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Alió del Barrio JL, Eldanasoury AM, Arbelaez J, Faini S, Versaci F. Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer. Transl Vis Sci Technol 2024; 13:13. [PMID: 38587437 PMCID: PMC11005070 DOI: 10.1167/tvst.13.4.13] [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: 08/22/2023] [Accepted: 02/19/2024] [Indexed: 04/09/2024] Open
Abstract
Purpose To assess the efficacy of an automated program for keratoconus and keratoconus suspect detection based on corneal measurements provided by a combined Placido disc and anterior segment optical coherence tomography (OCT) topographer. Methods In a multicentric cross-sectional study, an artificial neural network (ANN) was created using 6677 eyes from an equal number of patients (classified as 2663 normal eyes, 1616 keratoconus eyes, 210 keratoconus suspect eyes, 1519 myopic postoperative eyes, and 669 abnormal eyes). Each group was randomly divided into a training set (70% of the dataset) and a validation set (the remaining 30%). A multilayer perceptron network with a backpropagation learning algorithm was developed for the study. Indexes used to train the ANN were based on curvature and elevation of both the anterior and posterior corneal surfaces and the new corneal OCT indexes-based on corneal, stromal, and epithelial thicknesses. Results For keratoconus detection, our ANN showed an accuracy of 98.6%, precision of 96%, recall of 97.9%, and F1-score of 96.9%. For keratoconus suspect detection, our ANN showed an accuracy of 98.5%, precision of 83.6%, recall of 69.7%, and F1-score of 76%. Conclusions Compared to previous literature, the addition of new OCT-based epithelial and stromal thickness indexes improves ANN detection capacity of keratoconus suspect eyes. For already stablished keratoconus our ANN detection capacity is excellent, but equivalent to previous evidence without incorporating such new OCT-based indexes. Translational Relevance OCT-based epithelial and stromal thickness indexes improve ANN detection capacity of keratoconus on its early stages.
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Affiliation(s)
- Jorge L. Alió del Barrio
- Cornea, Cataract and Refractive Surgery Unit, Vissum (Miranza Group), Alicante, Spain
- Division of Ophthalmology, School of Medicine, Universidad Miguel Hernández, Alicante, Spain
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Afifah A, Syafira F, Afladhanti PM, Dharmawidiarini D. Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis. J Taibah Univ Med Sci 2024; 19:296-303. [PMID: 38283379 PMCID: PMC10821587 DOI: 10.1016/j.jtumed.2023.12.007] [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: 07/20/2023] [Revised: 11/13/2023] [Accepted: 12/25/2023] [Indexed: 01/30/2024] Open
Abstract
Objectives The challenges in diagnosing keratoconus (KC) have led researchers to explore the use of artificial intelligence (AI) as a diagnostic tool. AI has emerged as a new way to improve the efficiency of KC diagnosis. This study analyzed the use of AI as a diagnostic modality for KC. Methods This study used a systematic review and meta-analysis following the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched selected databases using a combination of search terms: "((Artificial Intelligence) OR (Diagnostic Modality)) AND (Keratoconus)" from PubMed, Medline, and ScienceDirect within the last 5 years (2018-2023). Following a systematic review protocol, we selected 11 articles and 6 articles were eligible for final analysis. The relevant data were analyzed with Review Manager 5.4 software and the final output was presented in a forest plot. Results This research found neural networks as the most used AI model in diagnosing KC. Neural networks and naïve bayes showed the highest accuracy of AI in diagnosing KC with a sensitivity of 1.00, while random forests were >0.90. All studies in each group have proven high sensitivity and specificity over 0.90. Conclusions AI potentially makes a better diagnosis of the KC with its high performance, particularly on sensitivity and specificity, which can help clinicians make medical decisions about an individual patient.
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Affiliation(s)
- Azzahra Afifah
- Undaan Eye Hospital, Surabaya, Indonesia
- Medical Profession Program, Faculty of Medicine, Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
| | - Fara Syafira
- Medical Profession Program, Faculty of Medicine, Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
| | - Putri Mahirah Afladhanti
- Medical Profession Program, Faculty of Medicine, Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
| | - Dini Dharmawidiarini
- Lens, Cornea and Refractive Surgery Division, Undaan Eye Hospital, Surabaya, Indonesia
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Hashemi H, Doroodgar F, Niazi S, Khabazkhoob M, Heidari Z. Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis. Graefes Arch Clin Exp Ophthalmol 2024; 262:1017-1039. [PMID: 37418053 DOI: 10.1007/s00417-023-06154-6] [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: 11/12/2022] [Revised: 04/18/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
PURPOSE This review was designed to compare different corneal imaging modalities using artificial intelligence (AI) for the diagnosis of keratoconus (KCN), subclinical KCN (SKCN), and forme fruste KCN (FFKCN). METHODS A comprehensive systematic search was conducted in scientific databases, including Web of Science, PubMed, Scopus, and Google Scholar based on the PRISMA statement. Two independent reviewers assessed all potential publications on AI and KCN up to March 2022. The Critical Appraisal Skills Program (CASP) 11-item checklist was used to evaluate the validity of the studies. Eligible articles were categorized into three groups (KCN, SKCN, and FFKCN) and included in the meta-analysis. The pooled estimate of accuracy (PEA) was calculated for all selected articles. RESULTS The initial search yielded 575 relevant publications, of which 36 met the CASP quality criteria and were included in the analysis. Qualitative assessment showed that Scheimpflug and Placido combined with biomechanical and wavefront evaluations improved KCN detection (PEA, 99.2, and 99.0, respectively). The Scheimpflug system (92.25 PEA, 95% CI, 94.76-97.51) and a combination of Scheimpflug and Placido (96.44 PEA, 95% CI, 93.13-98.19) had the highest diagnostic accuracy for the detection of SKCN and FFKCN, respectively. The meta-analysis outcomes showed no significant difference between the CASP score and accuracy of the publications (all P > 0.05). CONCLUSIONS Simultaneous Scheimpflug and Placido corneal imaging methods provide high diagnostic accuracy for early detection of keratoconus. The use of AI models improves the discrimination of keratoconic eyes from normal corneas.
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Affiliation(s)
- Hassan Hashemi
- Noor Research Center for Ophthalmic Epidemiology, Noor Eye Hospital, Tehran, Iran
| | - Farideh Doroodgar
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Negah Eye Hospital Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sana Niazi
- Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Khabazkhoob
- Department of Medical Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Heidari
- Department of Ophthalmology, Bu-Ali Sina Hospital, Mazandaran University of Medical Sciences, Sari, Iran.
- Psychiatry and Behavioral Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran.
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Abtahi MA, Beheshtnejad AH, Latifi G, Akbari-Kamrani M, Ghafarian S, Masoomi A, Sonbolastan SA, Jahanbani-Ardakani H, Atighechian M, Banan L, Nouri H, Abtahi SH. Corneal Epithelial Thickness Mapping: A Major Review. J Ophthalmol 2024; 2024:6674747. [PMID: 38205099 PMCID: PMC10776199 DOI: 10.1155/2024/6674747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/27/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The corneal epithelium (CE) is the outermost layer of the cornea with constant turnover, relative stability, remarkable plasticity, and compensatory properties to mask alterations in the underlying stroma. The advent of quantitative imaging modalities capable of producing epithelial thickness mapping (ETM) has made it possible to characterize better the different patterns of epithelial remodeling. In this comprehensive synthesis, we reviewed all available data on ETM with different methods, including very high-frequency ultrasound (VHF-US) and spectral-domain optical coherence tomography (SD-OCT) in normal individuals, corneal or systemic diseases, and corneal surgical scenarios. We excluded OCT studies that manually measured the corneal epithelial thickness (CET) (e.g., by digital calipers) or the CE (e.g., by confocal scanning or handheld pachymeters). A comparison of different CET measuring technologies and devices capable of producing thickness maps is provided. Normative data on CET and the possible effects of gender, aging, diurnal changes, refraction, and intraocular pressure are discussed. We also reviewed ETM data in several corneal disorders, including keratoconus, corneal dystrophies, recurrent epithelial erosion, herpes keratitis, keratoplasty, bullous keratopathy, carcinoma in situ, pterygium, and limbal stem cell deficiency. The available data on the potential role of ETM in indicating refractive surgeries, planning the procedure, and assessing postoperative changes are reviewed. Alterations in ETM in systemic and ocular conditions such as eyelid abnormalities and dry eye disease and the effects of contact lenses, topical medications, and cataract surgery on the ETM profile are discussed.
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Affiliation(s)
| | | | - Golshan Latifi
- Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Sadegh Ghafarian
- Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Masoomi
- Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | | | - Laleh Banan
- Sunshine Coast University Hospital, Brisbane, Queensland, Australia
| | - Hosein Nouri
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed-Hossein Abtahi
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Niazi S, Gatzioufas Z, Doroodgar F, Findl O, Baradaran-Rafii A, Liechty J, Moshirfar M. Keratoconus: exploring fundamentals and future perspectives - a comprehensive systematic review. Ther Adv Ophthalmol 2024; 16:25158414241232258. [PMID: 38516169 PMCID: PMC10956165 DOI: 10.1177/25158414241232258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 01/22/2024] [Indexed: 03/23/2024] Open
Abstract
Background New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way. Objectives This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies. Design A multidimensional comprehensive systematic narrative review. Data sources and methods A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed. Results Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes. Conclusion The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients. Trial registration The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
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Affiliation(s)
- Sana Niazi
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zisis Gatzioufas
- Department of Ophthalmology, University Eye Hospital Basel, Basel, Switzerland
| | - Farideh Doroodgar
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran Province, Tehran, District 6, Pour Sina St, P94V+8MF, Tehran 1416753955, Iran
- Negah Aref Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Oliver Findl
- Department of Ophthalmology, Hanusch Hospital, Vienna Institute for Research in Ocular Surgery (VIROS), Vienna, Austria
| | - Alireza Baradaran-Rafii
- Department of Ophthalmology, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Jacob Liechty
- Department of Ophthalmology, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Majid Moshirfar
- John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA
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Agharezaei Z, Firouzi R, Hassanzadeh S, Zarei-Ghanavati S, Bahaadinbeigy K, Golabpour A, Akbarzadeh R, Agharezaei L, Bakhshali MA, Sedaghat MR, Eslami S. Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning. Sci Rep 2023; 13:20586. [PMID: 37996439 PMCID: PMC10667539 DOI: 10.1038/s41598-023-46903-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/07/2023] [Indexed: 11/25/2023] Open
Abstract
Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This study aims to develop and evaluate the accuracy of deep convolutional neural network (CNN) models for the detection of keratoconus (KCN) using corneal topographic maps. We retrospectively collected 1758 corneal images (978 normal and 780 keratoconus) from 1010 subjects of the KCN group with clinically evident keratoconus and the normal group with regular astigmatism. To expand the dataset, we developed a model using Variational Auto Encoder (VAE) to generate and augment images, resulting in a dataset of 4000 samples. Four deep learning models were used to extract and identify deep corneal features of original and synthesized images. We demonstrated that the utilization of synthesized images during training process increased classification performance. The overall average accuracy of the deep learning models ranged from 99% for VGG16 to 95% for EfficientNet-B0. All CNN models exhibited sensitivity and specificity above 0.94, with the VGG16 model achieving an AUC of 0.99. The customized CNN model achieved satisfactory results with an accuracy and AUC of 0.97 at a much faster processing speed compared to other models. In conclusion, the DL models showed high accuracy in screening for keratoconus based on corneal topography images. This is a development toward the potential clinical implementation of a more enhanced computer-aided diagnosis (CAD) system for KCN detection, which would aid ophthalmologists in validating the clinical decision and carrying out prompt and precise KCN treatment.
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Affiliation(s)
- Zhila Agharezaei
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Informatics Research Center, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Reza Firouzi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Samira Hassanzadeh
- School of Paramedical Sciences and Rehabilitation, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Amin Golabpour
- School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Reyhaneh Akbarzadeh
- Department of Optometry, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Laleh Agharezaei
- Modeling in Health Research Center, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Mohamad Amin Bakhshali
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Saeid Eslami
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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7
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Vandevenne MM, Favuzza E, Veta M, Lucenteforte E, Berendschot TT, Mencucci R, Nuijts RM, Virgili G, Dickman MM. Artificial intelligence for detecting keratoconus. Cochrane Database Syst Rev 2023; 11:CD014911. [PMID: 37965960 PMCID: PMC10646985 DOI: 10.1002/14651858.cd014911.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
BACKGROUND Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, when there are no clinical signs, diagnosis depends on the interpretation of corneal imaging (e.g. topography and tomography) by trained cornea specialists. Using artificial intelligence (AI) to analyse the corneal images and detect cases of keratoconus could help prevent visual acuity loss and even corneal transplantation. However, a missed diagnosis in people seeking refractive surgery could lead to weakening of the cornea and keratoconus-like ectasia. There is a need for a reliable overview of the accuracy of AI for detecting keratoconus and the applicability of this automated method to the clinical setting. OBJECTIVES To assess the diagnostic accuracy of artificial intelligence (AI) algorithms for detecting keratoconus in people presenting with refractive errors, especially those whose vision can no longer be fully corrected with glasses, those seeking corneal refractive surgery, and those suspected of having keratoconus. AI could help ophthalmologists, optometrists, and other eye care professionals to make decisions on referral to cornea specialists. Secondary objectives To assess the following potential causes of heterogeneity in diagnostic performance across studies. • Different AI algorithms (e.g. neural networks, decision trees, support vector machines) • Index test methodology (preprocessing techniques, core AI method, and postprocessing techniques) • Sources of input to train algorithms (topography and tomography images from Placido disc system, Scheimpflug system, slit-scanning system, or optical coherence tomography (OCT); number of training and testing cases/images; label/endpoint variable used for training) • Study setting • Study design • Ethnicity, or geographic area as its proxy • Different index test positivity criteria provided by the topography or tomography device • Reference standard, topography or tomography, one or two cornea specialists • Definition of keratoconus • Mean age of participants • Recruitment of participants • Severity of keratoconus (clinically manifest or subclinical) SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Trials Register), Ovid MEDLINE, Ovid Embase, OpenGrey, the ISRCTN registry, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). There were no date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 29 November 2022. SELECTION CRITERIA We included cross-sectional and diagnostic case-control studies that investigated AI for the diagnosis of keratoconus using topography, tomography, or both. We included studies that diagnosed manifest keratoconus, subclinical keratoconus, or both. The reference standard was the interpretation of topography or tomography images by at least two cornea specialists. DATA COLLECTION AND ANALYSIS Two review authors independently extracted the study data and assessed the quality of studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When an article contained multiple AI algorithms, we selected the algorithm with the highest Youden's index. We assessed the certainty of evidence using the GRADE approach. MAIN RESULTS We included 63 studies, published between 1994 and 2022, that developed and investigated the accuracy of AI for the diagnosis of keratoconus. There were three different units of analysis in the studies: eyes, participants, and images. Forty-four studies analysed 23,771 eyes, four studies analysed 3843 participants, and 15 studies analysed 38,832 images. Fifty-four articles evaluated the detection of manifest keratoconus, defined as a cornea that showed any clinical sign of keratoconus. The accuracy of AI seems almost perfect, with a summary sensitivity of 98.6% (95% confidence interval (CI) 97.6% to 99.1%) and a summary specificity of 98.3% (95% CI 97.4% to 98.9%). However, accuracy varied across studies and the certainty of the evidence was low. Twenty-eight articles evaluated the detection of subclinical keratoconus, although the definition of subclinical varied. We grouped subclinical keratoconus, forme fruste, and very asymmetrical eyes together. The tests showed good accuracy, with a summary sensitivity of 90.0% (95% CI 84.5% to 93.8%) and a summary specificity of 95.5% (95% CI 91.9% to 97.5%). However, the certainty of the evidence was very low for sensitivity and low for specificity. In both groups, we graded most studies at high risk of bias, with high applicability concerns, in the domain of patient selection, since most were case-control studies. Moreover, we graded the certainty of evidence as low to very low due to selection bias, inconsistency, and imprecision. We could not explain the heterogeneity between the studies. The sensitivity analyses based on study design, AI algorithm, imaging technique (topography versus tomography), and data source (parameters versus images) showed no differences in the results. AUTHORS' CONCLUSIONS AI appears to be a promising triage tool in ophthalmologic practice for diagnosing keratoconus. Test accuracy was very high for manifest keratoconus and slightly lower for subclinical keratoconus, indicating a higher chance of missing a diagnosis in people without clinical signs. This could lead to progression of keratoconus or an erroneous indication for refractive surgery, which would worsen the disease. We are unable to draw clear and reliable conclusions due to the high risk of bias, the unexplained heterogeneity of the results, and high applicability concerns, all of which reduced our confidence in the evidence. Greater standardization in future research would increase the quality of studies and improve comparability between studies.
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Affiliation(s)
- Magali Ms Vandevenne
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Eleonora Favuzza
- Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
| | - Mitko Veta
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Ersilia Lucenteforte
- Department of Statistics, Computer Science and Applications «G. Parenti», University of Florence, Florence, Italy
| | - Tos Tjm Berendschot
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Rita Mencucci
- Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
| | - Rudy Mma Nuijts
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Gianni Virgili
- Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
- Queen's University Belfast, Belfast, UK
| | - Mor M Dickman
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
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Shao Y, Jie Y, Liu ZG. Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023). Int J Ophthalmol 2023; 16:1373-1385. [PMID: 37724278 PMCID: PMC10475626 DOI: 10.18240/ijo.2023.09.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/14/2023] [Indexed: 09/20/2023] Open
Abstract
The landscape of ophthalmology has observed monumental shifts with the advent of artificial intelligence (AI) technologies. This article is devoted to elaborating on the nuanced application of AI in the diagnostic realm of anterior segment eye diseases, an area ripe with potential yet complex in its imaging characteristics. Historically, AI's entrenchment in ophthalmology was predominantly rooted in the posterior segment. However, the evolution of machine learning paradigms, particularly with the advent of deep learning methodologies, has reframed the focus. When combined with the exponential surge in available electronic image data pertaining to the anterior segment, AI's role in diagnosing corneal, conjunctival, lens, and eyelid pathologies has been solidified and has emerged from the realm of theoretical to practical. In light of this transformative potential, collaborations between the Ophthalmic Imaging and Intelligent Medicine Subcommittee of the China Medical Education Association and the Ophthalmology Committee of the International Translational Medicine Association have been instrumental. These eminent bodies mobilized a consortium of experts to dissect and assimilate advancements from both national and international quarters. Their mandate was not limited to AI's application in anterior segment pathologies like the cornea, conjunctiva, lens, and eyelids, but also ventured into deciphering the existing impediments and envisioning future trajectories. After iterative deliberations, the consensus synthesized herein serves as a touchstone, assisting ophthalmologists in optimally integrating AI into their diagnostic decisions and bolstering clinical research. Through this guideline, we aspire to offer a comprehensive framework, ensuring that clinical decisions are not merely informed but transformed by AI. By building upon existing literature yet maintaining the highest standards of originality, this document stands as a testament to both innovation and academic integrity, in line with the ethos of renowned journals such as Ophthalmology.
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Affiliation(s)
- Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Ying Jie
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University; Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing 100730, China
| | - Zu-Guo Liu
- Eye Institute of Xiamen University, Xiamen 361102, Fujian Province, China
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Niazi S, Jiménez-García M, Findl O, Gatzioufas Z, Doroodgar F, Shahriari MH, Javadi MA. Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review. Diagnostics (Basel) 2023; 13:2715. [PMID: 37627975 PMCID: PMC10453081 DOI: 10.3390/diagnostics13162715] [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: 05/24/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023] Open
Abstract
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
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Affiliation(s)
- Sana Niazi
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran P.O. Box 1336616351, Iran;
| | - Marta Jiménez-García
- Department of Ophthalmology, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
- Department of Medicine and Health Sciences, University of Antwerp, 2000 Antwerp, Belgium
| | - Oliver Findl
- Department of Ophthalmology, Vienna Institute for Research in Ocular Surgery (VIROS), Hanusch Hospital, 1140 Vienna, Austria
| | - Zisis Gatzioufas
- Department of Ophthalmology, University Hospital Basel, 4031 Basel, Switzerland;
| | - Farideh Doroodgar
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran P.O. Box 1336616351, Iran;
- Negah Aref Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 1544914599, Iran
| | - Mohammad Hasan Shahriari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 1971653313, Iran
| | - Mohammad Ali Javadi
- Ophthalmic Research Center, Labbafinezhad Hospital, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 19395-4741, Iran
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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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11
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Zhang J, Mazlin V, Fei K, Boccara AC, Yuan J, Xiao P. Time-domain full-field optical coherence tomography (TD-FF-OCT) in ophthalmic imaging. Ther Adv Chronic Dis 2023; 14:20406223231170146. [PMID: 37152350 PMCID: PMC10161339 DOI: 10.1177/20406223231170146] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 03/29/2023] [Indexed: 05/09/2023] Open
Abstract
Ocular imaging plays an irreplaceable role in the evaluation of eye diseases. Developing cellular-resolution ophthalmic imaging technique for more accurate and effective diagnosis and pathogenesis analysis of ocular diseases is a hot topic in the cross-cutting areas of ophthalmology and imaging. Currently, ocular imaging with traditional optical coherence tomography (OCT) is limited in lateral resolution and thus can hardly resolve cellular structures. Conventional OCT technology obtains ultra-high resolution at the expense of a certain imaging range and cannot achieve full field of view imaging. In the early years, Time-domain full-field OCT (TD-FF-OCT) has been mainly used for ex vivo ophthalmic tissue studies, limited by the low speed and low full-well capacity of existing two-dimensional (2D) cameras. The recent improvements in system design opened new imaging possibilities for in vivo applications thanks to its distinctive optical properties of TD-FF-OCT such as a spatial resolution almost insensitive to aberrations, and the possibility to control the curvature of the optical slice. This review also attempts to look at the future directions of TD-FF-OCT evolution, for example, the potential transfer of the functional-imaging dynamic TD-FF-OCT from the ex vivo into in vivo use and its expected benefit in basic and clinical ophthalmic research. Through non-invasive, wide-field, and cellular-resolution imaging, TD-FF-OCT has great potential to be the next-generation imaging modality to improve our understanding of human eye physiology and pathology.
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Affiliation(s)
- Jinze Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Viacheslav Mazlin
- ESPCI Paris, PSL University, CNRS, Langevin Institute, Paris, France
| | - Keyi Fei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | | | - Jin Yuan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Jinsui Road 7, Guangzhou 510060, Guangdong, China
| | - Peng Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Jinsui Road 7, Guangzhou 510060, Guangdong, China
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12
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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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13
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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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14
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Tan Z, Chen X, Li K, Liu Y, Cao H, Li J, Jhanji V, Zou H, Liu F, Wang R, Wang Y. Artificial Intelligence-Based Diagnostic Model for Detecting Keratoconus Using Videos of Corneal Force Deformation. Transl Vis Sci Technol 2022; 11:32. [PMID: 36178782 PMCID: PMC9527334 DOI: 10.1167/tvst.11.9.32] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Purpose To develop a novel method based on biomechanical parameters calculated from raw corneal dynamic deformation videos to quickly and accurately diagnose keratoconus using machine learning. Methods The keratoconus group was included according to Rabinowitz's criteria, and the normal group included corneal refractive surgery candidates. Independent biomechanical parameters were calculated from dynamic corneal deformation videos. A novel neural network model was trained to diagnose keratoconus. Tenfold cross-validation was performed, and the sample set was divided into a training set for training, a validation set for parameter validation, and a testing set for performance evaluation. External validation was performed to evaluate the model's generalizability. Results A novel intelligent diagnostic model for keratoconus based on a five-layer feedforward network was constructed by calculating four biomechanical characteristics, including time of the first applanation, deformation amplitude at the highest concavity, central corneal thickness, and radius at the highest concavity. The model was able to diagnose keratoconus with 99.6% accuracy, 99.3% sensitivity, 100% specificity, and 100% precision in the sample set (n = 276), and it achieved an accuracy of 98.7%, sensitivity of 97.4%, specificity of 100%, and precision of 100% in the external validation set (n = 78). Conclusions In the absence of corneal topographic examination, rapid and accurate diagnosis of keratoconus is possible with the aid of machine learning. Our study provides a new potential approach and sheds light on the diagnosis of keratoconus from a purely corneal biomechanical perspective. Translational Relevance Our findings could help improve the diagnosis of keratoconus based on corneal biomechanical properties.
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Affiliation(s)
- Zuoping Tan
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Xuan Chen
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Kangsheng Li
- Tianjin University of Technology, Tianjin, China
| | - Yan Liu
- Tianjin University of Technology, Tianjin, China
| | - Huazheng Cao
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Jing Li
- Shanxi Eye Hospital, Xi'an People's Hospital, Xi'an, Shanxi, China
| | - Vishal Jhanji
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Haohan Zou
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
| | - Fenglian Liu
- Tianjin University of Technology, Tianjin, China
| | - Riwei Wang
- Wenzhou University of Technology, Wenzhou, Zhejiang, China
| | - Yan Wang
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China.,Tianjin Eye Hospital, Tianjin Eye Institute, Tianjin Key Laboratory of Ophthalmology and Visual Science, Nankai University Affiliated Eye Hospital, Tianjin, China.,https://orcid.org/0000-0002-1257-6635
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15
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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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16
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Lu NJ, Elsheikh A, Rozema JJ, Hafezi N, Aslanides IM, Hillen M, Eckert D, Funck C, Koppen C, Cui LL, Hafezi F. Combining Spectral-Domain OCT and Air-Puff Tonometry Analysis to Diagnose Keratoconus. J Refract Surg 2022; 38:374-380. [PMID: 35686708 DOI: 10.3928/1081597x-20220414-02] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
PURPOSE To investigate the diagnostic capacity of spectral-domain optical coherence tomography (SD-OCT) combined with air-puff tonometry using artificial intelligence (AI) in differentiating between normal and keratoconic eyes. METHODS Patients who had either undergone uneventful laser vision correction with at least 3 years of stable follow-up or those who had forme fruste keratoconus (FFKC), early keratoconus (EKC), or advanced keratoconus (AKC) were included. SD-OCT and biomechanical information from air-puff tonometry was divided into training and validation sets. AI models based on random forest or neural networks were trained to distinguish eyes with FFKC from normal eyes. Model accuracy was independently tested in eyes with FFKC and normal eyes. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity values. RESULTS A total of 223 normal eyes from 223 patients, 69 FFKC eyes from 69 patients, 72 EKC eyes from 72 patients, and 258 AKC eyes from 258 patients were included. The top AUC ROC values (normal eyes compared with AKC and EKC) were Pentacam Random Forest Index (AUC = 0.985 and 0.958), Tomographic and Biomechanical Index (AUC = 0.983 and 0.925), and Belin-Ambrósio Enhanced Ectasia Total Deviation Index (AUC = 0.981 and 0.922). When SD-OCT and air-puff tonometry data were combined, the random forest AI model provided the highest accuracy with 99% AUC for FFKC (75% sensitivity; 94.74% specificity). CONCLUSIONS Currently, AI parameters accurately diagnose AKC and EKC, but have a limited ability to diagnose FFKC. AI-assisted diagnostic technology that uses both SD-OCT and air-puff tonometry may overcome this limitation, leading to improved treatment of patients with keratoconus. [J Refract Surg. 2022;38(6):374-380.].
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17
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Keratoconus Classification with Convolutional Neural Networks Using Segmentation and Index Quantification of Eye Topography Images by Particle Swarm Optimisation. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8119685. [PMID: 35360512 PMCID: PMC8964157 DOI: 10.1155/2022/8119685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 11/17/2022]
Abstract
In keratoconus, the cornea assumes a conical shape due to its thinning and protrusion. Early detection of keratoconus is vital in preventing vision loss or costly repairs. In corneal topography maps, curvature and steepness can be distinguished by the colour scales, with warm colours representing curved steep areas and cold colours representing flat areas. With the advent of machine learning algorithms like convolutional neural networks (CNN), the identification and classification of keratoconus from these topography maps have been made faster and more accurate. The classification and grading of keratoconus depend on the colour scales used. Artefacts and minimal variations in the corneal shape, in mild or developing keratoconus, are not represented clearly in the image gradients. Segmentation of the maps needs to be carried out for identifying the severity of the keratoconus as well as for identifying the changes in the severity. In this paper, we are considering the use of particle swarm optimisation and its modifications for segmenting the topography image. Pretrained CNN models are then trained with the dataset and tested. Results show that the performance of the system in terms of accuracy is 95.9% compared to 93%, 95.3%, and 84% available in the literature for a 3-class classification that involved mild keratoconus or forme fruste keratoconus.
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18
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Cao K, Verspoor K, Sahebjada S, Baird PN. Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis. J Clin Med 2022; 11:jcm11030478. [PMID: 35159930 PMCID: PMC8836961 DOI: 10.3390/jcm11030478] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 12/26/2022] Open
Abstract
(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.
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Affiliation(s)
- Ke Cao
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia; (K.C.); (S.S.)
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia;
- School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Srujana Sahebjada
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia; (K.C.); (S.S.)
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia
| | - Paul N. Baird
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, VIC 3002, Australia
- Correspondence: ; Tel.: +61-3-9929-8613
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Wang M, Shi C, Zhou Y, Ye Y, Fan X, Huang H, Yu X, Lu F, Shen M. The Location Consistency Index Helps to Distinguish Eyes With Subclinical Keratoconus From Normal Eyes. J Refract Surg 2022; 38:35-42. [PMID: 35020538 DOI: 10.3928/1081597x-20211111-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
PURPOSE To develop a novel index that combines the locations and magnitudes of corneal alterations to improve discrimination of eyes with subclinical keratoconus from normal eyes. METHODS A Scheimpflug-based tomography system was used to image 252 eyes (normal: 78 eyes, subclinical keratoconus: 71 eyes, and keratoconus: 103 eyes) of 252 patients from two clinical centers. Coordinates and magnitudes of the maximum corneal protrusion alterations were extracted from curvature, elevation, and pachymetry maps. A location consistency index (LCI) was calculated from the Euclidean distances among these locations. A logistic regression model, named the location consistency enhanced score (LCES), which combined the LCI and the magnitudes of these maximum alterations, was trained and tested in two different datasets. RESULTS The LCI in eyes with subclinical keratoconus was 7.8 ± 2.6 µm, which was significantly different from that in normal eyes (11.8 ± 3.9 µm) and eyes with keratoconus (5.8 ± 2.4 µm) (all P < .001). The LCI could differentiate eyes with subclinical keratoconus from normal eyes with a sensitivity of 67.6%, specificity of 83.3%, and area under the receiver operating characteristic curve (AUC) of 0.81. Combining the magnitudes of these maximum alterations with the LCI for the LCES yielded a sensitivity of 90.0% and a specificity of 74.4% for differentiating eyes with subclinical keratoconus from normal eyes (AUC: 0.91). CONCLUSIONS The LCI can assist in differentiating eyes with subclinical keratoconus from normal eyes. The LCES is a potential new index to assist in a confirmatory test of eyes with subclinical keratoconus. [J Refract Surg. 2022;38(1):35-42.].
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Santodomingo-Rubido J, Carracedo G, Suzaki A, Villa-Collar C, Vincent SJ, Wolffsohn JS. Keratoconus: An updated review. Cont Lens Anterior Eye 2022; 45:101559. [PMID: 34991971 DOI: 10.1016/j.clae.2021.101559] [Citation(s) in RCA: 156] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/23/2021] [Accepted: 12/12/2021] [Indexed: 02/06/2023]
Abstract
Keratoconus is a bilateral and asymmetric disease which results in progressive thinning and steeping of the cornea leading to irregular astigmatism and decreased visual acuity. Traditionally, the condition has been described as a noninflammatory disease; however, more recently it has been associated with ocular inflammation. Keratoconus normally develops in the second and third decades of life and progresses until the fourth decade. The condition affects all ethnicities and both sexes. The prevalence and incidence rates of keratoconus have been estimated to be between 0.2 and 4,790 per 100,000 persons and 1.5 and 25 cases per 100,000 persons/year, respectively, with highest rates typically occurring in 20- to 30-year-olds and Middle Eastern and Asian ethnicities. Progressive stromal thinning, rupture of the anterior limiting membrane, and subsequent ectasia of the central/paracentral cornea are the most commonly observed histopathological findings. A family history of keratoconus, eye rubbing, eczema, asthma, and allergy are risk factors for developing keratoconus. Detecting keratoconus in its earliest stages remains a challenge. Corneal topography is the primary diagnostic tool for keratoconus detection. In incipient cases, however, the use of a single parameter to diagnose keratoconus is insufficient, and in addition to corneal topography, corneal pachymetry and higher order aberration data are now commonly used. Keratoconus severity and progression may be classified based on morphological features and disease evolution, ocular signs, and index-based systems. Keratoconus treatment varies depending on disease severity and progression. Mild cases are typically treated with spectacles, moderate cases with contact lenses, while severe cases that cannot be managed with scleral contact lenses may require corneal surgery. Mild to moderate cases of progressive keratoconus may also be treated surgically, most commonly with corneal cross-linking. This article provides an updated review on the definition, epidemiology, histopathology, aetiology and pathogenesis, clinical features, detection, classification, and management and treatment strategies for keratoconus.
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Affiliation(s)
| | - Gonzalo Carracedo
- Department of Optometry and Vision, Faculty of Optics and Optometry, Universidad Complutense de Madrid, Madrid, Spain
| | - Asaki Suzaki
- Clinical Research and Development Center, Menicon Co., Ltd., Nagoya, Japan
| | - Cesar Villa-Collar
- Department of Pharmacy, Biotechnology, Nutrition, Optics and Optometry, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain
| | - Stephen J Vincent
- Contact Lens and Visual Optics Laboratory, School of Optometry and Vision Science, Centre for Vision and Eye Research, Queensland University of Technology, Brisbane, Australia
| | - James S Wolffsohn
- School of optometry, Health and Life Sciences, Aston University, Birmingham B4 7ET, United Kingdom
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Maile H, Li JPO, Gore D, Leucci M, Mulholland P, Hau S, Szabo A, Moghul I, Balaskas K, Fujinami K, Hysi P, Davidson A, Liskova P, Hardcastle A, Tuft S, Pontikos N. Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review. JMIR Med Inform 2021; 9:e27363. [PMID: 34898463 PMCID: PMC8713097 DOI: 10.2196/27363] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/10/2021] [Accepted: 10/14/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements. OBJECTIVE The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions. METHODS For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. RESULTS We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study. CONCLUSIONS Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.
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Affiliation(s)
- Howard Maile
- UCL Institute of Ophthalmology, University College London, London, United Kingdom
| | | | - Daniel Gore
- Moorfields Eye Hospital, London, United Kingdom
| | | | - Padraig Mulholland
- UCL Institute of Ophthalmology, University College London, London, United Kingdom.,Moorfields Eye Hospital, London, United Kingdom.,Centre for Optometry & Vision Science, Biomedical Sciences Research Institute, Ulster University, Coleraine, United Kingdom
| | - Scott Hau
- Moorfields Eye Hospital, London, United Kingdom
| | - Anita Szabo
- UCL Institute of Ophthalmology, University College London, London, United Kingdom
| | | | | | - Kaoru Fujinami
- UCL Institute of Ophthalmology, University College London, London, United Kingdom.,Moorfields Eye Hospital, London, United Kingdom.,Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan.,Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan
| | - Pirro Hysi
- Section of Ophthalmology, School of Life Course Sciences, King's College London, London, United Kingdom.,Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Alice Davidson
- UCL Institute of Ophthalmology, University College London, London, United Kingdom
| | - Petra Liskova
- Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.,Department of Ophthalmology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Alison Hardcastle
- UCL Institute of Ophthalmology, University College London, London, United Kingdom
| | - Stephen Tuft
- UCL Institute of Ophthalmology, University College London, London, United Kingdom.,Moorfields Eye Hospital, London, United Kingdom
| | - Nikolas Pontikos
- UCL Institute of Ophthalmology, University College London, London, United Kingdom.,Moorfields Eye Hospital, London, United Kingdom
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22
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Cao K, Verspoor K, Chan E, Daniell M, Sahebjada S, Baird PN. Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus. Comput Biol Med 2021; 138:104884. [PMID: 34607273 DOI: 10.1016/j.compbiomed.2021.104884] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/15/2021] [Accepted: 09/19/2021] [Indexed: 12/26/2022]
Abstract
PURPOSE To investigate the performance of a machine learning model based on a reduced dimensionality parameter space derived from complete Pentacam parameters to identify subclinical keratoconus (KC). METHODS All 1692 available parameters were obtained from the Pentacam imaging machine on 145 subclinical KC and 122 control eyes. We applied a principal component analysis (PCA) to the complete Pentacam dataset to reduce its parameter dimensionality. Subsequently, we investigated machine learning performance of the random forest algorithm with increasing numbers of components to identify their optimal number for detecting subclinical KC from control eyes. RESULTS The dimensionality of the complete set of 1692 Pentacam parameters was reduced to 267 principal components using PCA. Subsequent selection of 15 of these principal components explained over 85% of the variance of the original Pentacam-derived parameters and input to train a random forest machine learning model to achieve the best accuracy of 98% in detecting subclinical KC eyes. The model established also reached a high sensitivity of 97% in identification of subclinical KC and a specificity of 98% in recognizing control eyes. CONCLUSIONS A random forest-based model trained using a modest number of components derived from a reduced dimensionality representation of complete Pentacam system parameters allowed for high accuracy of subclinical KC identification.
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Affiliation(s)
- Ke Cao
- Centre for Eye Research Australia, Melbourne, Victoria, Australia; Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Australia; School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
| | - Elsie Chan
- Centre for Eye Research Australia, Melbourne, Victoria, Australia; Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, Victoria, Australia; Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia
| | - Mark Daniell
- Centre for Eye Research Australia, Melbourne, Victoria, Australia; Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, Victoria, Australia; Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia
| | - Srujana Sahebjada
- Centre for Eye Research Australia, Melbourne, Victoria, Australia; Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Paul N Baird
- Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, Victoria, Australia.
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23
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Wu D, Lim DKA, Lim BXH, Wong N, Hafezi F, Manotosh R, Lim CHL. Corneal Cross-Linking: The Evolution of Treatment for Corneal Diseases. Front Pharmacol 2021; 12:686630. [PMID: 34349648 PMCID: PMC8326410 DOI: 10.3389/fphar.2021.686630] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/05/2021] [Indexed: 01/31/2023] Open
Abstract
Corneal cross-linking (CXL) using riboflavin and ultraviolet A (UVA) light has become a useful treatment option for not only corneal ectasias, such as keratoconus, but also a number of other corneal diseases. Riboflavin is a photoactivated chromophore that plays an integral role in facilitating collagen crosslinking. Modifications to its formulation and administration have been proposed to overcome shortcomings of the original epithelium-off Dresden CXL protocol and increase its applicability across various clinical scenarios. Hypoosmolar riboflavin formulations have been used to artificially thicken thin corneas prior to cross-linking to mitigate safety concerns regarding the corneal endothelium, whereas hyperosmolar formulations have been used to reduce corneal oedema when treating bullous keratopathy. Transepithelial protocols incorporate supplementary topical medications such as tetracaine, benzalkonium chloride, ethylenediaminetetraacetic acid and trometamol to disrupt the corneal epithelium and improve corneal penetration of riboflavin. Further assistive techniques include use of iontophoresis and other wearable adjuncts to facilitate epithelium-on riboflavin administration. Recent advances include, Photoactivated Chromophore for Keratitis-Corneal Cross-linking (PACK-CXL) for treatment of infectious keratitis, customised protocols (CurV) utilising riboflavin coupled with customised UVA shapes to induce targeted stiffening have further induced interest in the field. This review aims to examine the latest advances in riboflavin and UVA administration, and their efficacy and safety in treating a range of corneal diseases. With such diverse riboflavin delivery options, CXL is well primed to complement the armamentarium of therapeutic options available for the treatment of a variety of corneal diseases.
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Affiliation(s)
- Duoduo Wu
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dawn Ka-Ann Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Ophthalmology, National University Health System, Singapore, Singapore
| | - Blanche Xiao Hong Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Ophthalmology, National University Health System, Singapore, Singapore
| | - Nathan Wong
- Royal Victorian Eye Hospital, Melbourne, VIC, Australia
| | - Farhad Hafezi
- Ocular Cell Biology Group, Center for Applied Biotechnology and Molecular Medicine, University of Zurich, Zurich, Switzerland.,ELZA Institute, Dietikon, Switzerland.,Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Ophthalmology, USC Roski Eye Institute, Los Angeles, CA, United States.,Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Ray Manotosh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Ophthalmology, National University Health System, Singapore, Singapore
| | - Chris Hong Long Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Ophthalmology, National University Health System, Singapore, Singapore.,Singapore Eye Research Institute, Singapore, Singapore.,School of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia
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24
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Atalay E, Özalp O, Yıldırım N. Advances in the diagnosis and treatment of keratoconus. Ther Adv Ophthalmol 2021; 13:25158414211012796. [PMID: 34263132 PMCID: PMC8246497 DOI: 10.1177/25158414211012796] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 04/07/2021] [Indexed: 01/31/2023] Open
Abstract
Keratoconus had traditionally been considered a rare disease at a time when the imaging technology was inept in detecting subtle manifestations, resulting in more severe disease at presentation. The increased demand for refractive surgery in recent years also made it essential to more effectively detect keratoconus before attempting any ablative procedure. Consequently, the armamentarium of tools that can be used to diagnose and treat keratoconus has significantly expanded. The advances in imaging technology have allowed clinicians and researchers alike to visualize the cornea layer by layer looking for any early changes that might be indicative of keratoconus. In addition to the conventional geometrical evaluation, efforts are also underway to enable spatially resolved corneal biomechanical evaluation. Artificial intelligence has been exploited in a multitude of ways to enhance diagnostic efficiency and to guide treatment. As for treatment, corneal cross-linking treatment remains the mainstay preventive approach, yet the current main focus of research is on increasing oxygen availability and developing new strategies to improve riboflavin permeability during the procedure. Some new combined protocols are being proposed to simultaneously halt keratoconus progression and correct refractive error. Bowman layer transplantation and additive keratoplasty are newly emerging alternatives to conventional keratoplasty techniques that are used in keratoconus surgery. Advances in tissue engineering and regenerative therapy might bring new perspectives for treatment at the cellular level and hence obviate the need for invasive surgeries. In this review, we describe the advances in the diagnosis and treatment of keratoconus primarily focusing on newly emerging approaches and strategies.
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Affiliation(s)
- Eray Atalay
- Department of Ophthalmology, Medical School, Eskişehir Osmangazi University, Meşelik Kampüsü, Odunpazarı, Eskişehir 26040, Turkey
| | - Onur Özalp
- Department of Ophthalmology, Medical School, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Nilgün Yıldırım
- Department of Ophthalmology, Medical School, Eskişehir Osmangazi University, Eskişehir, Turkey
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25
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Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model. PHOTONICS 2021. [DOI: 10.3390/photonics8040118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Machine learning (ML) has an impressive capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied where three approaches of ML were explored. Once all images were analyzed, representative areas from every digital image were also extracted, processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning—support vector machine (TL-SVM) (AUC = 0.94, SPE 88%, SEN 100%) and transfer learning—random forest (TL- RF) method (AUC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUC = 0.84, SPE 77%, SEN 91%) and random forest (AUC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas using a small sample.
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26
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Jayadev C, Shetty R. Artificial intelligence in laser refractive surgery - Potential and promise! Indian J Ophthalmol 2020; 68:2650-2651. [PMID: 33229635 PMCID: PMC7856980 DOI: 10.4103/ijo.ijo_3304_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chaitra Jayadev
- Narayana Nethralaya Eye Institute, 121/C, Chord Road, Rajajinagar, Bangalore - 560 010, Karnataka, India
| | - Rohit Shetty
- Narayana Nethralaya Eye Institute, 121/C, Chord Road, Rajajinagar, Bangalore - 560 010, Karnataka, India
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