 2021; 16:e0252653. [PMID: 34081736 PMCID: PMC8174724 DOI: 10.1371/journal.pone.0252653] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/19/2021] [Indexed: 01/10/2023] Open
Abstract
Purpose Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images. Methods A total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean. Results The best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545). Conclusions The deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.
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Affiliation(s)
- Fan Xu
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Yikun Qin
- China-ASEAN Information Harbor, Nanning, Guangxi, China
| | - Wenjing He
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Guangyi Huang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Jian Lv
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Xinxin Xie
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Chunli Diao
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Fen Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Li Jiang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Rushi Lan
- Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics, Guilin University of Electronic Technology, Guilin, Guangxi, China
| | - Xiaohui Cheng
- Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology, Guilin, Guangxi, China
| | - Xiaolin Xiao
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Siming Zeng
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Qi Chen
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Ling Cui
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Min Li
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
- * E-mail: (ML); (NT)
| | - Ningning Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
- * E-mail: (ML); (NT)
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