1
|
Starovoitov VV, Golub YI, Lukashevich MM. A Universal Retinal Image Template for Automated Screening of Diabetic Retinopathy. PATTERN RECOGNITION AND IMAGE ANALYSIS 2022. [DOI: 10.1134/s1054661822020195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
2
|
Huang X, Wang H, She C, Feng J, Liu X, Hu X, Chen L, Tao Y. Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy. Front Endocrinol (Lausanne) 2022; 13:946915. [PMID: 36246896 PMCID: PMC9559815 DOI: 10.3389/fendo.2022.946915] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.
Collapse
Affiliation(s)
- Xuan Huang
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Medical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Hui Wang
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Chongyang She
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jing Feng
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Xuhui Liu
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Xiaofeng Hu
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Li Chen
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Yong Tao
- Department of Ophthalmology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- *Correspondence: Yong Tao,
| |
Collapse
|
3
|
Shigueoka LS, Jammal AA, Medeiros FA, Costa VP. Artificial Intelligence in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
4
|
Kanclerz P, Tuuminen R, Khoramnia R. Imaging Modalities Employed in Diabetic Retinopathy Screening: A Review and Meta-Analysis. Diagnostics (Basel) 2021; 11:diagnostics11101802. [PMID: 34679501 PMCID: PMC8535170 DOI: 10.3390/diagnostics11101802] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Urbanization has caused dramatic changes in lifestyle, and these rapid transitions have led to an increased risk of noncommunicable diseases, such as type 2 diabetes. In terms of cost-effectiveness, screening for diabetic retinopathy is a critical aspect in diabetes management. The aim of this study was to review the imaging modalities employed for retinal examination in diabetic retinopathy screening. METHODS The PubMed and Web of Science databases were the main sources used to investigate the medical literature. An extensive search was performed to identify relevant articles concerning "imaging", "diabetic retinopathy" and "screening" up to 1 June 2021. Imaging techniques were divided into the following: (i) mydriatic fundus photography, (ii) non-mydriatic fundus photography, (iii) smartphone-based imaging, and (iv) ultrawide-field imaging. A meta-analysis was performed to analyze the performance and technical failure rate of each method. RESULTS The technical failure rates for mydriatic and non-mydriatic digital fundus photography, smartphone-based and ultrawide-field imaging were 3.4% (95% CI: 2.3-4.6%), 12.1% (95% CI: 5.4-18.7%), 5.3% (95% CI: 1.5-9.0%) and 2.2% (95% CI: 0.3-4.0%), respectively. The rate was significantly different between all analyzed techniques (p < 0.001), and the overall failure rate was 6.6% (4.9-8.3%; I2 = 97.2%). The publication bias factor for smartphone-based imaging was significantly higher than for mydriatic digital fundus photography and non-mydriatic digital fundus photography (b = -8.61, b = -2.59 and b = -7.03, respectively; p < 0.001). Ultrawide-field imaging studies were excluded from the final sensitivity/specificity analysis, as the total number of patients included was too small. CONCLUSIONS Regardless of the type of the device used, retinal photographs should be taken on eyes with dilated pupils, unless contraindicated, as this setting decreases the rate of ungradable images. Smartphone-based and ultrawide-field imaging may become potential alternative methods for optimized DR screening; however, there is not yet enough evidence for these techniques to displace mydriatic fundus photography.
Collapse
Affiliation(s)
- Piotr Kanclerz
- Hygeia Clinic, 80-286 Gdańsk, Poland
- Helsinki Retina Research Group, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
- Correspondence: ; Tel./Fax: +48-58-776-4046
| | - Raimo Tuuminen
- Helsinki Retina Research Group, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
- Eye Centre, Kymenlaakso Central Hospital, 48100 Kotka, Finland
| | - Ramin Khoramnia
- The David J. Apple International Laboratory for Ocular Pathology, Department of Ophthalmology, University of Heidelberg, 69120 Heidelberg, Germany;
| |
Collapse
|
5
|
A review of diabetic retinopathy: Datasets, approaches, evaluation metrics and future trends. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
6
|
Shigueoka LS, Jammal AA, Medeiros FA, Costa VP. Artificial Intelligence in Ophthalmology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_201-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
7
|
Xie R, Liu J, Cao R, Qiu CS, Duan J, Garibaldi J, Qiu G. End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:116-128. [PMID: 32915729 DOI: 10.1109/tmi.2020.3023254] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.
Collapse
|
8
|
Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05060-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
9
|
Zapata MA, Royo-Fibla D, Font O, Vela JI, Marcantonio I, Moya-Sánchez EU, Sánchez-Pérez A, Garcia-Gasulla D, Cortés U, Ayguadé E, Labarta J. Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma. Clin Ophthalmol 2020; 14:419-429. [PMID: 32103888 PMCID: PMC7025650 DOI: 10.2147/opth.s235751] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 01/22/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose To assess the performance of deep learning algorithms for different tasks in retinal fundus images: (1) detection of retinal fundus images versus optical coherence tomography (OCT) or other images, (2) evaluation of good quality retinal fundus images, (3) distinction between right eye (OD) and left eye (OS) retinal fundus images,(4) detection of age-related macular degeneration (AMD) and (5) detection of referable glaucomatous optic neuropathy (GON). Patients and Methods Five algorithms were designed. Retrospective study from a database of 306,302 images, Optretina’s tagged dataset. Three different ophthalmologists, all retinal specialists, classified all images. The dataset was split per patient in a training (80%) and testing (20%) splits. Three different CNN architectures were employed, two of which were custom designed to minimize the number of parameters with minimal impact on its accuracy. Main outcome measure was area under the curve (AUC) with accuracy, sensitivity and specificity. Results Determination of retinal fundus image had AUC of 0.979 with an accuracy of 96% (sensitivity 97.7%, specificity 92.4%). Determination of good quality retinal fundus image had AUC of 0.947, accuracy 91.8% (sensitivity 96.9%, specificity 81.8%). Algorithm for OD/OS had AUC 0.989, accuracy 97.4%. AMD had AUC of 0.936, accuracy 86.3% (sensitivity 90.2% specificity 82.5%), GON had AUC of 0.863, accuracy 80.2% (sensitivity 76.8%, specificity 83.8%). Conclusion Deep learning algorithms can differentiate a retinal fundus image from other images. Algorithms can evaluate the quality of an image, discriminate between right or left eye and detect the presence of AMD and GON with a high level of accuracy, sensitivity and specificity.
Collapse
Affiliation(s)
| | | | | | - José Ignacio Vela
- Ophthalmology Department, Hospital de la Santa Creu I de Sant Pau, Barcelona 08041, Spain.,Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Barcelona, Spain
| | - Ivanna Marcantonio
- Ophthalmology Department, Hospital de la Santa Creu I de Sant Pau, Barcelona 08041, Spain.,Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Barcelona, Spain
| | - Eduardo Ulises Moya-Sánchez
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Universidad Autónoma de Guadalajara - Postgrado en Ciencias Computacionales, Guadalajara, Mexico
| | - Abraham Sánchez-Pérez
- Universidad Autónoma de Guadalajara - Postgrado en Ciencias Computacionales, Guadalajara, Mexico
| | | | - Ulises Cortés
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Universitat Politècnica de Catalunya - BarcelonaTECH, Campus Nord, Barcelona, Spain
| | - Eduard Ayguadé
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Universitat Politècnica de Catalunya - BarcelonaTECH, Campus Nord, Barcelona, Spain
| | - Jesus Labarta
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Universitat Politècnica de Catalunya - BarcelonaTECH, Campus Nord, Barcelona, Spain
| |
Collapse
|
10
|
Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy. Symmetry (Basel) 2019. [DOI: 10.3390/sym11060749] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.
Collapse
|
11
|
Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F. Retinal image analysis for disease screening through local tetra patterns. Comput Biol Med 2018; 102:200-210. [DOI: 10.1016/j.compbiomed.2018.09.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/11/2018] [Accepted: 09/26/2018] [Indexed: 11/27/2022]
|
12
|
Screening und Management retinaler Erkrankungen mittels digitaler Medizin. Ophthalmologe 2018; 115:728-736. [DOI: 10.1007/s00347-018-0752-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
13
|
Elloumi Y, Akil M, Kehtarnavaz N. A mobile computer aided system for optic nerve head detection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:139-148. [PMID: 29903480 DOI: 10.1016/j.cmpb.2018.05.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 04/17/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The detection of optic nerve head (ONH) in retinal fundus images plays a key role in identifying Diabetic Retinopathy (DR) as well as other abnormal conditions in eye examinations. This paper presents a method and its associated software towards the development of an Android smartphone app based on a previously developed ONH detection algorithm. The development of this app and the use of the d-Eye lens which can be snapped onto a smartphone provide a mobile and cost-effective computer-aided diagnosis (CAD) system in ophthalmology. In particular, this CAD system would allow eye examination to be conducted in remote locations with limited access to clinical facilities. METHODS A pre-processing step is first carried out to enable the ONH detection on the smartphone platform. Then, the optimization steps taken to run the algorithm in a computationally and memory efficient manner on the smartphone platform is discussed. RESULTS The smartphone code of the ONH detection algorithm was applied to the STARE and DRIVE databases resulting in about 96% and 100% detection rates, respectively, with an average execution time of about 2 s and 1.3 s. In addition, two other databases captured by the d-Eye and iExaminer snap-on lenses for smartphones were considered resulting in about 93% and 91% detection rates, respectively, with an average execution time of about 2.7 s and 2.2 s, respectively.
Collapse
Affiliation(s)
- Yaroub Elloumi
- Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France; Medical Technology and Image Processing Laboratory, Faculty of medicine, University of Monastir, Tunisia.
| | - Mohamed Akil
- Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France
| | - Nasser Kehtarnavaz
- Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
| |
Collapse
|
14
|
Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67:1-29. [PMID: 30076935 DOI: 10.1016/j.preteyeres.2018.07.004] [Citation(s) in RCA: 352] [Impact Index Per Article: 58.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 07/24/2018] [Accepted: 07/31/2018] [Indexed: 02/08/2023]
Abstract
Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.
Collapse
Affiliation(s)
- Ursula Schmidt-Erfurth
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | - Amir Sadeghipour
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| | - Bianca S Gerendas
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| | - Sebastian M Waldstein
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| | - Hrvoje Bogunović
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| |
Collapse
|