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Muhsin ZJ, Qahwaji R, AlShawabkeh M, AlRyalat SA, Al Bdour M, Al-Taee M. Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices. EYE AND VISION (LONDON, ENGLAND) 2024; 11:28. [PMID: 38978067 PMCID: PMC11229244 DOI: 10.1186/s40662-024-00394-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/17/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements. METHODS A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience. RESULTS The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality. CONCLUSION The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.
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Affiliation(s)
- Zahra J Muhsin
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, UK.
| | - Rami Qahwaji
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, UK
| | | | | | - Muawyah Al Bdour
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Majid Al-Taee
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, UK
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Goodman D, Zhu AY. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1380701. [PMID: 38984114 PMCID: PMC11182163 DOI: 10.3389/fopht.2024.1380701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/11/2024]
Abstract
Introduction The application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias. Methods We conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings. Results Ninety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus. Discussion Algorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.
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Tey KY, Cheong EZK, Ang M. Potential applications of artificial intelligence in image analysis in cornea diseases: a review. EYE AND VISION (LONDON, ENGLAND) 2024; 11:10. [PMID: 38448961 PMCID: PMC10919022 DOI: 10.1186/s40662-024-00376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/09/2024] [Indexed: 03/08/2024]
Abstract
Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology-primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.
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Affiliation(s)
- Kai Yuan Tey
- Singapore National Eye Centre, 11 Third Hospital Ave, Singapore, 168751, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | | | - Marcus Ang
- Singapore National Eye Centre, 11 Third Hospital Ave, Singapore, 168751, Singapore.
- Singapore Eye Research Institute, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
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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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Deshmukh R, Ong ZZ, Rampat R, Alió del Barrio JL, Barua A, Ang M, Mehta JS, Said DG, Dua HS, Ambrósio R, Ting DSJ. Management of keratoconus: an updated review. Front Med (Lausanne) 2023; 10:1212314. [PMID: 37409272 PMCID: PMC10318194 DOI: 10.3389/fmed.2023.1212314] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Keratoconus is the most common corneal ectatic disorder. It is characterized by progressive corneal thinning with resultant irregular astigmatism and myopia. Its prevalence has been estimated at 1:375 to 1:2,000 people globally, with a considerably higher rate in the younger populations. Over the past two decades, there was a paradigm shift in the management of keratoconus. The treatment has expanded significantly from conservative management (e.g., spectacles and contact lenses wear) and penetrating keratoplasty to many other therapeutic and refractive modalities, including corneal cross-linking (with various protocols/techniques), combined CXL-keratorefractive surgeries, intracorneal ring segments, anterior lamellar keratoplasty, and more recently, Bowman's layer transplantation, stromal keratophakia, and stromal regeneration. Several recent large genome-wide association studies (GWAS) have identified important genetic mutations relevant to keratoconus, facilitating the development of potential gene therapy targeting keratoconus and halting the disease progression. In addition, attempts have been made to leverage the power of artificial intelligence-assisted algorithms in enabling earlier detection and progression prediction in keratoconus. In this review, we provide a comprehensive overview of the current and emerging treatment of keratoconus and propose a treatment algorithm for systematically guiding the management of this common clinical entity.
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Affiliation(s)
- Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Zun Zheng Ong
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, United Kingdom
| | - Radhika Rampat
- Department of Ophthalmology, Royal Free London NHS Foundation Trust, London, United Kingdom
| | - 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
| | - Ankur Barua
- Birmingham and Midland Eye Centre, Birmingham, United Kingdom
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Jodhbir S. Mehta
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Dalia G. Said
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, United Kingdom
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Harminder S. Dua
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, United Kingdom
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Renato Ambrósio
- Department of Cornea and Refractive Surgery, Instituto de Olhos Renato Ambrósio, Rio de Janeiro, Brazil
- Department of Ophthalmology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil
- Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - Darren Shu Jeng Ting
- Birmingham and Midland Eye Centre, Birmingham, United Kingdom
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
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Kundu G, Shetty N, Shetty R, Khamar P, D'Souza S, Meda TR, Nuijts RMMA, Narasimhan R, Roy AS. Artificial intelligence-based stratification of demographic, ocular surface high-risk factors in progression of keratoconus. Indian J Ophthalmol 2023; 71:1882-1888. [PMID: 37203049 PMCID: PMC10391495 DOI: 10.4103/ijo.ijo_2651_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023] Open
Abstract
Purpose The purpose of this study was to identify and analyze the clinical and ocular surface risk factors influencing the progression of keratoconus (KC) using an artificial intelligence (AI) model. Methods This was a prospective analysis in which 450 KC patients were included. We used the random forest (RF) classifier model from our previous study (which evaluated longitudinal changes in tomographic parameters to predict "progression" and "no progression") to classify these patients. Clinical and ocular surface risk factors were determined through a questionnaire, which included presence of eye rubbing, duration of indoor activity, usage of lubricants and immunomodulator topical medications, duration of computer use, hormonal disturbances, use of hand sanitizers, immunoglobulin E (IgE), and vitamins D and B12 from blood investigations. An AI model was then built to assess whether these risk factors were linked to the future progression versus no progression of KC. The area under the curve (AUC) and other metrics were evaluated. Results The tomographic AI model classified 322 eyes as progression and 128 eyes as no progression. Also, 76% of the cases that were classified as progression (from tomographic changes) were correctly predicted as progression and 67% of cases that were classified as no progression were predicted as no progression based on clinical risk factors at the first visit. IgE had the highest information gain, followed by presence of systemic allergies, vitamin D, and eye rubbing. The clinical risk factors AI model achieved an AUC of 0.812. Conclusion This study demonstrated the importance of using AI for risk stratification and profiling of patients based on clinical risk factors, which could impact the progression in KC eyes and help manage them better.
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Affiliation(s)
- Gairik Kundu
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, India
| | - Naren Shetty
- Department of Cataract and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, India
| | - Rohit Shetty
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, India
| | - Pooja Khamar
- Department of Cataract and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, India
| | - Sharon D'Souza
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, India
| | - Tulasi R Meda
- Department of Cataract and Refractive Surgery, Narayana Nethralaya, Bengaluru, Karnataka, India
| | - Rudy M M A Nuijts
- Department of Ophthalmology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Raghav Narasimhan
- Imaging, Biomechanics and Mathematical Modeling Solutions, Narayana Nethralaya Foundation, Bengaluru, Karnataka, India
| | - Abhijit Sinha Roy
- Imaging, Biomechanics and Mathematical Modeling Solutions, Narayana Nethralaya Foundation, Bengaluru, Karnataka, India
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Kundu G, Shetty R, D’Souza S, Khamar P, Nuijts RMMA, Sethu S, Roy AS. A novel combination of corneal confocal microscopy, clinical features and artificial intelligence for evaluation of ocular surface pain. PLoS One 2022; 17:e0277086. [PMID: 36318586 PMCID: PMC9624399 DOI: 10.1371/journal.pone.0277086] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
OBJECTIVES To analyse various corneal nerve parameters using confocal microscopy along with systemic and orthoptic parameters in patients presenting with ocular surface pain using a random forest artificial intelligence (AI) model. DESIGN Observational, cross-sectional. METHODS Two hundred forty eyes of 120 patients with primary symptom of ocular surface pain or discomfort and control group of 60 eyes of 31 patients with no symptoms of ocular pain were analysed. A detailed ocular examination included visual acuity, refraction, slit-lamp and fundus. All eyes underwent laser scanning confocal microscopy (Heidelberg Engineering, Germany) and their nerve parameters were evaluated. The presence or absence of orthoptic issues and connective tissue disorders were included in the AI. The eyes were grouped as those (Group 1) with symptom grade higher than signs, (Group 2) with similar grades of symptoms and signs, (Group3) without symptoms but with signs, (Group 4) without symptoms and signs. The area under curve (AUC), accuracy, recall, precision and F1-score were evaluated. RESULTS Over all, the AI achieved an AUC of 0.736, accuracy of 86%, F1-score of 85.9%, precision of 85.6% and recall of 86.3%. The accuracy was the highest for Group 2 and least for Group 3 eyes. The top 6 parameters used for classification by the AI were microneuromas, immature and mature dendritic cells, presence of orthoptic issues and nerve fractal dimension parameter. CONCLUSIONS This study demonstrated that various corneal nerve parameters, presence or absence of systemic and orthoptic issues coupled with AI can be a useful technique to understand and correlate the various clinical and imaging parameters of ocular surface pain.
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Affiliation(s)
- Gairik Kundu
- Department of Cornea and Refractive surgery, Narayana Nethralaya, Bangalore, India
- * E-mail:
| | - Rohit Shetty
- Department of Cornea and Refractive surgery, Narayana Nethralaya, Bangalore, India
| | - Sharon D’Souza
- Department of Cornea and Refractive surgery, Narayana Nethralaya, Bangalore, India
| | - Pooja Khamar
- Department of Cataract and Refractive surgery, Narayana Nethralaya, Bangalore, India
| | - Rudy M. M. A. Nuijts
- Department of Ophthalmology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Swaminathan Sethu
- GROW Research Laboratory, Narayana Nethralaya Foundation, Bangalore, India
| | - Abhijit Sinha Roy
- Imaging, Biomechanics and Mathematical Modeling Solutions, Narayana Nethralaya Foundation, Bangalore, India
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Angelo L, Gokul Boptom A, McGhee C, Ziaei M. Corneal Crosslinking: Present and Future. Asia Pac J Ophthalmol (Phila) 2022; 11:441-452. [PMID: 36094381 DOI: 10.1097/apo.0000000000000557] [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] [Received: 03/27/2022] [Accepted: 07/08/2022] [Indexed: 11/25/2022] Open
Abstract
Keratoconus is a progressive corneal thinning disorder that can lead to vision loss. In the last 2 decades, corneal crosslinking (CXL) has emerged as an effective method to halt the progression of keratoconus and reduce the number of patients requiring keratoplasty. The procedure has been adopted globally and has evolved to become a part of combination treatments to regularize the cornea and improve visual outcomes. CXL has even been extrapolated in managing other ocular pathologies such as progressive myopia, infectious keratitis, and bullous keratopathy. This review aims to summarize the current role of CXL in keratoconus and its alternative uses, and provide insights into future developments in this fast-developing field.
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Affiliation(s)
- Lize Angelo
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
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Rahman L, Hafejee A, Anantharanjit R, Wei W, Cordeiro MF. Accelerating precision ophthalmology: recent advances. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2022. [DOI: 10.1080/23808993.2022.2154146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Loay Rahman
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Ammaarah Hafejee
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Rajeevan Anantharanjit
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Wei Wei
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
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