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Lendzioszek M, Bryl A, Poppe E, Zorena K, Mrugacz M. Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review. J Clin Med 2024; 13:3950. [PMID: 38999513 DOI: 10.3390/jcm13133950] [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: 05/28/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
Thrombosis of retinal veins is one of the most common retinal vascular diseases that may lead to vascular blindness. The latest epidemiological data leave no illusions that the burden on the healthcare system, as impacted by patients with this diagnosis, will increase worldwide. This obliges scientists to search for new therapeutic and diagnostic options. In the 21st century, there has been tremendous progress in retinal imaging techniques, which has facilitated a better understanding of the mechanisms related to the development of retinal vein occlusion (RVO) and its complications, and consequently has enabled the introduction of new treatment methods. Moreover, artificial intelligence (AI) is likely to assist in selecting the best treatment option for patients in the near future. The aim of this comprehensive review is to re-evaluate the old but still relevant data on the RVO and confront them with new studies. The paper will provide a detailed overview of diagnosis, current treatment, prevention, and future therapeutic possibilities regarding RVO, as well as clarifying the mechanism of macular edema in this disease entity.
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Affiliation(s)
- Maja Lendzioszek
- Department of Ophthalmology, Voivodship Hospital, 18-400 Lomza, Poland
| | - Anna Bryl
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, 15-089 Bialystok, Poland
| | - Ewa Poppe
- Department of Ophthalmology, Voivodship Hospital, 18-400 Lomza, Poland
| | - Katarzyna Zorena
- Department of Immunobiology and Environment Microbiology, Medical University of Gdansk, Dębinki 7, 80-211 Gdansk, Poland
| | - Malgorzata Mrugacz
- Department of Ophthalmology and Eye Rehabilitation, Medical University of Bialystok, 15-089 Bialystok, Poland
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Maleczek M, Laxar D, Kapral L, Kuhrn M, Abulesz YT, Dibiasi C, Kimberger O. A Comparison of Five Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of Five Different Vital Signs: A Retrospective Analysis. Anesthesiology 2024; 141:32-43. [PMID: 38466210 DOI: 10.1097/aln.0000000000004971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
BACKGROUND Research on electronic health record physiologic data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized that different artifact detection algorithms, including machine learning, may be necessary to provide optimal performance for various vital signs and clinical contexts. METHODS In a retrospective single-center study, intraoperative operating room and intensive care unit (ICU) electronic health record datasets including heart rate, oxygen saturation, blood pressure, temperature, and capnometry were included. All records were screened for artifacts by at least two human experts. Classical artifact detection methods (cutoff, multiples of SD [z-value], interquartile range, and local outlier factor) and a supervised learning model implementing long short-term memory neural networks were tested for each vital sign against the human expert reference dataset. For each artifact detection algorithm, sensitivity and specificity were calculated. RESULTS A total of 106 (53 operating room and 53 ICU) patients were randomly selected, resulting in 392,808 data points. Human experts annotated 5,167 (1.3%) data points as artifacts. The artifact detection algorithms demonstrated large variations in performance. The specificity was above 90% for all detection methods and all vital signs. The neural network showed significantly higher sensitivities than the classic methods for heart rate (ICU, 33.6%; 95% CI, 33.1 to 44.6), systolic invasive blood pressure (in both the operating room [62.2%; 95% CI, 57.5 to 71.9] and the ICU [60.7%; 95% CI, 57.3 to 71.8]), and temperature in the operating room (76.1%; 95% CI, 63.6 to 89.7). The CI for specificity overlapped for all methods. Generally, sensitivity was low, with only the z-value for oxygen saturation in the operating room reaching 88.9%. All other sensitivities were less than 80%. CONCLUSIONS No single artifact detection method consistently performed well across different vital signs and clinical settings. Neural networks may be a promising artifact detection method for specific vital signs. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Mathias Maleczek
- Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Daniel Laxar
- Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Lorenz Kapral
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Melanie Kuhrn
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Yannic-Tomas Abulesz
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Christoph Dibiasi
- Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Oliver Kimberger
- Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
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Gueddena Y, Aboudi N, Zgolli H, Mabrouk S, Sidibe D, Tabia H, Khlifa N. A new intelligent system based deep learning to detect DME and AMD in OCT images. Int Ophthalmol 2024; 44:191. [PMID: 38653842 DOI: 10.1007/s10792-024-03115-8] [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: 05/06/2023] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16)2 , BCNN (VGG19)2 , and BCNN (Inception_V3)2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.
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Affiliation(s)
- Yassmine Gueddena
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tuins El Manar, 1006, Tunis, Tunisia
| | - Noura Aboudi
- Laboratory of Biophysics and Medical Technologies, National Engineering School of Carthage, 2035, Tunis, Tunisia.
| | - Hsouna Zgolli
- Department A, Hedi Raies of Ophthalmology Institute, Tunis, Tunisia
| | - Sonia Mabrouk
- Department A, Hedi Raies of Ophthalmology Institute, Tunis, Tunisia
| | - Désiré Sidibe
- IBISC Laboratory, University of Paris-Saclay, Evry, France
| | - Hedi Tabia
- IBISC Laboratory, University of Paris-Saclay, Evry, France
| | - Nawres Khlifa
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tuins El Manar, 1006, Tunis, Tunisia
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Alenezi A, Alhamad H, Brindhaban A, Amizadeh Y, Jodeiri A, Danishvar S. Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach. Bioengineering (Basel) 2024; 11:300. [PMID: 38671722 PMCID: PMC11047645 DOI: 10.3390/bioengineering11040300] [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: 02/12/2024] [Revised: 03/08/2024] [Accepted: 03/15/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model's accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.
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Affiliation(s)
- Ahmad Alenezi
- Radiologic Sciences Department, Kuwait University, Jabriya 31470, Kuwait
| | - Hamad Alhamad
- Occupational Therapy Department, Kuwait University, Jabriya 31470, Kuwait;
| | - Ajit Brindhaban
- Radiologic Sciences Department, Kuwait University, Jabriya 31470, Kuwait
| | | | - Ata Jodeiri
- Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz 51656, Iran
| | - Sebelan Danishvar
- College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK;
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5
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Ji YK, Hua RR, Liu S, Xie CJ, Zhang SC, Yang WH. Intelligent diagnosis of retinal vein occlusion based on color fundus photographs. Int J Ophthalmol 2024; 17:1-6. [PMID: 38239946 PMCID: PMC10754666 DOI: 10.18240/ijo.2024.01.01] [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: 08/29/2023] [Accepted: 10/17/2023] [Indexed: 01/22/2024] Open
Abstract
AIM To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.
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Affiliation(s)
- Yu-Ke Ji
- Eye Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Rong-Rong Hua
- College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, Jiangsu Province, China
| | - Sha Liu
- Eye Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Cui-Juan Xie
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518000, Guangdong Province, China
| | - Shao-Chong Zhang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518000, Guangdong Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518000, Guangdong Province, China
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Koseoglu ND, Grzybowski A, Liu TYA. Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review. Ophthalmol Ther 2023; 12:2347-2359. [PMID: 37493854 PMCID: PMC10441995 DOI: 10.1007/s40123-023-00775-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023] Open
Abstract
Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.
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Affiliation(s)
- Neslihan Dilruba Koseoglu
- Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - T Y Alvin Liu
- Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA.
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He S, Bulloch G, Zhang L, Xie Y, Wu W, He Y, Meng W, Shi D, He M. Cross-camera Performance of Deep Learning Algorithms to Diagnose Common Ophthalmic Diseases: A Comparative Study Highlighting Feasibility to Portable Fundus Camera Use. Curr Eye Res 2023; 48:857-863. [PMID: 37246918 DOI: 10.1080/02713683.2023.2215984] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/19/2023] [Accepted: 05/14/2023] [Indexed: 05/30/2023]
Abstract
PURPOSE To compare the inter-camera performance and consistency of various deep learning (DL) diagnostic algorithms applied to fundus images taken from desktop Topcon and portable Optain cameras. METHODS Participants over 18 years of age were enrolled between November 2021 and April 2022. Pair-wise fundus photographs from each patient were collected in a single visit; once by Topcon (used as the reference camera) and once by a portable Optain camera (the new target camera). These were analyzed by three previously validated DL models for the detection of diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucomatous optic neuropathy (GON). Ophthalmologists manually analyzed all fundus photos for the presence of DR and these were referred to as the ground truth. Sensitivity, specificity, the area under the curve (AUC) and agreement between cameras (estimated by Cohen's weighted kappa, K) were the primary outcomes of this study. RESULTS A total of 504 patients were recruited. After excluding 12 photographs with matching errors and 59 photographs with low quality, 906 pairs of Topcon-Optain fundus photos were available for algorithm assessment. Topcon and Optain cameras had excellent consistency (Κ=0.80) when applied to the referable DR algorithm, while AMD had moderate consistency (Κ=0.41) and GON had poor consistency (Κ=0.32). For the DR model, Topcon and Optain achieved a sensitivity of 97.70% and 97.67% and a specificity of 97.92% and 97.93%, respectively. There was no significant difference between the two camera models (McNemar's test: x2=0.08, p = .78). CONCLUSION Topcon and Optain cameras had excellent consistency for detecting referable DR, albeit performances for detecting AMD and GON models were unsatisfactory. This study highlights the methods of using pair-wise images to evaluate DL models between reference and new fundus cameras.
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Affiliation(s)
- Shuang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Gabriella Bulloch
- University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Melbourne, Victoria, Australia
| | - Liangxin Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yiyu Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Weiyu Wu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Yahong He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Wei Meng
- Eyetelligence Ltd, Melbourne, Victoria, Australia
| | - Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Eyetelligence Ltd, Melbourne, Victoria, Australia
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Sahoo M, Mitra M, Pal S. Improved Detection of Dry Age-Related Macular Degeneration from Optical Coherence Tomography Images using Adaptive Window Based Feature Extraction and Weighted Ensemble Based Classification Approach. Photodiagnosis Photodyn Ther 2023:103629. [PMID: 37244451 DOI: 10.1016/j.pdpdt.2023.103629] [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: 03/04/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task. METHODOLOGY This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer. RESULT The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset. CONCLUSION The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.
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Affiliation(s)
- Moumita Sahoo
- Department of Applied Electronics and Instrumentation Engineering, Haldia Institute of Technology, Haldia, West Bengal, India.
| | - Madhuchhanda Mitra
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - Saurabh Pal
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
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Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Front Cell Dev Biol 2023; 11:1168327. [PMID: 37056999 PMCID: PMC10086262 DOI: 10.3389/fcell.2023.1168327] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.
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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
| | - Yun Ji
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
| | - Yunfang Liu
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
| | - Ying Zhao
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
| | - Liya Zhang
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
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Medhi JP, S.R. N, Choudhury S, Dandapat S. Improved detection and analysis of Macular Edema using modified guided image filtering with modified level set spatial fuzzy clustering on Optical Coherence Tomography images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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11
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da Costa PB, de Almeida JDS, Teixeira JAM, Braz G, de Paiva AC, Silva AC. Computational method for aid in the diagnosis of sixth optic nerve palsy through digital videos. Comput Biol Med 2022; 150:106098. [PMID: 36166988 DOI: 10.1016/j.compbiomed.2022.106098] [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: 02/23/2022] [Revised: 08/14/2022] [Accepted: 09/10/2022] [Indexed: 11/18/2022]
Abstract
The sixth cranial nerve, also known as the abducens nerve, is responsible for controlling the movements of the lateral rectus muscle. Palsies on the sixth nerve prevent some muscles that control eye movements from proper functioning, causing headaches, migraines, blurred vision, vertigo, and double vision. Hence, such palsy should be diagnosed in the early stages to treat it without leaving any sequela. The usual methods for diagnosing the sixth nerve palsy are invasive or depend on expensive equipment, and computer-based methods designed specifically to diagnose the aforementioned palsy were not found until the publication of this work. Therefore, a low-cost, non-invasive method can support or guide the ophthalmologist's diagnosis. In this context, this work presents a computational methodology to aid in diagnosing the sixth nerve palsy using videos to assist ophthalmologists in the diagnostic process, serving as a second opinion. The proposed method uses convolutional neural networks and image processing techniques to track both eyes' movement trajectory during the video. With this trajectory, it is possible to calculate the average velocity (AV) in which each eye moves. Since it is known that paretic eyes move slower than healthy eyes, comparing the AV of both eyes can determine if the eye is healthy or paretic. The results obtained with the proposed method showed that paretic eyes move at least 19.65% slower than healthy ones. This threshold, along with the AV of the movement of the eyes, can help ophthalmologists in their analysis. The proposed method reached 92.64% accuracy in diagnosing the sixth optic nerve palsy (SONP), with a Kappa index of 0.925, which highlights the reliability of the results and gives favorable perspectives for further clinical application.
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Affiliation(s)
- Polyana Bezerra da Costa
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugues, SN, Campus Baganga, Baganga 65085-584, São Luís, MA, Brazil
| | - João Dallyson Sousa de Almeida
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugues, SN, Campus Baganga, Baganga 65085-584, São Luís, MA, Brazil.
| | - Jorge Antonio Meireles Teixeira
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugues, SN, Campus Baganga, Baganga 65085-584, São Luís, MA, Brazil
| | - Geraldo Braz
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugues, SN, Campus Baganga, Baganga 65085-584, São Luís, MA, Brazil
| | - Anselmo Cardoso de Paiva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugues, SN, Campus Baganga, Baganga 65085-584, São Luís, MA, Brazil
| | - Aristófanes Correa Silva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugues, SN, Campus Baganga, Baganga 65085-584, São Luís, MA, Brazil
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Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys. Diagnostics (Basel) 2022; 12:diagnostics12081927. [PMID: 36010277 PMCID: PMC9406878 DOI: 10.3390/diagnostics12081927] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence (AI) has expanded by finding applications in medical diagnosis for clinical support systems [...]
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13
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Toğaçar M, Ergen B, Tümen V. Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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