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Jiang Y, Jiang H, Zhang J, Chen T, Li Y, Zhou Y, Chen Y, Li F. The development of a machine learning model to train junior ophthalmologists in diagnosing the pre-clinical keratoconus. Front Med (Lausanne) 2024; 11:1458356. [PMID: 39359918 PMCID: PMC11445185 DOI: 10.3389/fmed.2024.1458356] [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: 07/02/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024] Open
Abstract
Purpose This study aims to evaluate the diagnostic performance of a machine learning model (ML model) to train junior ophthalmologists in detecting preclinical keratoconus (PKC). Methods A total of 1,334 corneal topography images (The Pentacam HR system) from 413 keratoconus eyes, 32 PKC eyes and 222 normal eyes were collected. Five junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the ML model. The diagnostic performance of PKC was evaluated among three groups: junior ophthalmologist group (control group), ML model group and ML model-training junior ophthalmologist group (test group). Results The accuracy of the ML model between the eyes of patients with KC and NEs in all three clinics (99% accuracy, area under the receiver operating characteristic (ROC) curve AUC of 1.00, 99% sensitivity, 99% specificity) was higher than that for Belin-Ambrósio enhanced ectasia display total deviation (BAD-D) (86% accuracy, AUC of 0.97, 97% sensitivity, 69% specificity). The accuracy of the ML model between eyes with PKC and NEs in all three clinics (98% accuracy, AUC of 0.96, 98% sensitivity, 98% specificity) was higher than that of BAD-D (69% accuracy, AUC of 0.73, 67% sensitivity, 69% specificity). The diagnostic accuracy of PKC was 47.5% (95%CI, 0.5-71.6%), 100% (95%CI, 100-100%) and 94.4% (95%CI, 14.7-94.7%) in the control group, ML model group and test group. With the assistance of the proposed ML model, the diagnostic accuracy of junior ophthalmologists improved with statistical significance (p < 0.05). According to the questionnaire of all the junior ophthalmologists, the average score was 4 (total 5) regarding to the comprehensiveness that the AI model has been in their keratoconus diagnosis learning; the average score was 4.4 (total 5) regarding to the convenience that the AI model has been in their keratoconus diagnosis learning. Conclusion The proposed ML model provided a novel approach for the detection of PKC with high diagnostic accuracy and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses.
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Affiliation(s)
- Yang Jiang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Hanyu Jiang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- Eight-Year Medical Doctor Program, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Zhang
- Department of Ophthalmology, Beijing Ming Vision Clinic, Beijing, China
| | - Tao Chen
- Department of Ophthalmology, Beijing Fenglian Jiayue Lege Clinic, Beijing, China
| | - Ying Li
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuehua Zhou
- Department of Ophthalmology, Beijing Ming Vision Clinic, Beijing, China
| | - Youxin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Fusheng Li
- Department of Ophthalmology, Beijing Ming Vision Clinic, Beijing, China
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Gurnani B, Kaur K, Lalgudi VG, Kundu G, Mimouni M, Liu H, Jhanji V, Prakash G, Roy AS, Shetty R, Gurav JS. Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review. J Fr Ophtalmol 2024; 47:104242. [PMID: 39013268 DOI: 10.1016/j.jfo.2024.104242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024]
Abstract
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
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Affiliation(s)
- B Gurnani
- Department of Cataract, Cornea, External Disease, Trauma, Ocular Surface and Refractive Surgery, ASG Eye Hospital, Jodhpur, Rajasthan, India.
| | - K Kaur
- Department of Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, Rajasthan, India
| | - V G Lalgudi
- Department of Cornea, Refractive surgery, Ira G Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, USA
| | - G Kundu
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - M Mimouni
- Department of Ophthalmology, Rambam Health Care Campus affiliated with the Bruce and Ruth Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - H Liu
- Department of Ophthalmology, University of Ottawa Eye Institute, Ottawa, Canada
| | - V Jhanji
- UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - G Prakash
- Department of Ophthalmology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - A S Roy
- Narayana Nethralaya Foundation, Bangalore, India
| | - R Shetty
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - J S Gurav
- Department of Opthalmology, Armed Forces Medical College, Pune, India
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Lavric A, Beguni C, Zadobrischi E, Căilean AM, Avătămăniței SA. A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations. SENSORS (BASEL, SWITZERLAND) 2024; 24:4834. [PMID: 39123881 PMCID: PMC11314945 DOI: 10.3390/s24154834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/01/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024]
Abstract
In the context in which severe visual impairment significantly affects human life, this article emphasizes the potential of Artificial Intelligence (AI) and Visible Light Communications (VLC) in developing future assistive technologies. Toward this path, the article summarizes the features of some commercial assistance solutions, and debates the characteristics of VLC and AI, emphasizing their compatibility with blind individuals' needs. Additionally, this work highlights the AI potential in the efficient early detection of eye diseases. This article also reviews the existing work oriented toward VLC integration in blind persons' assistive applications, showing the existing progress and emphasizing the high potential associated with VLC use. In the end, this work provides a roadmap toward the development of an integrated AI-based VLC assistance solution for visually impaired people, pointing out the high potential and some of the steps to follow. As far as we know, this is the first comprehensive work which focuses on the integration of AI and VLC technologies in visually impaired persons' assistance domain.
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Affiliation(s)
- Alexandru Lavric
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
| | - Cătălin Beguni
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
| | - Eduard Zadobrischi
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
| | - Alin-Mihai Căilean
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
| | - Sebastian-Andrei Avătămăniței
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
- East European Border Scientific and Technological Park, 725500 Siret, Romania
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Bodmer NS, Christensen DG, Bachmann LM, Faes L, Sanak F, Iselin K, Kaufmann C, Thiel MA, Baenninger PB. Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis. Cornea 2024; 43:916-931. [PMID: 38300179 PMCID: PMC11142647 DOI: 10.1097/ico.0000000000003467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 10/10/2023] [Accepted: 11/26/2023] [Indexed: 02/02/2024]
Abstract
PURPOSE The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for improving the accuracy of keratoconus detection. Deep learning (DL) offers considerable promise for improving the accuracy and speed of medical imaging interpretation. We establish an inventory of studies conducted with DL algorithms that have attempted to diagnose keratoconus. METHODS This systematic review was conducted according to the recommendations of the PRISMA statement. We searched (Pre-)MEDLINE, Embase, Science Citation Index, Conference Proceedings Citation Index, arXiv document server, and Google Scholar from inception to February 18, 2022. We included studies that evaluated the performance of DL algorithms in the diagnosis of keratoconus. The main outcome was diagnostic performance measured as sensitivity and specificity, and the methodological quality of the included studies was assessed using QUADAS-2. RESULTS Searches retrieved 4100 nonduplicate records, and we included 19 studies in the qualitative synthesis and 10 studies in the exploratory meta-analysis. The overall study quality was limited because of poor reporting of patient selection and the use of inadequate reference standards. We found a pooled sensitivity of 97.5% (95% confidence interval, 93.6%-99.0%) and a pooled specificity of 97.2% (95% confidence interval, 85.7%-99.5%) for topography images as input. CONCLUSIONS Our systematic review found that the overall diagnostic performance of DL models to detect keratoconus was good, but the methodological quality of included studies was modest.
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Affiliation(s)
- Nicolas S. Bodmer
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Medignition Inc. Research Consultants Zurich, Zurich, Switzerland
- University of Toronto, Institute of Health Policy, Management and Evaluation (IHPME), Toronto, ON, Canada;
| | | | - Lucas M. Bachmann
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Medignition Inc. Research Consultants Zurich, Zurich, Switzerland
| | - Livia Faes
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Medignition Inc. Research Consultants Zurich, Zurich, Switzerland
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Frantisek Sanak
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Katja Iselin
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Claude Kaufmann
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Michael A. Thiel
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Philipp B. Baenninger
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
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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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Hartmann LM, Langhans DS, Eggarter V, Freisenich TJ, Hillenmayer A, König SF, Vounotrypidis E, Wolf A, Wertheimer CM. Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data. Transl Vis Sci Technol 2024; 13:7. [PMID: 38727695 PMCID: PMC11104256 DOI: 10.1167/tvst.13.5.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/15/2024] [Indexed: 05/22/2024] Open
Abstract
Purpose Multiple clinical visits are necessary to determine progression of keratoconus before offering corneal cross-linking. The purpose of this study was to develop a neural network that can potentially predict progression during the initial visit using tomography images and other clinical risk factors. Methods The neural network's development depended on data from 570 keratoconus eyes. During the initial visit, numerical risk factors and posterior elevation maps from Scheimpflug imaging were collected. Increase of steepest keratometry of 1 diopter during follow-up was used as the progression criterion. The data were partitioned into training, validation, and test sets. The first two were used for training, and the latter for performance statistics. The impact of individual risk factors and images was assessed using ablation studies and class activation maps. Results The most accurate prediction of progression during the initial visit was obtained by using a combination of MobileNet and a multilayer perceptron with an accuracy of 0.83. Using numerical risk factors alone resulted in an accuracy of 0.82. The use of only images had an accuracy of 0.77. The most influential risk factors in the ablation study were age and posterior elevation. The greatest activation in the class activation maps was seen at the highest posterior elevation where there was significant deviation from the best fit sphere. Conclusions The neural network has exhibited good performance in predicting potential future progression during the initial visit. Translational Relevance The developed neural network could be of clinical significance for keratoconus patients by identifying individuals at risk of progression.
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Affiliation(s)
| | | | | | | | - Anna Hillenmayer
- Department of Ophthalmology, University Hospital Ulm, Ulm, Germany
| | - Susanna F. König
- Department of Ophthalmology, University Hospital Ulm, Ulm, Germany
| | | | - Armin Wolf
- Department of Ophthalmology, University Hospital Ulm, Ulm, Germany
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Yaraghi S, Khatibi T. Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach. BMJ Open Ophthalmol 2024; 9:e001589. [PMID: 38653536 PMCID: PMC11043764 DOI: 10.1136/bmjophth-2023-001589] [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: 12/06/2023] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus. METHODS AND ANALYSIS Three pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score. RESULTS The research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually. CONCLUSION The findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases.
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Affiliation(s)
- Shokufeh Yaraghi
- Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran (the Islamic Republic of)
| | - Toktam Khatibi
- Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran (the Islamic Republic of)
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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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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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Tsatsos M, Giachos I, Tsinopoulos I, Ziakas N, Jacob S. Something to SMILE about. Is small incision lenticule extraction ready to become the gold standard in laser refractive surgery? Yes. Eye (Lond) 2024; 38:636-638. [PMID: 37731050 PMCID: PMC10920690 DOI: 10.1038/s41433-023-02745-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 07/28/2023] [Accepted: 09/11/2023] [Indexed: 09/22/2023] Open
Affiliation(s)
- M Tsatsos
- 2nd Ophthalmology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - I Giachos
- Corneal Department, Dr Agarwal's Hospital, Chennai, India
| | - I Tsinopoulos
- 2nd Ophthalmology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - N Ziakas
- 2nd Ophthalmology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - S Jacob
- Corneal Department, Dr Agarwal's Hospital, Chennai, India
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12
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Xu S, Yang X, Zhang S, Zheng X, Zheng F, Liu Y, Zhang H, Li L, Ye Q. Evaluation of the corneal topography based on deep learning. Front Med (Lausanne) 2024; 10:1264659. [PMID: 38239613 PMCID: PMC10794654 DOI: 10.3389/fmed.2023.1264659] [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: 07/21/2023] [Accepted: 12/07/2023] [Indexed: 01/22/2024] Open
Abstract
Purpose The current study designed a unique type of corneal topography evaluation method based on deep learning and traditional image processing algorithms. The type of corneal topography of patients was evaluated through the segmentation of important medical zones and the calculation of relevant medical indicators of orthokeratology (OK) lenses. Methods The clinical data of 1,302 myopic subjects was collected retrospectively. A series of neural network-based U-Net was used to segment the pupil and the treatment zone in the corneal topography, and the decentration, effective defocusing contact range, and other indicators were calculated according to the image processing algorithm. The type of corneal topography was evaluated according to the evaluation criteria given by the optometrist. Finally, the method described in this article was used to evaluate the type of corneal topography and compare it with the type classified by the optometrist. Results When the important medical zones in the corneal topography were segmented, the precision and recall of the treatment zone reached 0.9587 and 0.9459, respectively, and the precision and recall of the pupil reached 0.9771 and 0.9712. Finally, the method described in this article was used to evaluate the type of corneal topography. When the reviewed findings based on deep learning and image processing algorithms were compared to the type of corneal topography marked by the professional optometrist, they demonstrated high accuracy with more than 98%. Conclusion The current study provided an effective and accurate deep learning algorithm to evaluate the type of corneal topography. The deep learning algorithm played an auxiliary role in the OK lens fitting, which could help optometrists select the parameters of OK lenses effectively.
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Affiliation(s)
- Shuai Xu
- Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics, Nankai University, Tianjin, China
| | - Xiaoyan Yang
- Tianjin Eye Hospital, Tianjin, China
- Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin, China
- Nankai University Affiliated Eye Hospital, Tianjin, China
- Eye Hospital Optometric Center, Tianjin, China
| | - Shuxian Zhang
- Tianjin Eye Hospital, Tianjin, China
- Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin, China
- Nankai University Affiliated Eye Hospital, Tianjin, China
- Eye Hospital Optometric Center, Tianjin, China
| | - Xuan Zheng
- Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics, Nankai University, Tianjin, China
| | - Fang Zheng
- Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics, Nankai University, Tianjin, China
| | - Yin Liu
- School of Medicine, Nankai University, Tianjin, China
| | - Hanyu Zhang
- School of Medicine, Nankai University, Tianjin, China
| | - Lihua Li
- Tianjin Eye Hospital, Tianjin, China
- Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin, China
- Nankai University Affiliated Eye Hospital, Tianjin, China
- Eye Hospital Optometric Center, Tianjin, China
| | - Qing Ye
- Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics, Nankai University, Tianjin, China
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13
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Shih PJ, Shih HJ, Wang IJ, Chang SW. The extraction and application of antisymmetric characteristics of the cornea during air-puff perturbations. Comput Biol Med 2024; 168:107804. [PMID: 38070205 DOI: 10.1016/j.compbiomed.2023.107804] [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: 07/28/2023] [Revised: 11/04/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND A non-contact tonometer is used to measure intraocular pressure, and studies have primarily relied on apex displacements to assess corneal properties. However, previous studies have overlooked the asymmetric characteristics of lateral corneal perturbations, leading to a gap in understanding of the lateral mechanical properties and its application. METHOD To investigate these lateral perturbations, we designed an experiment to sequentially record the corneal profiles when two consecutive air-puffs were applied at the center of the same cornea within a short period. Moreover, we used modal decomposition to decompose anterior surface profiles into symmetric and antisymmetric modes to comprehensively analyze the asymmetric characteristics. To extract mechanical properties, we utilized high-pass frequency analysis (>250 Hz) to filter out noise and errors. RESULTS Symmetric modes between the two consecutive air-puffs exhibited major similarities during vibration; however, antisymmetric modes exhibited minor differences in lateral perturbations of asymmetric vibration. The antisymmetric modes might be related to air-puff misalignment and mechanical properties. Through applying frequency analysis, the mechanical properties could be proven at high frequencies and misalignment shown at low frequencies. Furthermore, we compared the corneal vibration profiles of 259 healthy participants and 50 patients with keratoconus. Their properties showed that the antisymmetric modes of the keratoconus group exhibited a completely opposite direction of deformation compared to that in the healthy group. CONCLUSIONS Our proposed algorithm not only extracts antisymmetric characteristics but also offers valuable insights into decompose misalignment and mechanical properties of healthy and keratoconus corneas, presenting a new perspective for corneal biomechanics.
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Affiliation(s)
- Po-Jen Shih
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
| | - Hua-Ju Shih
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan
| | - I-Jong Wang
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan
| | - Shu-Wen Chang
- Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
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14
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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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15
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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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16
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Khosravi P, Huck NA, Shahraki K, Hunter SC, Danza CN, Kim SY, Forbes BJ, Dai S, Levin AV, Binenbaum G, Chang PD, Suh DW. Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study. Int J Mol Sci 2023; 24:15105. [PMID: 37894785 PMCID: PMC10606803 DOI: 10.3390/ijms242015105] [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: 08/25/2023] [Revised: 09/29/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.
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Affiliation(s)
- Pooya Khosravi
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
| | - Nolan A. Huck
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Kourosh Shahraki
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Stephen C. Hunter
- School of Medicine, University of California, 900 University Ave, Riverside, CA 92521, USA;
| | - Clifford Neil Danza
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - So Young Kim
- Department of Ophthalmology, College of Medicine, Soonchunhyang University, Cheonan 31151, Chungcheongnam-do, Republic of Korea;
| | - Brian J. Forbes
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Shuan Dai
- Department of Ophthalmology, Queensland Children’s Hospital, South Brisbane, QLD 4101, Australia;
| | - Alex V. Levin
- Department of Ophthalmology, Flaum Eye Institute, Golisano Children’s Hospital, Rochester, NY 14642, USA;
| | - Gil Binenbaum
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Peter D. Chang
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
- Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA 92697, USA
| | - Donny W. Suh
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
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17
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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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18
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Al-Timemy AH, Alzubaidi L, Mosa ZM, Abdelmotaal H, Ghaeb NH, Lavric A, Hazarbassanov RM, Takahashi H, Gu Y, Yousefi S. A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13101689. [PMID: 37238174 DOI: 10.3390/diagnostics13101689] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/06/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97-100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91-0.92 and an accuracy range of 88-92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.
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Affiliation(s)
- Ali H Al-Timemy
- Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10011, Iraq
| | - Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Zahraa M Mosa
- Department of Physics, College of Science, Al-Nahrain University, Baghdad 64021, Iraq
| | | | - Nebras H Ghaeb
- Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10011, Iraq
| | - Alexandru Lavric
- Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
| | - Rossen M Hazarbassanov
- Medical School, Universidade Anhembi Morumbi, São Paulo 03101-001, Brazil
- Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo 04021-001, Brazil
| | - Hidenori Takahashi
- Department of Ophthalmology, Jichi Medical University, Tochigi 329-0431, Japan
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
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19
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Jiang X, Xie M, Ma L, Dong L, Li D. International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:219. [PMID: 37007552 PMCID: PMC10061466 DOI: 10.21037/atm-22-3773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/18/2022] [Indexed: 03/17/2023]
Abstract
Background The literature on artificial intelligence (AI)-related topics has been expanding rapidly over the last two decades, showing that AI is a crucial force in advancing ophthalmology. This analysis aims to provide a dynamic and longitudinal bibliometric analysis of AI-related ophthalmic papers. Methods The Web of Science was searched to retrieve papers regarding the application of AI in ophthalmology published in the English language up to May 2022. The variables were analyzed using Microsoft Excel 2019 and GraphPad Prism 9. Data visualization was performed using VOSviewer and CiteSpace. Results In this study, a total of 1,686 publications were analyzed. Recently, AI-related ophthalmology research has increased exponentially. China was the most productive country in this research field, with 483 articles, but the United States of America (446 publications) contributed most to the sum of citations and the H-index. The League of European Research Universities, Ting DSW, and Daniel SW were the most prolific institution and researchers. This field is primarily concerned with diabetic retinopathy (DR), glaucoma, optical coherence tomography, and the classification and diagnosis of fundus pictures. Current hotspots in AI research include deep learning, diagnosing and predicting systemic disorders by fundus images, incidence and progression of ocular diseases, and outcome prediction. Conclusions This analysis thoroughly reviews AI-related research in ophthalmology to help academics better comprehend the growth and possible practice consequences of AI. The association between eye and systemic biomarkers, telemedicine, real-world studies, and the development and application of new AI algorithms, such as visual converters, will continue to be research hotspots over the next few years.
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Affiliation(s)
- Xue Jiang
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Minyue Xie
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Lan Ma
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Dongmei Li
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
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Gao S, Chen Y, Shi F, Peng Y, Xu C, Chen Z, Zhu W, Xu X, Tang W, Tan Z, Xu Y, Ren Y, Zhang X, Chen X. LKG-Net: lightweight keratoconus grading network based on corneal topography. BIOMEDICAL OPTICS EXPRESS 2023; 14:799-814. [PMID: 36874500 PMCID: PMC9979687 DOI: 10.1364/boe.480564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/01/2022] [Accepted: 12/10/2022] [Indexed: 06/18/2023]
Abstract
Keratoconus (KC) is a noninflammatory ectatic disease characterized by progressive thinning and an apical cone-shaped protrusion of the cornea. In recent years, more and more researchers have been committed to automatic and semi-automatic KC detection based on corneal topography. However, there are few studies about the severity grading of KC, which is particularly important for the treatment of KC. In this work, we propose a lightweight KC grading network (LKG-Net) for 4-level KC grading (Normal, Mild, Moderate, and Severe). First of all, we use depth-wise separable convolution to design a novel feature extraction block based on the self-attention mechanism, which can not only extract rich features but also reduce feature redundancy and greatly reduce the number of parameters. Then, to improve the model performance, a multi-level feature fusion module is proposed to fuse features from the upper and lower levels to obtain more abundant and effective features. The proposed LKG-Net was evaluated on the corneal topography of 488 eyes from 281 people with 4-fold cross-validation. Compared with other state-of-the-art classification methods, the proposed method achieves 89.55% for weighted recall (W_R), 89.98% for weighted precision (W_P), 89.50% for weighted F1 score (W_F1) and 94.38% for Kappa, respectively. In addition, the LKG-Net is also evaluated on KC screening, and the experimental results show the effectiveness.
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Affiliation(s)
- Song Gao
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
- These authors contributed equally to this paper
| | - Yingjie Chen
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, China
- These authors contributed equally to this paper
| | - Fei Shi
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Yuanyuan Peng
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Chenan Xu
- State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou 215006, China
| | - Zhongyue Chen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Weifang Zhu
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Xin Xu
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Wei Tang
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Zhiwei Tan
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Yue Xu
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, China
| | - Yaru Ren
- Department of Ophthalmology, Soochow University-Affiliated First Hospital, Suzhou, Suzhou, 215006, China
| | - Xiaofeng Zhang
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, China
- Department of Ophthalmology, Soochow University-Affiliated First Hospital, Suzhou, Suzhou, 215006, China
| | - Xinjian Chen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
- State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou 215006, China
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Wang S, Ji Y, Bai W, Ji Y, Li J, Yao Y, Zhang Z, Jiang Q, Li K. Advances in artificial intelligence models and algorithms in the field of optometry. Front Cell Dev Biol 2023; 11:1170068. [PMID: 37187617 PMCID: PMC10175695 DOI: 10.3389/fcell.2023.1170068] [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: 02/27/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.
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Affiliation(s)
- Suyu Wang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yuke Ji
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wen Bai
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jiajun Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yujia Yao
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ziran Zhang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Qin Jiang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
| | - Keran Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
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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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Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022; 10:1107689. [PMID: 36605721 PMCID: PMC9808405 DOI: 10.3389/fcell.2022.1107689] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Gao HB, Pan ZG, Shen MX, Lu F, Li H, Zhang XQ. KeratoScreen: Early Keratoconus Classification With Zernike Polynomial Using Deep Learning. Cornea 2022; 41:1158-1165. [PMID: 35543584 DOI: 10.1097/ico.0000000000003038] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/16/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE We aimed to investigate the usefulness of Zernike coefficients (ZCs) for distinguishing subclinical keratoconus (KC) from normal corneas and to evaluate the goodness of detection of the entire corneal topography and tomography characteristics with ZCs as a screening feature input set of artificial neural networks. METHODS This retrospective study was conducted at the Affiliated Eye Hospital of Wenzhou Medical University, China. A total of 208 patients (1040 corneal topography images) were evaluated. Data were collected between 2012 and 2018 using the Pentacam system and analyzed from February 2019 to December 2021. An artificial neural network (KeratoScreen) was trained using a data set of ZCs generated from corneal topography and tomography. Each image was previously assigned to 3 groups: normal (70 eyes; average age, 28.7 ± 2.6 years), subclinical KC (48 eyes; average age, 24.6 ± 5.7 years), and KC (90 eyes; average age, 25.9 ± 5.4 years). The data set was randomly split into 70% for training and 30% for testing. We evaluated the precision of screening symptoms and examined the discriminative capability of several combinations of the input set and nodes. RESULTS The best results were achieved using ZCs generated from corneal thickness as an input parameter, determining the 3 categories of clinical classification for each subject. The sensitivity and precision rates were 93.9% and 96.1% in subclinical KC cases and 97.6% and 95.1% in KC cases, respectively. CONCLUSIONS Deep learning algorithms based on ZCs could be used to screen for early KC and for other corneal ectasia during preoperative screening for corneal refractive surgery.
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Affiliation(s)
- He-Bei Gao
- Division of Health Sciences, Hangzhou Normal University, Hangzhou, China
- Department of Information, Wenzhou Polytechnic, Wenzhou, China
| | - Zhi-Geng Pan
- School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, China
| | - Mei-Xiao Shen
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China ; and
| | - Fan Lu
- School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China ; and
| | - Hong Li
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Xiao-Qin Zhang
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
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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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Gairola S, Joshi P, Balasubramaniam A, Murali K, Kwatra N, Jain M. Keratoconus Classifier for Smartphone-based Corneal Topographer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1875-1878. [PMID: 36086067 DOI: 10.1109/embc48229.2022.9871744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Keratoconus is a severe eye disease that leads to deformation of the cornea. It impacts people aged 10-25 years and is the leading cause of blindness in that demography. Corneal topography is the gold standard for keratoconus diag-nosis. It is a non-invasive process performed using expensive and bulky medical devices called corneal topographers. This makes it inaccessible to large populations, especially in the Global South. Low-cost smartphone-based corneal topographers, such as SmartKC, have been proposed to make keratoconus diagnosis accessible. Similar to medical-grade topographers, SmartKC outputs curvature heatmaps and quantitative metrics that need to be evaluated by doctors for keratoconus diagnosis. An auto-matic scheme for evaluation of these heatmaps and quantitative values can play a crucial role in screening keratoconus in areas where doctors are not available. In this work, we propose a dual-head convolutional neural network (CNN) for classifying keratoconus on the heatmaps generated by SmartKC. Since SmartKC is a new device and only had a small dataset (114 sam-ples), we developed a 2-stage transfer learning strategy-using historical data collected from a medical-grade topographer and a subset of SmartKC data-to satisfactorily train our network. This, combined with our domain-specific data augmentations, achieved a sensitivity of 91.3% and a specificity of 94.2%.
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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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A Systematic Review of Deep Learning Applications for Optical Coherence Tomography in Age-Related Macular Degeneration. Retina 2022; 42:1417-1424. [DOI: 10.1097/iae.0000000000003535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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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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Schatteburg J, Langenbucher A. Protocol for the diagnosis of keratoconus using convolutional neural networks. PLoS One 2022; 17:e0264219. [PMID: 35180279 PMCID: PMC8856512 DOI: 10.1371/journal.pone.0264219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/01/2022] [Indexed: 11/18/2022] Open
Abstract
Keratoconus is the corneal disease with the highest reported incidence of 1:2000. The treatment's level of success highly depends on how early it was started. Subsequently, a fast and highly capable diagnostic tool is crucial. While there are many computer-based systems that are capable of the analysis of medical image data, they only provide parameters. These have advanced quite far, though full diagnosis does not exist. Machine learning has provided the capabilities for the parameters, and numerous similar scientific fields have developed full image diagnosis based on neural networks. The Homburg Keratoconus Center has been gathering almost 2000 patient datasets, over 1000 of them over the course of their disease. Backed by this databank, this work aims to develop a convolutional neural network to tackle diagnosis of keratoconus as the major corneal disease.
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Affiliation(s)
- Jan Schatteburg
- Department of Experimental Ophthalmology, Saarland University, Homburg, Germany
| | - Achim Langenbucher
- Department of Experimental Ophthalmology, Saarland University, Homburg, Germany
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Artificial Intelligence Technique of Synthesis and Characterizations for Measurement of Optical Particles in Medical Devices. Appl Bionics Biomech 2022; 2022:9103551. [PMID: 35186120 PMCID: PMC8856814 DOI: 10.1155/2022/9103551] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/12/2022] [Accepted: 01/27/2022] [Indexed: 02/03/2023] Open
Abstract
The aim of this study is to demonstrate the effect of particle size on semiconductor properties; artificial intelligence is being used for the research methods. As a result, we picked cadmium sulfide (CdS), which is a unique semiconductor material that is employed in a broad variety of current applications. Given that CdS has distinct electrical and optical characteristics, it may be employed in the production of solar cells, for example. Solar cells, as is also well known, have become an essential source of energy in the world. Within the visible range (500-700 nm), we create one layer of bulk CdS and one layer of nano-CdS air bulk CdS air and air nano-CdS air. We used a number of instrumentation methods to investigate the naked CdS nanoparticles, including XRD, SEM-EDX, UV-Vis spectroscopy, TEM, XPS, and PL spectroscopy, among others. The results show that for bulk CdS at normal incidence, the transmittance is T = 45, and for nano-CdS with particle size 3 nm, the transmittance is T = 85.8, with transverse-electric (S-polarized) and transverse-magnetic (P-polarized) transmittances of TE = 75 and TM = 80, respectively.
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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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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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Al-Timemy AH, Mosa ZM, Alyasseri Z, Lavric A, Lui MM, Hazarbassanov RM, Yousefi S. A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps. Transl Vis Sci Technol 2021; 10:16. [PMID: 34913952 PMCID: PMC8684312 DOI: 10.1167/tvst.10.14.16] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Purpose To develop and assess the accuracy of a hybrid deep learning construct for detecting keratoconus (KCN) based on corneal topographic maps. Methods We collected 3794 corneal images from 542 eyes of 280 subjects and developed seven deep learning models based on anterior and posterior eccentricity, anterior and posterior elevation, anterior and posterior sagittal curvature, and corneal thickness maps to extract deep corneal features. An independent subset with 1050 images collected from 150 eyes of 85 subjects from a separate center was used to validate models. We developed a hybrid deep learning model to detect KCN. We visualized deep features of corneal parameters to assess the quality of learning subjectively and computed area under the receiver operating characteristic curve (AUC), confusion matrices, accuracy, and F1 score to evaluate models objectively. Results In the development dataset, 204 eyes were normal, 123 eyes were suspected KCN, and 215 eyes had KCN. In the independent validation dataset, 50 eyes were normal, 50 eyes were suspected KCN, and 50 eyes were KCN. Images were annotated by three corneal specialists. The AUC of the models for the two-class and three-class problems based on the development set were 0.99 and 0.93, respectively. Conclusions The hybrid deep learning model achieved high accuracy in identifying KCN based on corneal maps and provided a time-efficient framework with low computational complexity. Translational Relevance Deep learning can detect KCN from non-invasive corneal images with high accuracy, suggesting potential application in research and clinical practice to identify KCN.
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Affiliation(s)
- Ali H Al-Timemy
- Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq.,Centre for Robotics and Neural Systems, Cognitive Institute, School of Engineering, Computing and Mathematics, Plymouth University, Plymouth, UK
| | | | - Zaid Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.,ECE Department-Faculty of Engineering, University of Kufa, Najaf, Iraq
| | - Alexandru Lavric
- Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, Suceava, Bukovina, Romania
| | - Marcelo M Lui
- Hospital de Olhos-CRO, Guarulhos, São Paulo, São Paulo, Brazil
| | - Rossen M Hazarbassanov
- Hospital de Olhos-CRO, Guarulhos, São Paulo, São Paulo, Brazil.,Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo, Brazil
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.,Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9979560. [PMID: 34824602 PMCID: PMC8610665 DOI: 10.1155/2021/9979560] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 10/28/2021] [Indexed: 02/03/2023]
Abstract
Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.
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Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021; 135:2357-2376. [PMID: 34661658 DOI: 10.1042/cs20210207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.
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Kamiya K, Ayatsuka Y, Kato Y, Shoji N, Mori Y, Miyata K. Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography. Front Med (Lausanne) 2021; 8:724902. [PMID: 34671618 PMCID: PMC8520919 DOI: 10.3389/fmed.2021.724902] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/06/2021] [Indexed: 01/31/2023] Open
Abstract
Purpose: Placido disk-based corneal topography is still most commonly used in daily practice. This study was aimed to evaluate the diagnosability of keratoconus using deep learning of a color-coded map with Placido disk-based corneal topography. Methods: We retrospectively examined 179 keratoconic eyes [Grade 1 (54 eyes), 2 (52 eyes), 3 (23 eyes), and 4 (50 eyes), according to the Amsler-Krumeich classification], and 170 age-matched healthy eyes, with good quality images of corneal topography measured with a Placido disk corneal topographer (TMS-4TM, Tomey). Using deep learning of a color-coded map, we evaluated the diagnostic accuracy, sensitivity, and specificity, for keratoconus screening and staging tests, in these eyes. Results: Deep learning of color-coded maps exhibited an accuracy of 0.966 (sensitivity 0.988, specificity 0.944) in discriminating keratoconus from normal eyes. It also exhibited an accuracy of 0.785 (0.911 for Grade 1, 0.868 for Grade 2, 0.920 for Grade 3, and 0.905 for Grade 4) in classifying the stage. The area under the curve value was 0.997, 0.955, 0.899, 0.888, and 0.943 as Grade 0 (normal) to 4 grading tests, respectively. Conclusions: Deep learning using color-coded maps with conventional corneal topography effectively distinguishes between keratoconus and normal eyes and classifies the grade of the disease, indicating that this will become an aid for enhancing the diagnosis and staging ability of keratoconus in a clinical setting.
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Affiliation(s)
- Kazutaka Kamiya
- Visual Physiology, School of Allied Health Sciences, Kitasato University, Kanagawa, Japan
| | | | | | - Nobuyuki Shoji
- Department of Ophthalmology, School of Medicine, Kitasato University, Kanagawa, Japan
| | - Yosai Mori
- Department of Ophthalmology, Miyata Eye Hospital, Miyazaki, Japan
| | - Kazunori Miyata
- Department of Ophthalmology, Miyata Eye Hospital, Miyazaki, Japan
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39
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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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Hanif AM, Beqiri S, Keane PA, Campbell JP. Applications of interpretability in deep learning models for ophthalmology. Curr Opin Ophthalmol 2021; 32:452-458. [PMID: 34231530 PMCID: PMC8373813 DOI: 10.1097/icu.0000000000000780] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT FINDINGS The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' 'interpretability' and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice.
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Affiliation(s)
- Adam M. Hanif
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Sara Beqiri
- University College London Division of Medicine, London, United Kingdom
| | - Pearse A. Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- University College London Institute of Ophthalmology, United Kingdom
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Chen X, Zhao J, Iselin KC, Borroni D, Romano D, Gokul A, McGhee CNJ, Zhao Y, Sedaghat MR, Momeni-Moghaddam H, Ziaei M, Kaye S, Romano V, Zheng Y. Keratoconus detection of changes using deep learning of colour-coded maps. BMJ Open Ophthalmol 2021; 6:e000824. [PMID: 34337155 PMCID: PMC8278890 DOI: 10.1136/bmjophth-2021-000824] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/05/2021] [Indexed: 12/26/2022] Open
Abstract
Objective To evaluate the accuracy of convolutional neural networks technique (CNN) in detecting keratoconus using colour-coded corneal maps obtained by a Scheimpflug camera. Design Multicentre retrospective study. Methods and analysis We included the images of keratoconic and healthy volunteers’ eyes provided by three centres: Royal Liverpool University Hospital (Liverpool, UK), Sedaghat Eye Clinic (Mashhad, Iran) and The New Zealand National Eye Center (New Zealand). Corneal tomography scans were used to train and test CNN models, which included healthy controls. Keratoconic scans were classified according to the Amsler-Krumeich classification. Keratoconic scans from Iran were used as an independent testing set. Four maps were considered for each scan: axial map, anterior and posterior elevation map, and pachymetry map. Results A CNN model detected keratoconus versus health eyes with an accuracy of 0.9785 on the testing set, considering all four maps concatenated. Considering each map independently, the accuracy was 0.9283 for axial map, 0.9642 for thickness map, 0.9642 for the front elevation map and 0.9749 for the back elevation map. The accuracy of models in recognising between healthy controls and stage 1 was 0.90, between stages 1 and 2 was 0.9032, and between stages 2 and 3 was 0.8537 using the concatenated map. Conclusion CNN provides excellent detection performance for keratoconus and accurately grades different severities of disease using the colour-coded maps obtained by the Scheimpflug camera. CNN has the potential to be further developed, validated and adopted for screening and management of keratoconus.
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Affiliation(s)
- Xu Chen
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Jiaxin Zhao
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Katja C Iselin
- Department of Ophthalmology, St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Davide Borroni
- Department of Ophthalmology, St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Davide Romano
- Department of Ophthalmology, St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Akilesh Gokul
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Charles N J McGhee
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Mohammad-Reza Sedaghat
- Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Hamed Momeni-Moghaddam
- Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Mohammed Ziaei
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Stephen Kaye
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.,Department of Ophthalmology, St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Vito Romano
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.,Department of Ophthalmology, St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
| | - Yalin Zheng
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
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Al-Timemy AH, Ghaeb NH, Mosa ZM, Escudero J. Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09880-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Abstract
Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.
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