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Rizzieri N, Dall’Asta L, Ozoliņš M. Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9. Vision (Basel) 2024; 8:48. [PMID: 39311316 PMCID: PMC11417923 DOI: 10.3390/vision8030048] [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: 06/30/2024] [Revised: 08/13/2024] [Accepted: 08/20/2024] [Indexed: 09/26/2024] Open
Abstract
Computer vision is a powerful tool in medical image analysis, supporting the early detection and classification of eye diseases. Diabetic retinopathy (DR), a severe eye disease secondary to diabetes, accompanies several early signs of eye-threatening conditions, such as microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs), which have been widely studied and targeted as objects to be detected by computer vision models. In this work, we tested the performances of the state-of-the-art YOLOv8 and YOLOv9 architectures on DR fundus features segmentation without coding experience or a programming background. We took one hundred DR images from the public MESSIDOR database, manually labelled and prepared them for pixel segmentation, and tested the detection abilities of different model variants. We increased the diversity of the training sample by data augmentation, including tiling, flipping, and rotating the fundus images. The proposed approaches reached an acceptable mean average precision (mAP) in detecting DR lesions such as MA, HEMO, and EX, as well as a hallmark of the posterior pole of the eye, such as the optic disc. We compared our results with related works in the literature involving different neural networks. Our results are promising, but far from being ready for implementation into clinical practice. Accurate lesion detection is mandatory to ensure early and correct diagnoses. Future works will investigate lesion detection further, especially MA segmentation, with improved extraction techniques, image pre-processing, and standardized datasets.
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Affiliation(s)
- Nicola Rizzieri
- Department of Optometry and Vision Science, Faculty of Physics, Mathematics and Optometry, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia
| | - Luca Dall’Asta
- Research and Development, LIFE Srl, IT-70100 Bari, Italy
| | - Maris Ozoliņš
- Department of Optometry and Vision Science, Faculty of Physics, Mathematics and Optometry, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia
- Institute of Solid State Physics, University of Latvia, Kengaraga Street 8, LV-1063 Riga, Latvia
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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Aswini A, Sivarani T. Modified capsule network for diabetic retinopathy detection and classification using fundus images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diabetic retinopathy becomes an increasingly popular cause of vision loss in diabetic patients. Deep learning has recently received attention as one of the most popular methods for boosting performance in a range of sectors, including medical image analysis and classification. The proposed system comprises three steps; they are image preprocessing, image segmentation, and classification. In preprocessing, the image will be resized, denoising the image and enhancing the contrast of the image which is used for further processing. The lesion region of diabetic retinopathy fundus image is segmented by using Feature Fusion-based U-Net architecture. A blood vessel of a retinal image is extracted by using the spatial fuzzy c means clustering (SFCM) algorithm. Finally, the diabetic retinopathy images are classified using a modified capsule network. The convolution and primary capsule layers collect features from fundus images, while the class capsule and softmax layers decide whether the image belongs to a certain class. Using the Messidor dataset, the proposed system’s network efficiency is evaluated in terms of four performance indicators. The modified contrast limited adaptive histogram equalization technique enhanced the Peak Signal to Noise Ratio (PSNR), mean square error, and Structural Similarity Index Measure (SSIM) have average values of 36.18, 6.15, and 0.95, respectively. After enhancing the image, segmentation is performed to segment the vessel and lesion region. The segmentation accuracy is measured for the proposed segmentation algorithm by using two metrics namely intersection over union (IoU) and Dice similarity coefficient. Then modified capsule network is constructed for classifying the stages of diabetic retinopathy. The experimental result shows that the proposed modified capsule network got 98.57% of classification accuracy.
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Affiliation(s)
| | - T.S. Sivarani
- Department of EEE, Arunachala College of Engineering for Women, Vellichanthai, Tamil Nadu, 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: 3] [Impact Index Per Article: 1.0] [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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Yu Z, Yang X, Sweeting GL, Ma Y, Stolte SE, Fang R, Wu Y. Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods. BMC Med Inform Decis Mak 2022; 22:255. [PMID: 36167551 PMCID: PMC9513862 DOI: 10.1186/s12911-022-01996-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a leading cause of blindness in American adults. If detected, DR can be treated to prevent further damage causing blindness. There is an increasing interest in developing artificial intelligence (AI) technologies to help detect DR using electronic health records. The lesion-related information documented in fundus image reports is a valuable resource that could help diagnoses of DR in clinical decision support systems. However, most studies for AI-based DR diagnoses are mainly based on medical images; there is limited studies to explore the lesion-related information captured in the free text image reports. METHODS In this study, we examined two state-of-the-art transformer-based natural language processing (NLP) models, including BERT and RoBERTa, compared them with a recurrent neural network implemented using Long short-term memory (LSTM) to extract DR-related concepts from clinical narratives. We identified four different categories of DR-related clinical concepts including lesions, eye parts, laterality, and severity, developed annotation guidelines, annotated a DR-corpus of 536 image reports, and developed transformer-based NLP models for clinical concept extraction and relation extraction. We also examined the relation extraction under two settings including 'gold-standard' setting-where gold-standard concepts were used-and end-to-end setting. RESULTS For concept extraction, the BERT model pretrained with the MIMIC III dataset achieve the best performance (0.9503 and 0.9645 for strict/lenient evaluation). For relation extraction, BERT model pretrained using general English text achieved the best strict/lenient F1-score of 0.9316. The end-to-end system, BERT_general_e2e, achieved the best strict/lenient F1-score of 0.8578 and 0.8881, respectively. Another end-to-end system based on the RoBERTa architecture, RoBERTa_general_e2e, also achieved the same performance as BERT_general_e2e in strict scores. CONCLUSIONS This study demonstrated the efficiency of transformer-based NLP models for clinical concept extraction and relation extraction. Our results show that it's necessary to pretrain transformer models using clinical text to optimize the performance for clinical concept extraction. Whereas, for relation extraction, transformers pretrained using general English text perform better.
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Affiliation(s)
- Zehao Yu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA
| | - Xi Yang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA
| | - Gianna L. Sweeting
- Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, FL USA
| | - Yinghan Ma
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA
| | - Skylar E. Stolte
- Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, FL USA
| | - Ruogu Fang
- Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, FL USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA
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Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. SENSORS 2021; 21:s21165283. [PMID: 34450729 PMCID: PMC8398326 DOI: 10.3390/s21165283] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.
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Affiliation(s)
- Tahira Nazir
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
- Correspondence: (J.R.); (H.-Y.K.)
| | - Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Momina Masood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Farooq Ali
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
| | - Hyuk-Yoon Kwon
- Research Center for Electrical and Information Technology, Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
- Correspondence: (J.R.); (H.-Y.K.)
| | - Amir Hussain
- Centre of AI and Data Science, Edinburgh Napier University, Edinburgh EH11 4DY, UK;
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Li M, Wang G, Xia H, Feng Z, Xiao P, Yuan J. Retinal vascular geometry detection as a biomarker in diabetes mellitus. Eur J Ophthalmol 2021; 32:1710-1719. [PMID: 34284606 DOI: 10.1177/11206721211033488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To compare the vessel geometry characteristics of color fundus photographs in normal control and diabetes mellitus (DM) patients and to find potential biomarkers for early diabetic retinopathy (DR) based on a neural network vessel segmentation system and automated vascular geometry parameter analysis software. METHODS A total of 102 consecutive patients with type 2 DM (T2DM) and 132 healthy controls were recruited. All participants underwent general ophthalmic examinations, and retinal fundus photographs were taken with a digital fundus camera without mydriasis. Color fundus photographs were input into a dense-block generative adversarial network (D-GAN)-assisted retinal vascular segmentation system (http://www.gdcerc.cn:8081/#/login) to obtain binary images. These images were then analyzed by customized software (ocular microvascular analysis system V2.9.1) for automatic processing of vessel geometry parameters, including the monofractal dimension (Dbox), multifractal dimension (D0), vessel area ratio (R), max vessel diameter (dmax), average vessel diameter (dave), arc-chord ratio (A/C), and tortuosity (τn). Geometric differences between the healthy subjects and DM patients were analyzed. Then, regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic efficiency of the vascular geometry parameters. RESULTS No significant differences were observed between the baseline characteristics of each group. DM patients had lower Dbox and D0 values (1.330 ± 0.041; 1.347 ± 0.038) than healthy subjects (1.343 ± 0.048, p < 0.05; 1.362 ± 0.042, p < 0.05) and showed increasing values of dmax, dave, A/C, and τn compared with normal controls, although only the differences in dave and τn between the groups were statistically significant. In the regression analysis, dave and τn showed a good correlation with diabetes (dave, OR 1.765, 95% CI 1.319-2.362, p < 0.001; τn, OR 9.323, 95% CI 1.492-58.262, p < 0.05). CONCLUSIONS We demonstrated the relationship between retinal vascular geometry and the process in DM patients, showing that Dbox, D0, dave, and τn may be indicators of morphological changes in retinal vessels in DM patients and can be early biomarkers of DR.
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Affiliation(s)
- Meng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Gengyuan Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Honghui Xia
- Department of Ophthalmology, Zhaoqing Gaoyao People's Hospital, Zhaoqing, People's Republic of China
| | - Ziqing Feng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Peng Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jin Yuan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China
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Wang YL, Yang JY, Yang JY, Zhao XY, Chen YX, Yu WH. Progress of artificial intelligence in diabetic retinopathy screening. Diabetes Metab Res Rev 2021; 37:e3414. [PMID: 33010796 DOI: 10.1002/dmrr.3414] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 08/22/2020] [Accepted: 08/23/2020] [Indexed: 12/29/2022]
Abstract
Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.
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Affiliation(s)
- Yue-Lin Wang
- Department of Ophthalmology, Peking Union Medical College Hospital & Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jing-Yun Yang
- Division of Statistics, School of Economics & Research Center of Financial Information, Shanghai University, Shanghai, China
- Rush Alzheimer's Disease Center & Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - Jing-Yuan Yang
- Department of Ophthalmology, Peking Union Medical College Hospital & Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xin-Yu Zhao
- Department of Ophthalmology, Peking Union Medical College Hospital & Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - You-Xin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital & Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wei-Hong Yu
- Department of Ophthalmology, Peking Union Medical College Hospital & Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Ashraf MN, Hussain M, Habib Z. Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System. Curr Med Imaging 2021; 16:397-426. [PMID: 32410541 DOI: 10.2174/1573405615666190219102427] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 12/31/2018] [Accepted: 01/20/2019] [Indexed: 12/15/2022]
Abstract
Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.
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Affiliation(s)
| | - Muhammad Hussain
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Zulfiqar Habib
- Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
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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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11
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Tewarie IA, Senders JT, Kremer S, Devi S, Gormley WB, Arnaout O, Smith TR, Broekman MLD. Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential. Neurosurg Rev 2020; 44:2047-2057. [PMID: 33156423 PMCID: PMC8338817 DOI: 10.1007/s10143-020-01430-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/28/2020] [Accepted: 10/27/2020] [Indexed: 02/07/2023]
Abstract
Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.
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Affiliation(s)
- Ishaan Ashwini Tewarie
- Department of Neurosurgery, Haaglanden Medical Center, Lijnbaan 32, 2512 VA, The Hague, The Netherlands
- Faculty of Medicine, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joeky T Senders
- Department of Neurosurgery, Haaglanden Medical Center, Lijnbaan 32, 2512 VA, The Hague, The Netherlands
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stijn Kremer
- Department of Neurosurgery, Haaglanden Medical Center, Lijnbaan 32, 2512 VA, The Hague, The Netherlands
| | - Sharmila Devi
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- King's College, London, UK
| | - William B Gormley
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Omar Arnaout
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Timothy R Smith
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marike L D Broekman
- Department of Neurosurgery, Haaglanden Medical Center, Lijnbaan 32, 2512 VA, The Hague, The Netherlands.
- Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Neurosurgery, Leiden University Medical Center, Leiden, The Netherlands.
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12
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Jeba Derwin D, Tamil Selvi S, Jeba Singh O, Priestly Shan B. A novel automated system of discriminating Microaneurysms in fundus images. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101839] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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13
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Zhao L, Ren H, Zhang J, Cao Y, Wang Y, Meng D, Wu Y, Zhang R, Zou Y, Xu H, Li L, Zhang J, Cooper ME, Tong N, Liu F. DIABETIC RETINOPATHY, CLASSIFIED USING THE LESION-AWARE DEEP LEARNING SYSTEM, PREDICTS DIABETIC END-STAGE RENAL DISEASE IN CHINESE PATIENTS. Endocr Pract 2020; 26:429-443. [PMID: 31968187 DOI: 10.4158/ep-2019-0512] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Objective: To characterize the relationship between diabetic retinopathy (DR) and diabetic nephropathy (DN) in Chinese patients and to determine whether the severity of DR predicts end-stage renal disease (ESRD). Methods: Bilateral fundic photographs of 91 Chinese type 2 diabetic patients with biopsy-confirmed DN, not in ESRD stage, were obtained at the time of renal biopsy in this longitudinal study. The baseline severity of DR was determined using the Lesion-aware Deep Learning System (RetinalNET) in an open framework for deep learning and was graded using the Early Treatment Diabetic Retinopathy Study severity scale. Cox proportional hazard models were used to estimate the hazard ratio (HR) for the effect of the severity of diabetic retinopathy on ESRD. Results: During a median follow-up of 15 months, 25 patients progressed to ESRD. The severity of retinopathy at the time of biopsy was a prognostic factor for progression to ESRD (HR 2.18, 95% confidence interval 1.05 to 4.53, P = .04). At baseline, more severe retinopathy was associated with poor renal function, and more severe glomerular lesions. However, 30% of patients with mild retinopathy and severe glomerular lesions had higher low-density lipo-protein-cholesterol and more severe proteinuria than those with mild glomerular lesions. Additionally, 3% of patients with severe retinopathy and mild glomerular changes were more likely to have had diabetes a long time than those with severe glomerular lesions. Conclusion: Although the severity of DR predicted diabetic ESRD in patients with type 2 diabetes mellitus and DN, the severities of DR and DN were not always consistent, especially in patients with mild retinopathy or microalbuminuria. Abbreviations: CI = confidence interval; DM = diabetic mellitus; DN = diabetic nephropathy; DR = diabetic retinopathy; eGFR = estimated glomerular filtration rate; ESRD = end-stage renal disease; HbA1c = hemoglobin A1c; HR = hazard ratio; NPDR = nonproliferative diabetic retinopathy; PDR = proliferative diabetic retinopathy; SBP = systolic blood pressure; T2DM = type 2 diabetes mellitus; VEGF = vascular endothelial growth factor.
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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: 6.3] [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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15
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16
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Randive SN, Senapati RK, Rahulkar AD. A review on computer-aided recent developments for automatic detection of diabetic retinopathy. J Med Eng Technol 2019; 43:87-99. [PMID: 31198073 DOI: 10.1080/03091902.2019.1576790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.
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Affiliation(s)
- Santosh Nagnath Randive
- a Department of Electronics & Communication Engineering , Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram , Guntur , Andhra Pradesh , India
| | - Ranjan K Senapati
- a Department of Electronics & Communication Engineering , Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram , Guntur , Andhra Pradesh , India
| | - Amol D Rahulkar
- b Department of Electrical and Electronics Engineering , National Institute of Technology , Goa , India
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S K, D M. Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images. J Med Syst 2019; 43:173. [PMID: 31069550 DOI: 10.1007/s10916-019-1313-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 04/25/2019] [Indexed: 12/29/2022]
Abstract
Diabetes is characterized by constant high level of blood glucose. The human body needs to maintain insulin at very constrict range. The patients who are all affected by diabetes for a long time affected by eye disease called Diabetic Retinopathy (DR). The retinal landmarks namely Optic disc is predicted and masked to decrease the false positive in the exudates detection. The abnormalities like Exudates, Microaneurysms and Hemorrhages are segmented to classify the various stages of DR. The proposed approach is employed to separate the landmarks of retina and lesions of retina for the classification of stages of DR. The segmentation algorithms like Gabor double-sided hysteresis thresholding, maximum intensity variation, inverse surface adaptive thresholding, multi-agent approach and toboggan segmentation are used to detect and segment BVs, ODs, EXs, MAs and HAs. The feature vector formation and machine learning algorithm used to classify the various stages of DR are evaluated using images available in various retinal databases, and their performance measures are presented in this paper.
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Affiliation(s)
- Karkuzhali S
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education ( Deemed to be University), Srivilliputtur, Tamilnadu, India.
| | - Manimegalai D
- Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India
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18
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A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative. Comput Med Imaging Graph 2019; 73:11-18. [PMID: 30784984 DOI: 10.1016/j.compmedimag.2019.01.007] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/14/2018] [Accepted: 01/23/2019] [Indexed: 12/29/2022]
Abstract
This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects. At the feature selection/extraction stage, an independent component analysis (ICA) approach is used in order to reduce the dimensionality. Finally, Naive Bayes and random forest classifiers are used for the classification task. This novel image-based approach is applied on 1024 knee X-ray images from the public database OsteoArthritis Initiative (OAI). The results show that the proposed system has a good predictive classification rate for OA detection (82.98% for accuracy, 87.15% for sensitivity and up to 80.65% for specificity).
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Khojasteh P, Aliahmad B, Kumar DK. Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms. BMC Ophthalmol 2018; 18:288. [PMID: 30400869 PMCID: PMC6219077 DOI: 10.1186/s12886-018-0954-4] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 10/23/2018] [Indexed: 12/29/2022] Open
Abstract
Background Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity. Methods This study has proposed an alternate method using probabilistic output from Convolution neural network to automatically and simultaneously detect exudates, hemorrhages and microaneurysms. The method was evaluated using two approaches: patch and image-based analysis of the fundus images on two public databases: DIARETDB1 and e-Ophtha. The novelty of the proposed method is that the images were analyzed using probability maps generated by score values of the softmax layer instead of the use of the binary output. Results The sensitivity of the proposed approach was 0.96, 0.84 and 0.85 for detection of exudates, hemorrhages and microaneurysms, respectively when considering patch-based analysis. The results show overall accuracy for DIARETDB1 was 97.3% and 86.6% for e-Ophtha. The error rate for image-based analysis was also significantly reduced when compared with other works. Conclusion The proposed method provides the framework for convolution neural network-based analysis of fundus images to identify exudates, hemorrhages, and microaneurysms. It obtained accuracy and sensitivity which were significantly better than the reported studies and makes it suitable for automatic diabetic retinopathy signs detection.
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Affiliation(s)
- Parham Khojasteh
- Biosignal Lab, School of Engineering, RMIT University, Melbourne, Australia
| | - Behzad Aliahmad
- Biosignal Lab, School of Engineering, RMIT University, Melbourne, Australia
| | - Dinesh K Kumar
- Biosignal Lab, School of Engineering, RMIT University, Melbourne, 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: 4.3] [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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21
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Passaglia CL, Arvaneh T, Greenberg E, Richards D, Madow B. Automated Method of Grading Vitreous Haze in Patients With Uveitis for Clinical Trials. Transl Vis Sci Technol 2018; 7:10. [PMID: 29600118 PMCID: PMC5868860 DOI: 10.1167/tvst.7.2.10] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 02/11/2018] [Indexed: 12/29/2022] Open
Abstract
Purpose Uveitis is associated with accumulation of exudate in the vitreous, which reduces fundus visibility. The condition is assessed in patients by subjectively matching fundus photographs to a six-level (NIH) or nine-level (Miami) haze scale. This study aimed to develop an objective method of assessing vitreous haze. Methods An image-processing algorithm was designed that quantifies vitreous haze via high-pass filtering, entropy analysis, and power spectrum integration. The algorithm was refined using nine published photographs that represent incremental levels of fundus blur and applied without further refinement to 120 random fundus photographs from a uveitis image library. Computed scores were compared against the grades of two trained readers of vitreous haze and against acutance, a generic measure of image clarity, using Cohen's κ and Gwet's AC statistics. Results Exact agreement between algorithm scores and reader grades was substantial for both NIH and Miami scales (κ = 0.61 and 0.67, AC = 0.82 and 0.92). Within-one (κ = 0.78 and 0.82) and within-two (κ = 0.80 and 0.84) levels of agreement were almost perfect. The correspondence was comparable to that between readers. Whereas, exact (κ = 0.45 and 0.44, AC = 0.73 and 0.75), within-one (κ = 0.69 and 0.68), and within-two (κ = 0.73 and 0.72) levels of agreement for the two scales were moderate to substantial for acutance calculations. Conclusions The computer algorithm produces a quantitative measure of vitreous haze that correlates strongly with the perception of expert graders. Translational Relevance The work offers a rapid, unbiased, standardized means of assessing vitreous haze for clinical and telemedical monitoring of uveitis patients.
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Affiliation(s)
- Christopher L Passaglia
- Department of Chemical and Biomedical Engineering, University of South Florida, Tampa, FL, USA.,Department of Ophthalmology, University of South Florida, Tampa, FL, USA
| | - Tia Arvaneh
- Department of Chemical and Biomedical Engineering, University of South Florida, Tampa, FL, USA
| | - Erin Greenberg
- Department of Ophthalmology, University of South Florida, Tampa, FL, USA
| | - David Richards
- Department of Ophthalmology, University of South Florida, Tampa, FL, USA
| | - Brian Madow
- Department of Ophthalmology, University of South Florida, Tampa, FL, USA
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22
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Béouche-Hélias B, Helbert D, de Malézieu C, Leveziel N, Fernandez-Maloigne C. Neovascularization detection in diabetic retinopathy from fluorescein angiograms. J Med Imaging (Bellingham) 2017; 4:044503. [PMID: 29181431 DOI: 10.1117/1.jmi.4.4.044503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 10/27/2017] [Indexed: 12/29/2022] Open
Abstract
Although a lot of work has been done on optical coherence tomography and color images in order to detect and quantify diseases such as diabetic retinopathy, exudates, or neovascularizations, none of them is able to evaluate the diffusion of the neovascularizations in retinas. Our work has been to develop a tool that is able to quantify a neovascularization and the fluorescein leakage during an angiography. The proposed method has been developed following a clinical trial protocol; images are taken by a Spectralis (Heidelberg Engineering). Detections are done using a supervised classification using specific features. Images and their detected neovascularizations are then spatially matched by an image registration. We compute the expansion speed of the liquid that we call diffusion index. This last one specifies the state of the disease, permits indication of the activity of neovascularizations, and allows a follow-up of patients. The method proposed in this paper has been built to be robust, even with laser impacts, to compute a diffusion index.
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Affiliation(s)
| | - David Helbert
- XLIM-ASALI University of Poitiers, UMR CNRS 7252, Futuroscope Chasseneuil Cedex, France
| | | | - Nicolas Leveziel
- CHU Poitiers, Department of Ophthalmology, Poitiers Cedex, France
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Abdel-Hamid L, El-Rafei A, Michelson G. No-reference quality index for color retinal images. Comput Biol Med 2017; 90:68-75. [PMID: 28957660 DOI: 10.1016/j.compbiomed.2017.09.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 09/09/2017] [Accepted: 09/18/2017] [Indexed: 12/29/2022]
Abstract
Retinal image quality assessment (RIQA) is essential to assure that the images investigated by ophthalmologists or automatic systems are suitable for reliable medical diagnosis. Measure-based RIQA techniques have several advantages over the more commonly used binary classification-based RIQA methods. Numeric quality measures can aid ophthalmologists in associating a degree of confidence to the diagnosis performed through the investigation of a certain retinal image. Moreover, a numeric quality index can provide a mean for identifying the degree of enhancement required as well as to evaluate and compare the improvement achieved by enhancement techniques. In this work, a no-reference retinal image sharpness numeric quality index is introduced that is computed from the wavelet decomposition of the images. In order to account for the obscured retinal structures in unevenly illuminated image regions, the quality index is modified by a homogeneity parameter calculated from the previously introduced retinal image saturation channel. The proposed quality index was validated and tested on two datasets having different resolutions and quality grades. A strong (Spearman's coefficient > 0.8) and statistically highly significant (p-value < 0.001) correlation was found between the introduced quality index and the subjective human scores for the two different datasets. Moreover, multiclass classification using solely the devised retinal image quality index as a feature resulted in a micro average F-measure of 0.84 and 0.95 using the high and low resolution datasets, respectively. Several comparisons with other retinal image quality measures demonstrated superiority of the proposed quality index in both performance and speed.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Misr International University, Faculty of Engineering, Dept. of Electronics and Communication, Cairo, Egypt.
| | - Ahmed El-Rafei
- Ain Shams University, Faculty of Engineering, Dept. of Engineering Physics and Mathematics, Cairo, Egypt
| | - Georg Michelson
- Friedrich-Alexander University of Erlangen-Nuremberg, Dept. of Ophthalmology, Erlangen, Germany; Talkingeyes & More GmbH, Erlangen, Germany
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A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:1861645. [PMID: 28194221 PMCID: PMC5282461 DOI: 10.1155/2017/1861645] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 11/25/2016] [Accepted: 12/15/2016] [Indexed: 12/29/2022]
Abstract
EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.
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Abdel-Hamid L, El-Rafei A, El-Ramly S, Michelson G, Hornegger J. Retinal image quality assessment based on image clarity and content. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:96007. [PMID: 27637005 DOI: 10.1117/1.jbo.21.9.096007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Misr International University, Department of Electronics and Communication, Faculty of Engineering, Ismalia Road km28, Cairo, Egypt
| | - Ahmed El-Rafei
- Ain Shams University, Department of Engineering Physics and Mathematics, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt
| | - Salwa El-Ramly
- Ain Shams University, Department of Electronics and Communication, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt
| | - Georg Michelson
- Friedrich-Alexander University of Erlangen-Nuremberg, Department of Ophthalmology, Schwabachanlage 6, Erlangen 91054, GermanyeTalkingeyes & More GmbH, Medical Valley Center, Erlangen 91052, Germany
| | - Joachim Hornegger
- Friedrich-Alexander University of Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Martensstr. 3, Erlangen 91058, Germany
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Imani E, Pourreza HR. A novel method for retinal exudate segmentation using signal separation algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 133:195-205. [PMID: 27393810 DOI: 10.1016/j.cmpb.2016.05.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2015] [Revised: 04/24/2016] [Accepted: 05/27/2016] [Indexed: 06/06/2023]
Abstract
Diabetic retinopathy is one of the major causes of blindness in the world. Early diagnosis of this disease is vital to the prevention of visual loss. The analysis of retinal lesions such as exudates, microaneurysms and hemorrhages is a prerequisite to detect diabetic disorders such as diabetic retinopathy and macular edema in fundus images. This paper presents an automatic method for the detection of retinal exudates. The novelty of this method lies in the use of Morphological Component Analysis (MCA) algorithm to separate lesions from normal retinal structures to facilitate the detection process. In the first stage, vessels are separated from lesions using the MCA algorithm with appropriate dictionaries. Then, the lesion part of retinal image is prepared for the detection of exudate regions. The final exudate map is created using dynamic thresholding and mathematical morphologies. Performance of the proposed method is measured on the three publicly available DiaretDB, HEI-MED and e-ophtha datasets. Accordingly, the AUC of 0.961 and 0.948 and 0.937 is achieved respectively, which are greater than most of the state-of-the-art methods.
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Affiliation(s)
- Elaheh Imani
- Machine Vision Lab., Ferdowsi University of Mashhad, Mashhad, Iran
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Fong S, Wang D, Fiaidhi J, Mohammed S, Chen L, Ling L. WITHDRAWN: Clinical pathways inference from decision rules by hybrid stream mining and fuzzy unordered rule induction strategy. Comput Med Imaging Graph 2016:S0895-6111(16)30065-9. [PMID: 27666793 DOI: 10.1016/j.compmedimag.2016.06.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 06/17/2016] [Accepted: 06/27/2016] [Indexed: 12/29/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Simon Fong
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | - Dana Wang
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | - Jinan Fiaidhi
- Department of Computer Science, Lakehead University, Thunder Bay, Canada
| | - Sabah Mohammed
- Department of Computer Science, Lakehead University, Thunder Bay, Canada
| | - Libo Chen
- Department of Endocrinology, Guangdong Medical College Affiliated Shenzhen Nanshan Hospital, Shenzhen 518052, China.
| | - Li Ling
- Department of Endocrinology, Guangdong Medical College Affiliated Shenzhen Nanshan Hospital, Shenzhen 518052, China.
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Quellec G, Lamard M, Erginay A, Chabouis A, Massin P, Cochener B, Cazuguel G. Automatic detection of referral patients due to retinal pathologies through data mining. Med Image Anal 2015; 29:47-64. [PMID: 26774796 DOI: 10.1016/j.media.2015.12.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 12/17/2015] [Accepted: 12/18/2015] [Indexed: 12/29/2022]
Abstract
With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies.
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Affiliation(s)
| | - Mathieu Lamard
- Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France; Univ Bretagne Occidentale, F-29200 Brest, France
| | - Ali Erginay
- Service d'Ophtalmologie, Hôpital Lariboisière, APHP, F-75475 Paris, France
| | - Agnès Chabouis
- Service d'Ophtalmologie, Hôpital Lariboisière, APHP, F-75475 Paris, France
| | - Pascale Massin
- Service d'Ophtalmologie, Hôpital Lariboisière, APHP, F-75475 Paris, France
| | - Béatrice Cochener
- Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France; Univ Bretagne Occidentale, F-29200 Brest, France; Service d'Ophtalmologie, CHRU Brest, F-29200 Brest, France
| | - Guy Cazuguel
- Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France; Institut Mines-Telecom; Telecom Bretagne; UEB; Dpt ITI, F-29200 Brest, France
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