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Merle DA, Heidinger A, Horwath-Winter J, List W, Bauer H, Weissensteiner M, Kraus-Füreder P, Mayrhofer-Reinhartshuber M, Kainz P, Steinwender G, Wedrich A. Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis. Curr Eye Res 2024:1-8. [PMID: 38689527 DOI: 10.1080/02713683.2024.2344197] [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: 12/12/2023] [Accepted: 04/12/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface. METHODS Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface. RESULTS The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within a ± 10% range of the ground truth. CONCLUSIONS The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.
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Affiliation(s)
- David A Merle
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
- Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, Tübingen, Germany
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Astrid Heidinger
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
| | | | - Wolfgang List
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
| | - Heimo Bauer
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
| | | | | | | | | | | | - Andreas Wedrich
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
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Peng Z, Ma R, Zhang Y, Yan M, Lu J, Cheng Q, Liao J, Zhang Y, Wang J, Zhao Y, Zhu J, Qin B, Jiang Q, Shi F, Qian J, Chen X, Zhao C. Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study. Front Artif Intell 2023; 6:1323924. [PMID: 38145231 PMCID: PMC10748413 DOI: 10.3389/frai.2023.1323924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction Artificial intelligence (AI) technology has made rapid progress for disease diagnosis and triage. In the field of ophthalmic diseases, image-based diagnosis has achieved high accuracy but still encounters limitations due to the lack of medical history. The emergence of ChatGPT enables human-computer interaction, allowing for the development of a multimodal AI system that integrates interactive text and image information. Objective To develop a multimodal AI system using ChatGPT and anterior segment images for diagnosing and triaging ophthalmic diseases. To assess the AI system's performance through a two-stage cross-sectional study, starting with silent evaluation and followed by early clinical evaluation in outpatient clinics. Methods and analysis Our study will be conducted across three distinct centers in Shanghai, Nanjing, and Suqian. The development of the smartphone-based multimodal AI system will take place in Shanghai with the goal of achieving ≥90% sensitivity and ≥95% specificity for diagnosing and triaging ophthalmic diseases. The first stage of the cross-sectional study will explore the system's performance in Shanghai's outpatient clinics. Medical histories will be collected without patient interaction, and anterior segment images will be captured using slit lamp equipment. This stage aims for ≥85% sensitivity and ≥95% specificity with a sample size of 100 patients. The second stage will take place at three locations, with Shanghai serving as the internal validation dataset, and Nanjing and Suqian as the external validation dataset. Medical history will be collected through patient interviews, and anterior segment images will be captured via smartphone devices. An expert panel will establish reference standards and assess AI accuracy for diagnosis and triage throughout all stages. A one-vs.-rest strategy will be used for data analysis, and a post-hoc power calculation will be performed to evaluate the impact of disease types on AI performance. Discussion Our study may provide a user-friendly smartphone-based multimodal AI system for diagnosis and triage of ophthalmic diseases. This innovative system may support early detection of ocular abnormalities, facilitate establishment of a tiered healthcare system, and reduce the burdens on tertiary facilities. Trial registration The study was registered in ClinicalTrials.gov on June 25th, 2023 (NCT05930444).
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Affiliation(s)
- Zhiyu Peng
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Department of Ophthalmology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
| | - Ruiqi Ma
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
| | - Yihan Zhang
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
| | - Mingxu Yan
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
- School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Jie Lu
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
- School of Public Health, Fudan University, Shanghai, China
| | - Qian Cheng
- Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China
| | - Jingjing Liao
- Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China
| | - Yunqiu Zhang
- School of Public Health, Fudan University, Shanghai, China
| | - Jinghan Wang
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
| | - Yue Zhao
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
| | - Jiang Zhu
- Department of Ophthalmology, Suqian First Hospital, Suqian, China
| | - Bing Qin
- Department of Ophthalmology, Suqian First Hospital, Suqian, China
| | - Qin Jiang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Fei Shi
- Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China
| | - Jiang Qian
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
| | - Xinjian Chen
- Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
| | - Chen Zhao
- Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
- NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
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Sarayar R, Lestari YD, Setio AAA, Sitompul R. Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis. Front Public Health 2023; 11:1239231. [PMID: 38074720 PMCID: PMC10704127 DOI: 10.3389/fpubh.2023.1239231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
Background Infectious keratitis (IK) is a sight-threatening condition requiring immediate definite treatment. The need for prompt treatment heavily depends on timely diagnosis. The diagnosis of IK, however, is challenged by the drawbacks of the current "gold standard." The poorly differentiated clinical features, the possibility of low microbial culture yield, and the duration for culture are the culprits of delayed IK treatment. Deep learning (DL) is a recent artificial intelligence (AI) advancement that has been demonstrated to be highly promising in making automated diagnosis in IK with high accuracy. However, its exact accuracy is not yet elucidated. This article is the first systematic review and meta-analysis that aims to assess the accuracy of available DL models to correctly classify IK based on etiology compared to the current gold standards. Methods A systematic search was carried out in PubMed, Google Scholars, Proquest, ScienceDirect, Cochrane and Scopus. The used keywords are: "Keratitis," "Corneal ulcer," "Corneal diseases," "Corneal lesions," "Artificial intelligence," "Deep learning," and "Machine learning." Studies including slit lamp photography of the cornea and validity study on DL performance were considered. The primary outcomes reviewed were the accuracy and classification capability of the AI machine learning/DL algorithm. We analyzed the extracted data with the MetaXL 5.2 Software. Results A total of eleven articles from 2002 to 2022 were included with a total dataset of 34,070 images. All studies used convolutional neural networks (CNNs), with ResNet and DenseNet models being the most used models across studies. Most AI models outperform the human counterparts with a pooled area under the curve (AUC) of 0.851 and accuracy of 96.6% in differentiating IK vs. non-IK and pooled AUC 0.895 and accuracy of 64.38% for classifying bacterial keratitis (BK) vs. fungal keratitis (FK). Conclusion This study demonstrated that DL algorithms have high potential in diagnosing and classifying IK with accuracy that, if not better, is comparable to trained corneal experts. However, various factors, such as the unique architecture of DL model, the problem with overfitting, image quality of the datasets, and the complex nature of IK itself, still hamper the universal applicability of DL in daily clinical practice.
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Affiliation(s)
- Randy Sarayar
- Residency Program in Ophthalmology Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Yeni Dwi Lestari
- Department of Ophthalmology, Faculty of Medicine Universitas Indonesia – Cipto Mangunkusumo General Hospital, Jakarta, Indonesia
| | - Arnaud A. A. Setio
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Ratna Sitompul
- Department of Ophthalmology, Faculty of Medicine Universitas Indonesia – Cipto Mangunkusumo General Hospital, Jakarta, Indonesia
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Kuo MT, Hsu BWY, Lin YS, Fang PC, Yu HJ, Hsiao YT, Tseng VS. Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks. Transl Vis Sci Technol 2023; 12:1. [PMID: 37910082 PMCID: PMC10627292 DOI: 10.1167/tvst.12.11.1] [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: 04/27/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
Purpose For this study, we aimed to determine whether a convolutional neural network (CNN)-based method (based on a feature extractor and an identifier) can be applied to monitor the progression of keratitis while managing suspected microbial keratitis (MK). Methods This multicenter longitudinal cohort study included patients with suspected MK undergoing serial external eye photography at the 5 branches of Chang Gung Memorial Hospital from August 20, 2000, to August 19, 2020. Data were primarily analyzed from January 1 to March 25, 2022. The CNN-based model was evaluated via F1 score and accuracy. The area under the receiver operating characteristic curve (AUROC) was used to measure the precision-recall trade-off. Results The model was trained using 1456 image pairs from 468 patients. In comparing models via only training the identifier, statistically significant higher accuracy (P < 0.05) in models via training both the identifier and feature extractor (full training) was verified, with 408 image pairs from 117 patients. The full training EfficientNet b3-based model showed 90.2% (getting better) and 82.1% (becoming worse) F1 scores, 87.3% accuracy, and 94.2% AUROC for 505 getting better and 272 becoming worse test image pairs from 452 patients. Conclusions A CNN-based approach via deep learning applied in suspected MK can monitor the progress/regress during treatment by comparing external eye image pairs. Translational Relevance The study bridges the gap between the investigation of the state-of-the-art CNN-based deep learning algorithm applied in ocular image analysis and the clinical care of suspected patients with MK.
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Affiliation(s)
- Ming-Tse Kuo
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
| | - Benny Wei-Yun Hsu
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yi Sheng Lin
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Po-Chiung Fang
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
| | - Hun-Ju Yu
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
| | - Yu-Ting Hsiao
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
| | - Vincent S. Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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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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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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7
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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: 0] [Impact Index Per Article: 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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Ong ZZ, Sadek Y, Liu X, Qureshi R, Liu SH, Li T, Sounderajah V, Ashrafian H, Ting DSW, Said DG, Mehta JS, Burton MJ, Dua HS, Ting DSJ. Diagnostic performance of deep learning in infectious keratitis: a systematic review and meta-analysis protocol. BMJ Open 2023; 13:e065537. [PMID: 37164459 PMCID: PMC10173987 DOI: 10.1136/bmjopen-2022-065537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 04/27/2023] [Indexed: 05/12/2023] Open
Abstract
INTRODUCTION Infectious keratitis (IK) represents the fifth-leading cause of blindness worldwide. A delay in diagnosis is often a major factor in progression to irreversible visual impairment and/or blindness from IK. The diagnostic challenge is further compounded by low microbiological culture yield, long turnaround time, poorly differentiated clinical features and polymicrobial infections. In recent years, deep learning (DL), a subfield of artificial intelligence, has rapidly emerged as a promising tool in assisting automated medical diagnosis, clinical triage and decision-making, and improving workflow efficiency in healthcare services. Recent studies have demonstrated the potential of using DL in assisting the diagnosis of IK, though the accuracy remains to be elucidated. This systematic review and meta-analysis aims to critically examine and compare the performance of various DL models with clinical experts and/or microbiological results (the current 'gold standard') in diagnosing IK, with an aim to inform practice on the clinical applicability and deployment of DL-assisted diagnostic models. METHODS AND ANALYSIS This review will consider studies that included application of any DL models to diagnose patients with suspected IK, encompassing bacterial, fungal, protozoal and/or viral origins. We will search various electronic databases, including EMBASE and MEDLINE, and trial registries. There will be no restriction to the language and publication date. Two independent reviewers will assess the titles, abstracts and full-text articles. Extracted data will include details of each primary studies, including title, year of publication, authors, types of DL models used, populations, sample size, decision threshold and diagnostic performance. We will perform meta-analyses for the included primary studies when there are sufficient similarities in outcome reporting. ETHICS AND DISSEMINATION No ethical approval is required for this systematic review. We plan to disseminate our findings via presentation/publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER CRD42022348596.
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Affiliation(s)
- Zun Zheng Ong
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK
| | - Youssef Sadek
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK
| | - Xiaoxuan Liu
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Riaz Qureshi
- Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Su-Hsun Liu
- Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Tianjing Li
- Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Viknesh Sounderajah
- Institute of Global Health Innovation, Imperial College London, London, UK
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Daniel Shu Wei Ting
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Dalia G Said
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK
- Research Institute of Ophthalmology, Cairo, Egypt
| | - Jodhbir S Mehta
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Matthew J Burton
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, London, UK
- National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Harminder Singh Dua
- Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Darren Shu Jeng Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK
- Birmingham and Midland Eye Centre, Birmingham, UK
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Hubbard DC, Cox P, Redd TK. Assistive applications of artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:261-266. [PMID: 36728651 PMCID: PMC10065924 DOI: 10.1097/icu.0000000000000939] [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] [Indexed: 02/03/2023]
Abstract
PURPOSE OF REVIEW Assistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications. RECENT FINDINGS Artificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies. SUMMARY Over the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.
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Affiliation(s)
- Donald C Hubbard
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Parker Cox
- Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Travis K Redd
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
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10
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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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11
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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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12
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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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13
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Ting DSJ, Chodosh J, Mehta JS. Achieving diagnostic excellence for infectious keratitis: A future roadmap. Front Microbiol 2022; 13:1020198. [PMID: 36262329 PMCID: PMC9576146 DOI: 10.3389/fmicb.2022.1020198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 09/20/2022] [Indexed: 02/02/2023] Open
Affiliation(s)
- Darren S. J. Ting
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,Department of Ophthalmology, Queen's Medical Centre, Nottingham, United Kingdom
| | - James Chodosh
- Department of Ophthalmology, Massachusetts Eye and Ear and Harvard Medical School, Boston, MA, United States
| | - Jodhbir S. Mehta
- Department of Cornea & Refractive Surgery, Singapore National Eye Centre, Singapore, Singapore,Singapore Eye Research Institute, Singapore, Singapore,*Correspondence: Jodhbir S. Mehta
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14
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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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15
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Cabrera‐Aguas M, Khoo P, Watson SL. Infectious keratitis: A review. Clin Exp Ophthalmol 2022; 50:543-562. [PMID: 35610943 PMCID: PMC9542356 DOI: 10.1111/ceo.14113] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 12/29/2022]
Abstract
Globally, infectious keratitis is the fifth leading cause of blindness. The main predisposing factors include contact lens wear, ocular injury and ocular surface disease. Staphylococcus species, Pseudomonas aeruginosa, Fusarium species, Candida species and Acanthamoeba species are the most common causal organisms. Culture of corneal scrapes is the preferred initial test to identify the culprit organism. Polymerase chain reaction (PCR) tests and in vivo confocal microscopy can complement the diagnosis. Empiric therapy is typically commenced with fluoroquinolones, or fortified antibiotics for bacterial keratitis; topical natamycin for fungal keratitis; and polyhexamethylene biguanide or chlorhexidine for acanthamoeba keratitis. Herpes simplex keratitis is mainly diagnosed clinically; however, PCR can also be used to confirm the initial diagnosis and in atypical cases. Antivirals and topical corticosteroids are indicated depending on the corneal layer infected. Vision impairment, blindness and even loss of the eye can occur with a delay in diagnosis and inappropriate antimicrobial therapy.
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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 New South Wales Australia
- Corneal Unit Sydney Eye Hospital Sydney New South Wales Australia
| | - Pauline Khoo
- Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health The University of Sydney Sydney New South Wales Australia
- Corneal Unit Sydney Eye Hospital Sydney New South Wales Australia
| | - Stephanie L. Watson
- Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health The University of Sydney Sydney New South Wales Australia
- Corneal Unit Sydney Eye Hospital Sydney New South Wales Australia
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16
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Clinical Characteristics and Outcomes of Fungal Keratitis in the United Kingdom 2011-2020: A 10-Year Study. J Fungi (Basel) 2021; 7:jof7110966. [PMID: 34829253 PMCID: PMC8624743 DOI: 10.3390/jof7110966] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/08/2021] [Accepted: 11/08/2021] [Indexed: 12/27/2022] Open
Abstract
Fungal keratitis (FK) is a serious ocular infection that often poses significant diagnostic and therapeutic dilemmas. This study aimed to examine the causes, clinical characteristics, outcomes, and prognostic factors of FK in the UK. All culture-positive and culture-negative presumed FK (with complete data) that presented to Queen’s Medical Centre, Nottingham, and the Queen Victoria Hospital, East Grinstead, between 2011 and 2020 were included. We included 117 patients (n = 117 eyes) with FK in this study. The mean age was 59.0 ± 19.6 years (range, 4–92 years) and 51.3% of patients were female. Fifty-three fungal isolates were identified from 52 (44.4%) culture-positive cases, with Candida spp. (33, 62.3%), Fusarium spp. (9, 17.0%), and Aspergillus spp. (5, 9.4%) being the most common organisms. Ocular surface disease (60, 51.3%), prior corneal surgery (44, 37.6%), and systemic immunosuppression (42, 35.9%) were the three most common risk factors. Hospitalisation for intensive treatment was required for 95 (81.2%) patients, with a duration of 18.9 ± 16.3 days. Sixty-six (56.4%) patients required additional surgical interventions for eradicating the infection. Emergency therapeutic/tectonic keratoplasty was performed in 29 (24.8%) cases, though 13 (44.8%) of them failed at final follow-up. The final corrected-distance-visual-acuity (CDVA) was 1.67 ± 1.08 logMAR. Multivariable logistic regression analyses demonstrated increased age, large infiltrate size (>3 mm), and poor presenting CDVA (<1.0 logMAR) as significant negative predictive factors for poor visual outcome (CDVA of <1.0 logMAR) and poor corneal healing (>60 days of healing time or occurrence of corneal perforation requiring emergency keratoplasty; all p < 0.05). In conclusion, FK represents a difficult-to-treat ocular infection that often results in poor visual outcomes, with a high need for surgical interventions. Innovative treatment strategies are urgently required to tackle this unmet need.
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