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Rao Y, Zhang Q, Wang X, Xue X, Ma W, Xu L, Xing S. Automated diagnosis of adenoid hypertrophy with lateral cephalogram in children based on multi-scale local attention. Sci Rep 2024; 14:18619. [PMID: 39127777 PMCID: PMC11316792 DOI: 10.1038/s41598-024-69827-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/09/2024] [Indexed: 08/12/2024] Open
Abstract
Adenoid hypertrophy can lead to adenoidal mouth breathing, which can result in "adenoid face" and, in severe cases, can even lead to respiratory tract obstruction. The Fujioka ratio method, which calculates the ratio of adenoid (A) to nasopharyngeal (N) space in an adenoidal-cephalogram (A/N), is a well-recognized and effective technique for detecting adenoid hypertrophy. However, this process is time-consuming and relies on personal experience, so a fully automated and standardized method needs to be designed. Most of the current deep learning-based methods for automatic diagnosis of adenoids are CNN-based methods, which are more sensitive to features similar to adenoids in lateral views and can affect the final localization results. In this study, we designed a local attention-based method for automatic diagnosis of adenoids, which takes AdeBlock as the basic module, fuses the spatial and channel information of adenoids through two-branch local attention computation, and combines the downsampling method without losing spatial information. Our method achieved mean squared error (MSE) 0.0023, mean radial error (MRE) 1.91, and SD (standard deviation) 7.64 on the three hospital datasets, outperforming other comparative methods.
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Affiliation(s)
- Yanying Rao
- Department of Radiology, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350014, Fujian, China
- Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350005, Fujian, China
| | - Qiuyun Zhang
- Department of Otorhinolaryngology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fujian, 350005, China
- Department of Otorhinolaryngology, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fujian, 350014, China
| | - Xiaowei Wang
- Department of Computer Science and Mathematics, Fujian University of Technology, Fujian, 350116, China
| | - Xiaoling Xue
- Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350005, Fujian, China
| | - Wenjing Ma
- Department of Computer Science and Mathematics, Fujian University of Technology, Fujian, 350116, China
| | - Lin Xu
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Shuli Xing
- Department of Computer Science and Mathematics, Fujian University of Technology, Fujian, 350116, China.
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian, 350116, China.
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Kumar KS, Singh NP. An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf. Med Eng Phys 2022; 110:103936. [PMID: 36529622 DOI: 10.1016/j.medengphy.2022.103936] [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: 05/05/2022] [Revised: 11/05/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4 s, which has been identified on boundary condition of Pdf function.
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Affiliation(s)
- K Susheel Kumar
- GITAM University, Bengaluru, 561203, India; National Institute of Technology Hamirpur, Himachal Pradesh 177005, India.
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Morya AK, Janti SS, Sisodiya P, Tejaswini A, Prasad R, Mali KR, Gurnani B. Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies. World J Diabetes 2022; 13:822-834. [PMID: 36311999 PMCID: PMC9606792 DOI: 10.4239/wjd.v13.i10.822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/11/2022] [Accepted: 09/10/2022] [Indexed: 02/05/2023] Open
Abstract
Artificial Intelligence is a multidisciplinary field with the aim of building platforms that can make machines act, perceive, reason intelligently and whose goal is to automate activities that presently require human intelligence. From the cornea to the retina, artificial intelligence (AI) is expected to help ophthalmologists diagnose and treat ocular diseases. In ophthalmology, computerized analytics are being viewed as efficient and more objective ways to interpret the series of images and come to a conclusion. AI can be used to diagnose and grade diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, IOL power calculation, retinopathy of prematurity and keratoconus. This review article intends to discuss various aspects of artificial intelligence in ophthalmology.
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Affiliation(s)
- Arvind Kumar Morya
- Department of Ophthalmology, All India Institute of Medical Sciences Bibinagar, Hyderabad 508126, Telangana, India
| | - Siddharam S Janti
- Department of Ophthalmology, All India Institute of Medical Sciences Bibinagar, Hyderabad 508126, Telangana, India
| | - Priya Sisodiya
- Department of Ophthalmology, Sadguru Netra Chikitsalaya, Chitrakoot 485001, Madhya Pradesh, India
| | - Antervedi Tejaswini
- Department of Ophthalmology, All India Institute of Medical Sciences Bibinagar, Hyderabad 508126, Telangana, India
| | - Rajendra Prasad
- Department of Ophthalmology, R P Eye Institute, New Delhi 110001, New Delhi, India
| | - Kalpana R Mali
- Department of Pharmacology, All India Institute of Medical Sciences, Bibinagar, Hyderabad 508126, Telangana, India
| | - Bharat Gurnani
- Department of Ophthalmology, Aravind Eye Hospital and Post Graduate Institute of Ophthalmology, Pondicherry 605007, Pondicherry, India
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Bhardwaj C, Jain S, Sood M. Transfer learning based robust automatic detection system for diabetic retinopathy grading. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06042-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Krishna Adithya V, Williams BM, Czanner S, Kavitha S, Friedman DS, Willoughby CE, Venkatesh R, Czanner G. EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection. J Imaging 2021; 7:92. [PMID: 39080880 PMCID: PMC8321378 DOI: 10.3390/jimaging7060092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/21/2021] [Accepted: 05/27/2021] [Indexed: 12/11/2022] Open
Abstract
Current research in automated disease detection focuses on making algorithms "slimmer" reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation "EffUnet" with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed "SpaGen" We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, "EffUnet-SpaGen", is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.
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Affiliation(s)
- Venkatesh Krishna Adithya
- Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India; (V.K.A.); (S.K.); (R.V.)
| | - Bryan M. Williams
- School of Computing and Communications, Lancaster University, Bailrigg, Lancaster LA1 4WA, UK;
| | - Silvester Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Srinivasan Kavitha
- Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India; (V.K.A.); (S.K.); (R.V.)
| | - David S. Friedman
- Glaucoma Center of Excellence, Harvard Medical School, Boston, MA 02114, USA;
| | - Colin E. Willoughby
- Biomedical Research Institute, Ulster University, Coleraine, Co. Londonderry BT52 1SA, UK;
| | - Rengaraj Venkatesh
- Department of Glaucoma, Aravind Eye Care System, Thavalakuppam, Pondicherry 605007, India; (V.K.A.); (S.K.); (R.V.)
| | - Gabriela Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK;
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Bhardwaj C, Jain S, Sood M. Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model. J Digit Imaging 2021; 34:440-457. [PMID: 33686525 DOI: 10.1007/s10278-021-00418-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 12/23/2020] [Accepted: 01/03/2021] [Indexed: 12/23/2022] Open
Abstract
The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these eye-related problems, helping the ophthalmic experts in providing the second opinion for effectual treatment. The deep learning techniques have evolved as an improvement over the conventional approaches, which are dependent on the handcrafted feature extraction. To address the issue of proficient DR discrimination, the authors have proposed a quadrant ensemble automated DR grading approach by implementing InceptionResnet-V2 deep neural network framework. The presented model incorporates histogram equalization, optical disc localization, and quadrant cropping along with the data augmentation step for improving the network performance. A superior accuracy performance of 93.33% is observed for the proposed framework, and a significant reduction of 0.325 is noticed in the cross-entropy loss function for MESSIDOR benchmark dataset; however, its validation utilizing the latest IDRiD dataset establishes its generalization ability. The accuracy improvement of 13.58% is observed when the proposed QEIRV-2 model is compared with the classical Inception-V3 CNN model. To justify the viability of the proposed framework, its performance is compared with the existing state-of-the-art approaches and 25.23% of accuracy improvement is observed.
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Affiliation(s)
- Charu Bhardwaj
- Department of Electronics and Communication Engineering, JUIT Waknaghat, Solan, HP, India.
| | - Shruti Jain
- Department of Electronics and Communication Engineering, JUIT Waknaghat, Solan, HP, India
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Panda BB, Thakur S, Mohapatra S, Parida S. Artificial intelligence in ophthalmology: A new era is beginning. Artif Intell Med Imaging 2021; 2:5-12. [DOI: 10.35711/aimi.v2.i1.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 12/31/2020] [Accepted: 02/12/2021] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence (AI) in ophthalmology is not very new and its use is expanding into various subspecialties of the eye like retina and glaucoma, thereby helping ophthalmologists to diagnose and treat diseases better than before. Incorporating “deep learning” (a subfield of AI) into image-based systems such as optical coherence tomography has dramatically improved the machine's ability to screen and identify stages of diabetic retinopathy accurately. Similar applications have been tried in the field of retinopathy of prematurity and age-related macular degeneration, a silent retinal condition that needs to be diagnosed early to prevent progression. The advent of AI into glaucoma diagnostics in analyzing visual fields and assessing disease progression also holds a promising role. The ability of the software to detect even a subtle defect that the human eye can miss has led to a revolution in the management of certain ocular conditions. However, there are few significant challenges in the AI systems, such as the incorporation of quality images, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving the machines/software to potentiate their efficacy and standards. This review article shall discuss the current applications of AI in ophthalmology, significant challenges and the prospects as to how both science and medicine can work together.
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Affiliation(s)
- Bijnya Birajita Panda
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
| | - Subhodeep Thakur
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
| | - Sumita Mohapatra
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
| | - Subhabrata Parida
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
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8
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Automated detection of optic disc contours in fundus images using decision tree classifier. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2019.11.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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9
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Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol 2019; 67:1004-1009. [PMID: 31238395 PMCID: PMC6611318 DOI: 10.4103/ijo.ijo_1989_18] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Use of artificial intelligence in medicine in an evolving technology which holds promise for mass screening and perhaps may even help in establishing an accurate diagnosis. The ability of complex computing is to perform pattern recognition by creating complex relationships based on input data and then comparing it with performance standards is a big step. Diabetic retinopathy is an ever-increasing problem. Early screening and timely treatment of the same can reduce the burden of sight threatening retinopathy. Any tool which can aid in quick screening of this disorder and minimize requirement of trained human resource for the same would probably be a boon for patients and ophthalmologists. In this review we discuss the current status of use of artificial intelligence in diabetic retinopathy and few other common retinal disorders.
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Affiliation(s)
- Srikanta Kumar Padhy
- Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Brijesh Takkar
- Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Rohan Chawla
- Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | - Atul Kumar
- Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
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Bajwa MN, Malik MI, Siddiqui SA, Dengel A, Shafait F, Neumeier W, Ahmed S. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med Inform Decis Mak 2019; 19:136. [PMID: 31315618 PMCID: PMC6637616 DOI: 10.1186/s12911-019-0842-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 06/19/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. METHODS The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. RESULTS The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset. CONCLUSION Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier's performance and calls for additional performance metrics to substantiate the results.
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Affiliation(s)
- Muhammad Naseer Bajwa
- Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
| | - Muhammad Imran Malik
- Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad, 46000 Pakistan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, H-12, Islamabad, 46000 Pakistan
| | - Shoaib Ahmed Siddiqui
- Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
| | - Andreas Dengel
- Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
| | - Faisal Shafait
- Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad, 46000 Pakistan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology, H-12, Islamabad, 46000 Pakistan
| | | | - Sheraz Ahmed
- Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI), 67663 Kaiserslautern, Germany
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Uribe-Valencia LJ, Martínez-Carballido JF. Automated Optic Disc region location from fundus images: Using local multi-level thresholding, best channel selection, and an Intensity Profile Model. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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12
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Diabetic retinopathy techniques in retinal images: A review. Artif Intell Med 2018; 97:168-188. [PMID: 30448367 DOI: 10.1016/j.artmed.2018.10.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 10/08/2018] [Accepted: 10/24/2018] [Indexed: 12/23/2022]
Abstract
The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy.We give a detailed review of 79 algorithms for detecting different features of diabetic retinopathy during the last eight years.
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Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Exp Ophthalmol 2018; 47:128-139. [DOI: 10.1111/ceo.13381] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 08/25/2018] [Indexed: 12/23/2022]
Affiliation(s)
- Daniel T Hogarty
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne; Melbourne Victoria Australia
| | - David A Mackey
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne; Melbourne Victoria Australia
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia; Perth Western Australia Australia
- Menzies Institute for Medical Research, University of Tasmania; Hobart Tasmania Australia
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne; Melbourne Victoria Australia
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia; Perth Western Australia Australia
- Menzies Institute for Medical Research, University of Tasmania; Hobart Tasmania Australia
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Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol 2018; 11:1555-1561. [PMID: 30225234 PMCID: PMC6133903 DOI: 10.18240/ijo.2018.09.21] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 05/03/2018] [Indexed: 12/18/2022] Open
Abstract
Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.
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Affiliation(s)
- Xue-Li Du
- Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Wen-Bo Li
- Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Bo-Jie Hu
- Tianjin Medical University Eye Hospital, Tianjin 300384, China
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15
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Optic Disc Detection from Fundus Photography via Best-Buddies Similarity. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8050709] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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16
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Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry (Basel) 2018. [DOI: 10.3390/sym10040087] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm. Int J Biomed Imaging 2018; 2018:2815163. [PMID: 29552029 PMCID: PMC5818904 DOI: 10.1155/2018/2815163] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 12/17/2017] [Accepted: 01/10/2018] [Indexed: 12/23/2022] Open
Abstract
Examining retinal image continuously plays an important role in determining human eye health; with any variation present in this image, it may be resulting from some disease. Therefore, there is a need for computer-aided scanning for retinal image to perform this task automatically and accurately. The fundamental step in this task is identification of the retina elements; optical disk localization is the most important one in this identification. Different optical disc localization algorithms have been suggested, such as an algorithm that would be proposed in this paper. The assumption is based on the fact that optical disc area has rich information, so its entropy value is more significant in this area. The suggested algorithm has recursive steps for testing the entropy of different patches in image; sliding window technique is used to get these patches in a specific way. The results of practical work were obtained using different common data set, which achieved good accuracy in trivial computation time. Finally, this paper consists of four sections: a section for introduction containing the related works, a section for methodology and material, a section for practical work with results, and a section for conclusion.
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Automatic CDR Estimation for Early Glaucoma Diagnosis. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:5953621. [PMID: 29279773 PMCID: PMC5723944 DOI: 10.1155/2017/5953621] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 09/09/2017] [Accepted: 09/24/2017] [Indexed: 12/23/2022]
Abstract
Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.
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