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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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Choi JY, Kim H, Kim JK, Lee IS, Ryu IH, Kim JS, Yoo TK. Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. Med Biol Eng Comput 2024; 62:449-463. [PMID: 37889431 DOI: 10.1007/s11517-023-02952-6] [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: 04/21/2023] [Accepted: 10/14/2023] [Indexed: 10/28/2023]
Abstract
Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with several pathological corneal conditions. As FP-based examination systems have already been widely distributed, it would be helpful for telemedicine to extract information such as corneal curvature using FP. This study aims to develop a deep learning model based on FP for corneal curvature prediction by categorizing corneas into steep, regular, and flat groups. The EfficientNetB0 architecture with transfer learning was used to learn FP patterns to predict flat, regular, and steep corneas. In validation, the model achieved a multiclass accuracy of 0.727, a Matthews correlation coefficient of 0.519, and an unweighted Cohen's κ of 0.590. The areas under the receiver operating characteristic curves for binary prediction of flat and steep corneas were 0.863 and 0.848, respectively. The optic nerve and its peripheral areas were the main focus of the model. The developed algorithm shows that FP can potentially be used as an imaging modality to estimate corneal curvature in the post-COVID-19 era, whereby patients may benefit from the detection of abnormal corneal curvatures using FP in the telemedicine setting.
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Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | | | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Jung Soo Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Tae Keun Yoo
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.
- Research and Development Department, VISUWORKS, Seoul, South Korea.
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Wan Q, Wei R, Ma K, Yin H, Deng YP, Tang J. Deep Learning-Based Automatic Diagnosis of Keratoconus with Corneal Endothelium Image. Ophthalmol Ther 2023; 12:3047-3065. [PMID: 37665500 PMCID: PMC10640564 DOI: 10.1007/s40123-023-00795-w] [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: 05/11/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023] Open
Abstract
INTRODUCTION The primary objective of this study was to develop an end-to-end model that can accurately identify corneal endothelial cells and diagnose keratoconus based on corneal endothelial images acquired from a non-contact specular microscope. METHODS This was a retrospective case-control study performed at the Refractive Surgery Center of West China Hospital. A total of 403 keratoconus eyes (221 patients) and 370 myopic eyes (185 normal controls) were consecutively recruited from January 2021 to September 2022. Specular microscopy was used to image and measure the morphometric parameters of the corneal endothelial cells. A Fully Convolutional Network model with a ResNet50 (FCN_ResNet50) was established to perform the endothelial segmentation. The images were then classified using an ensemble machine learning system consisting of four pre-trained deep learning networks: DenseNet121, ResNet50, Inception_v3, and MobileNet_v2. The performance of the models was evaluated based on different metrics, such as accuracy, intersection over union (IoU), and mean IoU. RESULTS We established a fully end-to-end deep-learning model for the segmentation of endothelial and diagnosis of keratoconus. For endothelial segmentation, the accuracy of the FCN_ResNet50 model achieved near 90% with mean IoU converging to about 80%. The ensemble machine learning system can achieve over 92% accuracy, and > 98% area under curve (AUC) values to diagnose keratoconus with endothelial cell images. In addition, we constructed a diagnostic model based on deep-learning features and developed an associated nomogram which manifested an excellent performance for diagnosis and monitoring the progression of keratoconus. CONCLUSIONS Our research developed an end-to-end model to automatically identify and assess corneal endothelial morphological changes in keratoconus eyes. Moreover, we also constructed a novel nomogram, which can provide valuable information for the diagnosis, monitoring, and management of the disease.
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Affiliation(s)
- Qi Wan
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ran Wei
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ke Ma
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongbo Yin
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ying-Ping Deng
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Jing Tang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Dua HS, Freitas R, Mohammed I, Ting DSJ, Said DG. The pre-Descemet's layer (Dua's layer, also known as the Dua-Fine layer and the pre-posterior limiting lamina layer): Discovery, characterisation, clinical and surgical applications, and the controversy. Prog Retin Eye Res 2023; 97:101161. [PMID: 36642673 DOI: 10.1016/j.preteyeres.2022.101161] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 12/22/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023]
Abstract
The pre-Descemet's layer/Dua's layer, also termed the Dua-Fine layer and the pre-posterior limiting lamina layer, lies anterior to the Descemet's membrane in the cornea, is 10 μm (range 6-16) thick, made predominantly of type I and some type VI collagen with abundant elastin, more than any other layer of the cornea. It has high tensile strength (bursting pressure up to 700 mm of Hg), is impervious to air and almost acellular. At the periphery it demonstrates fenestrations and ramifies to become the core of the trabecular meshwork, with implications for intraocular pressure and glaucoma. It has been demonstrated in some species of animals. The layer has assumed considerable importance in anterior and posterior lamellar corneal transplant surgery by improving our understanding of the behaviour of corneal tissue during these procedures, improved techniques and made the surgery safer with better outcomes. It has led to the innovation of new surgical procedures namely, pre-Descemet's endothelial keratoplasty, suture management of acute hydrops, DALK-triple and Fogla's mini DALK. The discovery and knowledge of the layer has introduced paradigm shifts in our age old concepts of Descemet's membrane detachment, acute corneal hydrops in keratoconus and Descemetoceles, with impact on management approaches. It has been shown to contribute to the pathology and clinical signs observed in corneal infections and some corneal dystrophies. Early evidence suggests that it may have a role in the pathogenesis of keratoconus in relation to its elastin content. Its contribution to corneal biomechanics and glaucoma are subjects of current investigations.
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Affiliation(s)
- Harminder S Dua
- Larry A Donoso Laboratory for Eye Research, Academic Unit of Ophthalmology and Visual Sciences, University of Nottingham, The Queens Medical Centre, Nottingham University Hospitals, NHS Trust, Nottingham, England, UK.
| | - Rui Freitas
- Larry A Donoso Laboratory for Eye Research, Academic Unit of Ophthalmology and Visual Sciences, University of Nottingham, The Queens Medical Centre, Nottingham University Hospitals, NHS Trust, Nottingham, England, UK; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Portugal.
| | - Imran Mohammed
- Larry A Donoso Laboratory for Eye Research, Academic Unit of Ophthalmology and Visual Sciences, University of Nottingham, The Queens Medical Centre, Nottingham University Hospitals, NHS Trust, Nottingham, England, UK.
| | - Darren S J Ting
- Larry A Donoso Laboratory for Eye Research, Academic Unit of Ophthalmology and Visual Sciences, University of Nottingham, The Queens Medical Centre, Nottingham University Hospitals, NHS Trust, Nottingham, England, UK.
| | - Dalia G Said
- Larry A Donoso Laboratory for Eye Research, Academic Unit of Ophthalmology and Visual Sciences, University of Nottingham, The Queens Medical Centre, Nottingham University Hospitals, NHS Trust, Nottingham, England, UK; Research Institute of Ophthalmology, Cairo, Egypt.
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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: 0] [Impact Index Per Article: 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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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: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
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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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Fırat M, Çınar A, Çankaya C, Fırat İT, Tuncer T. Prediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model. Comput Biol Med 2022; 146:105541. [PMID: 35525070 DOI: 10.1016/j.compbiomed.2022.105541] [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: 01/26/2022] [Revised: 04/13/2022] [Accepted: 04/16/2022] [Indexed: 11/03/2022]
Abstract
Keratoconus is a common corneal disease that causes vision loss. In order to prevent the progression of the disease, the corneal cross-linking (CXL) treatment is applied. The follow-up of keratoconus after treatment is essential to predict the course of the disease and possible changes in the treatment. In this paper, a deep learning-based 2D regression method is proposed to predict the postoperative Pentacam map images of CXL-treated patients. New images are obtained by the linear interpolation augmentation method from the Pentacam images obtained before and after the CXL treatment. Augmented images and preoperative Pentacam images are given as input to U-Net-based 2D regression architecture. The output of the regression layer, the last layer of the U-Net architecture, provides a predicted Pentacam image of the later stage of the disease. The similarity of the predicted image in the final layer output to the Pentacam image in the postoperative period is evaluated by image similarity algorithms. As a result of the evaluation, the mean SSIM (The structural similarity index measure), PSNR (peak signal-to-noise ratio), and RMSE (root mean square error) similarity values are calculated as 0.8266, 65.85, and 0.134, respectively. These results show that our method successfully predicts the postoperative images of patients treated with CXL.
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Affiliation(s)
- Murat Fırat
- Malatya Turgut Ozal University, Faculty of Medicine, Malatya, Turkey
| | - Ahmet Çınar
- Firat University, Faculty of Engineering, Computer Engineering, Elazig, Turkey
| | - Cem Çankaya
- Inonu University Faculty of Medicine, Malatya, Turkey
| | | | - Taner Tuncer
- Firat University, Faculty of Engineering, Computer Engineering, Elazig, Turkey.
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