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Inouye K, Petrosyan A, Moskalensky L, Thankam FG. Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology. Exp Eye Res 2024; 245:109954. [PMID: 38838975 DOI: 10.1016/j.exer.2024.109954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 04/09/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. Hyperlipidemic lesions and occlusions to the vessels supplying the retina result in permanent blindness, necessitating prompt detection and treatment. Retinal vascular occlusion is diagnosed using different imaging modalities, including optical coherence tomography angiography. These diagnostic techniques obtain images representing the blood flow through the retinal vessels, providing an opportunity for AI to utilize image recognition to detect blockages and abnormalities before patients present with symptoms. AI is already being used as a non-invasive method to detect retinal vascular occlusions and other vascular pathology, as well as predict treatment outcomes. As providers see an increase in patients presenting with new retinal vascular occlusions, the use of AI to detect and treat these conditions has the potential to improve patient outcomes and reduce the financial burden on the healthcare system. This article comprehends the implications of AI in the current management strategies of retinal vascular occlusion (RVO) in hyperlipidemia and the recent developments of AI technology in the management of ocular diseases.
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Affiliation(s)
- Keiko Inouye
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Aelita Petrosyan
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Liana Moskalensky
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA
| | - Finosh G Thankam
- Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, USA.
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2
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Guo Y, Hormel TT, Gao M, You Q, Wang J, Flaxel CJ, Bailey ST, Hwang TS, Jia Y. Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network. Transl Vis Sci Technol 2024; 13:15. [PMID: 39023443 PMCID: PMC11262538 DOI: 10.1167/tvst.13.7.15] [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: 01/16/2024] [Accepted: 06/05/2024] [Indexed: 07/20/2024] Open
Abstract
Purpose To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA). Methods This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset. Results The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%). Conclusions A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy. Translational Relevance Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.
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Affiliation(s)
- Yukun Guo
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Tristan T. Hormel
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Min Gao
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Qisheng You
- Kresge Eye Institute, Wayne State University, Detroit, MI, USA
| | - Jie Wang
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | | | - Steven T. Bailey
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Thomas S. Hwang
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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Sorrentino FS, Gardini L, Fontana L, Musa M, Gabai A, Maniaci A, Lavalle S, D’Esposito F, Russo A, Longo A, Surico PL, Gagliano C, Zeppieri M. Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence. J Pers Med 2024; 14:690. [PMID: 39063944 PMCID: PMC11278069 DOI: 10.3390/jpm14070690] [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: 05/30/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. AIM This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). METHODS The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. RESULTS Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. CONCLUSION A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.
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Affiliation(s)
| | - Lorenzo Gardini
- Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy; (F.S.S.)
| | - Luigi Fontana
- Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Andrea Gabai
- Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy
| | - Antonino Maniaci
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Salvatore Lavalle
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Fabiana D’Esposito
- Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy
| | - Andrea Russo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Antonio Longo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Pier Luigi Surico
- Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
| | - Caterina Gagliano
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
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Masayoshi K, Katada Y, Ozawa N, Ibuki M, Negishi K, Kurihara T. Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography. Sci Rep 2024; 14:10801. [PMID: 38734727 PMCID: PMC11088618 DOI: 10.1038/s41598-024-61561-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024] Open
Abstract
The non-perfusion area (NPA) of the retina is an important indicator in the visual prognosis of patients with branch retinal vein occlusion (BRVO). However, the current evaluation method of NPA, fluorescein angiography (FA), is invasive and burdensome. In this study, we examined the use of deep learning models for detecting NPA in color fundus images, bypassing the need for FA, and we also investigated the utility of synthetic FA generated from color fundus images. The models were evaluated using the Dice score and Monte Carlo dropout uncertainty. We retrospectively collected 403 sets of color fundus and FA images from 319 BRVO patients. We trained three deep learning models on FA, color fundus images, and synthetic FA. As a result, though the FA model achieved the highest score, the other two models also performed comparably. We found no statistical significance in median Dice scores between the models. However, the color fundus model showed significantly higher uncertainty than the other models (p < 0.05). In conclusion, deep learning models can detect NPAs from color fundus images with reasonable accuracy, though with somewhat less prediction stability. Synthetic FA stabilizes the prediction and reduces misleading uncertainty estimates by enhancing image quality.
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Affiliation(s)
- Kanato Masayoshi
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Yusaku Katada
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Nobuhiro Ozawa
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Mari Ibuki
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Kazuno Negishi
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan
| | - Toshihide Kurihara
- Laboratory of Photobiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, Japan.
- Department of Ophthalmology, Keio University School of Medicine, Shinanomachi, Shinjuku-Ku, Tokyo, Japan.
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Parmar UPS, Surico PL, Singh RB, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:527. [PMID: 38674173 PMCID: PMC11052176 DOI: 10.3390/medicina60040527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.
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Affiliation(s)
| | - Pier Luigi Surico
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Rohan Bir Singh
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Francesco Romano
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Carlo Salati
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Leopoldo Spadea
- Eye Clinic, Policlinico Umberto I, “Sapienza” University of Rome, 00142 Rome, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Caterina Gagliano
- Faculty of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Tommaso Mori
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Department of Ophthalmology, University of California San Diego, La Jolla, CA 92122, USA
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
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6
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Ren X, Feng W, Ran R, Gao Y, Lin Y, Fu X, Tao Y, Wang T, Wang B, Ju L, Chen Y, He L, Xi W, Liu X, Ge Z, Zhang M. Artificial intelligence to distinguish retinal vein occlusion patients using color fundus photographs. Eye (Lond) 2023; 37:2026-2032. [PMID: 36302974 PMCID: PMC10333217 DOI: 10.1038/s41433-022-02239-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 08/04/2022] [Accepted: 09/02/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Our aim is to establish an AI model for distinguishing color fundus photographs (CFP) of RVO patients from normal individuals. METHODS The training dataset included 2013 CFP from fellow eyes of RVO patients and 8536 age- and gender-matched normal CFP. Model performance was assessed in two independent testing datasets. We evaluated the performance of the AI model using the area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity, and confusion matrices. We further explained the probable clinical relevance of the AI by extracting and comparing features of the retinal images. RESULTS Our model achieved an average AUC was 0.9866 (95% CI: 0.9805-0.9918), accuracy was 0.9534 (95% CI: 0.9421-0.9639), precision was 0.9123 (95% CI: 0.8784-9453), specificity was 0.9810 (95% CI: 0.9729-0.9884), and sensitivity was 0.8367 (95% CI: 0.7953-0.8756) for identifying fundus images of RVO patients in training dataset. In independent external datasets 1, the AUC of the RVO group was 0.8102 (95% CI: 0.7979-0.8226), the accuracy of 0.7752 (95% CI: 0.7633-0.7875), the precision of 0.7041 (95% CI: 0.6873-0.7211), specificity of 0.6499 (95% CI: 0.6305-0.6679) and sensitivity of 0.9124 (95% CI: 0.9004-0.9241) for RVO group. There were significant differences in retinal arteriovenous ratio, optic cup to optic disc ratio, and optic disc tilt angle (p = 0.001, p = 0.0001, and p = 0.0001, respectively) between the two groups in training dataset. CONCLUSION We trained an AI model to classify color fundus photographs of RVO patients with stable performance both in internal and external datasets. This may be of great importance for risk prediction in patients with retinal venous occlusion.
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Affiliation(s)
- Xiang Ren
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Wei Feng
- Beijing Airdoc Technology Co Ltd, Beijing, China
| | - Ruijin Ran
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
- Minda Hospital of Hubei Minzu University, Enshi, China
| | - Yunxia Gao
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Yu Lin
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Xiangyu Fu
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Yunhan Tao
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Ting Wang
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
- Research Laboratory of Ophthalmology and Vision Sciences, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China
| | - Bin Wang
- Beijing Airdoc Technology Co Ltd, Beijing, China
| | - Lie Ju
- Beijing Airdoc Technology Co Ltd, Beijing, China
- ECSE, Faculty of Engineering, Monash University, Melbourne, VIC, Australia
| | - Yuzhong Chen
- Beijing Airdoc Technology Co Ltd, Beijing, China
| | - Lanqing He
- Beijing Airdoc Technology Co Ltd, Beijing, China
| | - Wu Xi
- Chengdu Ikangguobin Health Examination Center Ltd, Chengdu, China
| | - Xiaorong Liu
- Chengdu Ikangguobin Health Examination Center Ltd, Chengdu, China
| | - Zongyuan Ge
- ECSE, Faculty of Engineering, Monash University, Melbourne, VIC, Australia
- eResearch Centre, Monash University, Melbourne, VIC, Australia
| | - Ming Zhang
- Department of Ophthalmology, Ophthalmic Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, P. R. China.
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Heindl LM, Li S, Ting DSW, Keane PA. Artificial intelligence in ophthalmological practice: when ideal meets reality. BMJ Open Ophthalmol 2023; 8:e001129. [PMID: 37493688 PMCID: PMC10255244 DOI: 10.1136/bmjophth-2022-001129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023] Open
Affiliation(s)
- Ludwig M Heindl
- Department of Ophthalmology, University of Cologne, Koln, Germany
| | - Senmao Li
- Department of Ophthalmology, University of Cologne, Koln, Germany
- Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
- Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Pearse A Keane
- Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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8
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Ong CJT, Wong MYZ, Cheong KX, Zhao J, Teo KYC, Tan TE. Optical Coherence Tomography Angiography in Retinal Vascular Disorders. Diagnostics (Basel) 2023; 13:diagnostics13091620. [PMID: 37175011 PMCID: PMC10178415 DOI: 10.3390/diagnostics13091620] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 05/15/2023] Open
Abstract
Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods-in particular, deep learning-show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders.
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Affiliation(s)
- Charles Jit Teng Ong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Mark Yu Zheng Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Kai Xiong Cheong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Jinzhi Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Kelvin Yi Chong Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore 169857, Singapore
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore 169857, Singapore
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9
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Le D, Abtahi M, Adejumo T, Ebrahimi B, K Dadzie A, Son T, Yao X. Deep learning for artery-vein classification in optical coherence tomography angiography. Exp Biol Med (Maywood) 2023; 248:747-761. [PMID: 37452729 PMCID: PMC10468646 DOI: 10.1177/15353702231181182] [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] [Indexed: 07/18/2023] Open
Abstract
Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal imaging. However, it cannot discern capillary-level structures due to the limited image contrast. As a functional extension of OCT modality, optical coherence tomography angiography (OCTA) is a non-invasive, label-free method for enhanced contrast visualization of retinal vasculatures at the capillary level. Recently differential artery-vein (AV) analysis in OCTA has been demonstrated to improve the sensitivity for staging of retinopathies. Therefore, AV classification is an essential step for disease detection and diagnosis. However, current methods for AV classification in OCTA have employed multiple imagers, that is, fundus photography and OCT, and complex algorithms, thereby making it difficult for clinical deployment. On the contrary, deep learning (DL) algorithms may be able to reduce computational complexity and automate AV classification. In this article, we summarize traditional AV classification methods, recent DL methods for AV classification in OCTA, and discuss methods for interpretability in DL models.
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Affiliation(s)
- David Le
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Mansour Abtahi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Tobiloba Adejumo
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Behrouz Ebrahimi
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Albert K Dadzie
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Taeyoon Son
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Xincheng Yao
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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10
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Cao S, Zhang R, Jiang A, Kuerban M, Wumaier A, Wu J, Xie K, Aizezi M, Tuersun A, Liang X, Chen R. Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening. Biomed Eng Online 2023; 22:38. [PMID: 37095516 PMCID: PMC10127070 DOI: 10.1186/s12938-023-01097-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 03/24/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment. METHODS A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening. RESULTS The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening. CONCLUSIONS In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.
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Affiliation(s)
- Shujuan Cao
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Rongpei Zhang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Aixin Jiang
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Mayila Kuerban
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Aizezi Wumaier
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Jianhua Wu
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Kaihua Xie
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Mireayi Aizezi
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Abudurexiti Tuersun
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Xuanwei Liang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China.
| | - Rongxin Chen
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China.
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11
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Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Front Cell Dev Biol 2023; 11:1168327. [PMID: 37056999 PMCID: PMC10086262 DOI: 10.3389/fcell.2023.1168327] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
| | - Yunfang Liu
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
| | - Ying Zhao
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
| | - Liya Zhang
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
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12
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Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13020326. [PMID: 36673135 PMCID: PMC9857993 DOI: 10.3390/diagnostics13020326] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.
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13
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Ciftci Kavaklioglu B, Erdman L, Goldenberg A, Kavaklioglu C, Alexander C, Oppermann HM, Patel A, Hossain S, Berenbaum T, Yau O, Yea C, Ly M, Costello F, Mah JK, Reginald A, Banwell B, Longoni G, Ann Yeh E. Machine learning classification of multiple sclerosis in children using optical coherence tomography. Mult Scler 2022; 28:2253-2262. [PMID: 35946086 DOI: 10.1177/13524585221112605] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. OBJECTIVE This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. METHODS This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. RESULTS Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. CONCLUSION This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.
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Affiliation(s)
- Beyza Ciftci Kavaklioglu
- Neuroscience and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada/Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Lauren Erdman
- Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada/Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Can Kavaklioglu
- Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada
| | - Cara Alexander
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Hannah M Oppermann
- Department of Computer Science, University of Toronto, Toronto, ON, Canada/Department of Information and Computing Sciences, Utrecht University, Utrecht, the Netherlands
| | - Amish Patel
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Soaad Hossain
- Department of Computer Science, University of Toronto, Toronto, ON, Canada/Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada/Environics Analytics, Toronto, ON, Canada
| | - Tara Berenbaum
- Division of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Olivia Yau
- Division of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Carmen Yea
- Division of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Mina Ly
- Division of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Fiona Costello
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada/Department of Surgery (Ophthalmology), University of Calgary, Calgary, AB, Canada
| | - Jean K Mah
- Department Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Arun Reginald
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada/Department of Ophthalmology and Vision Sciences, The Hospital for Sick Children, Toronto, ON, Canada
| | - Brenda Banwell
- Division of Neurology, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Giulia Longoni
- SickKids Research Institute, Neuroscience and Mental Health Program, The Hospital for Sick Children, Toronto, ON, Canada/Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada/Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - E Ann Yeh
- SickKids Research Institute, Neuroscience and Mental Health Program, The Hospital for Sick Children, Toronto, ON, Canada/Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada/Department of Pediatrics, University of Toronto, Toronto, ON, Canada.,Neuroscience and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada
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14
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Miao J, Yu J, Zou W, Su N, Peng Z, Wu X, Huang J, Fang Y, Yuan S, Xie P, Huang K, Chen Q, Hu Z, Liu Q. Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion. Front Med (Lausanne) 2022; 9:794045. [PMID: 35847781 PMCID: PMC9279621 DOI: 10.3389/fmed.2022.794045] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose To develop artificial intelligence (AI)-based deep learning (DL) models for automatically detecting the ischemia type and the non-perfusion area (NPA) from color fundus photographs (CFPs) of patients with branch retinal vein occlusion (BRVO). Methods This was a retrospective analysis of 274 CFPs from patients diagnosed with BRVO. All DL models were trained using a deep convolutional neural network (CNN) based on 45 degree CFPs covering the fovea and the optic disk. We first trained a DL algorithm to identify BRVO patients with or without the necessity of retinal photocoagulation from 219 CFPs and validated the algorithm on 55 CFPs. Next, we trained another DL algorithm to segment NPA from 104 CFPs and validated it on 29 CFPs, in which the NPA was manually delineated by 3 experienced ophthalmologists according to fundus fluorescein angiography. Both DL models have been cross-validated 5-fold. The recall, precision, accuracy, and area under the curve (AUC) were used to evaluate the DL models in comparison with three types of independent ophthalmologists of different seniority. Results In the first DL model, the recall, precision, accuracy, and area under the curve (AUC) were 0.75 ± 0.08, 0.80 ± 0.07, 0.79 ± 0.02, and 0.82 ± 0.03, respectively, for predicting the necessity of laser photocoagulation for BRVO CFPs. The second DL model was able to segment NPA in CFPs of BRVO with an AUC of 0.96 ± 0.02. The recall, precision, and accuracy for segmenting NPA was 0.74 ± 0.05, 0.87 ± 0.02, and 0.89 ± 0.02, respectively. The performance of the second DL model was nearly comparable with the senior doctors and significantly better than the residents. Conclusion These results indicate that the DL models can directly identify and segment retinal NPA from the CFPs of patients with BRVO, which can further guide laser photocoagulation. Further research is needed to identify NPA of the peripheral retina in BRVO, or other diseases, such as diabetic retinopathy.
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Affiliation(s)
- Jinxin Miao
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiale Yu
- School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, China
| | - Wenjun Zou
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Ophthalmology, The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Na Su
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zongyi Peng
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Xinjing Wu
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Junlong Huang
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yuan Fang
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ping Xie
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, China
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, China
| | - Zizhong Hu
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Qinghuai Liu
| | - Qinghuai Liu
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Zizhong Hu
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15
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Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes. Am J Ophthalmol 2022; 236:298-308. [PMID: 34780803 PMCID: PMC10042115 DOI: 10.1016/j.ajo.2021.11.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 11/07/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022]
Abstract
PURPOSE To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measurements and OCT retinal nerve fiber layer (RNFL) thickness measurements for classifying healthy and glaucomatous eyes. DESIGN Comparison of diagnostic approaches. METHODS A total of 130 eyes of 80 healthy individuals and 275 eyes of 185 glaucoma patients with optic nerve head (ONH) OCTA and OCT imaging were included. Classification performance of a VGG16 CNN trained and tested on entire en face 4.5 × 4.5-mm radial peripapillary capillary OCTA ONH images was compared to the performance of separate GBC models trained and tested on standard OCTA and OCT measurements. Five-fold cross-validation was used to test predictions for CNNs and GBCs. Areas under the precision recall curves (AUPRC) were calculated to control for training/test set size imbalance and were compared. RESULTS Adjusted AUPRCs for GBC models were 0.89 (95% CI = 0.82, 0.92) for whole image vessel density GBC, 0.89 (0.83, 0.92) for whole image capillary density GBC, 0.91 (0.88, 0.93) for combined whole image vessel and whole image capillary density GBC, and 0.93 (0.91, 095) for RNFL thickness GBC. The adjusted AUPRC using CNN analysis of en face vessel density images was 0.97 (0.95, 0.99) resulting in significantly improved classification compared to GBC OCTA-based results and GBC OCT-based results (P ≤ 0.01 for all comparisons). CONCLUSION Deep learning en face image analysis improves on feature-based GBC models for classifying healthy and glaucoma eyes.
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16
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Yasser I, Khalifa F, Abdeltawab H, Ghazal M, Sandhu HS, El-Baz A. Automated Diagnosis of Optical Coherence Tomography Angiography (OCTA) Based on Machine Learning Techniques. SENSORS 2022; 22:s22062342. [PMID: 35336513 PMCID: PMC8952189 DOI: 10.3390/s22062342] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/17/2022]
Abstract
Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.
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Affiliation(s)
- Ibrahim Yasser
- Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Fahmi Khalifa
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (F.K.); (H.A.); (H.S.S.)
| | - Hisham Abdeltawab
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (F.K.); (H.A.); (H.S.S.)
| | - Mohammed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates;
| | - Harpal Singh Sandhu
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (F.K.); (H.A.); (H.S.S.)
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (F.K.); (H.A.); (H.S.S.)
- Correspondence:
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17
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Zhu S, Lu B, Wang C, Wu M, Zheng B, Jiang Q, Wei R, Cao Q, Yang W. Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet. Front Med (Lausanne) 2022; 9:808402. [PMID: 35280876 PMCID: PMC8904395 DOI: 10.3389/fmed.2022.808402] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/12/2022] [Indexed: 11/21/2022] Open
Abstract
Purpose A six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases. Methods A total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained. The results from the six-category models in this study and the results from a five-category model in our previous study based on ResNet50 were compared. A total of 1,315 fundus images were used to test the models, the clinical diagnosis results and the diagnosis results of the two six-category models were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), 95% confidence interval, kappa and accuracy, and the receiver operator characteristic curves of the two six-category models were compared in the study. Results The diagnostic accuracy rate of EfficientNet-B4 model was 95.59%, the kappa value was 94.61%, and there was high diagnostic consistency. The AUC of the normal diagnosis and the five retinal diseases were all above 0.95. The sensitivity, specificity, and F1-score for the diagnosis of normal fundus images were 100, 99.9, and 99.83%, respectively. The specificity and F1-score for RVO diagnosis were 95.68, 98.61, and 93.09%, respectively. The sensitivity, specificity, and F1-score for high myopia diagnosis were 96.1, 99.6, and 97.37%, respectively. The sensitivity, specificity, and F1-score for glaucoma diagnosis were 97.62, 99.07, and 94.62%, respectively. The sensitivity, specificity, and F1-score for DR diagnosis were 90.76, 99.16, and 93.3%, respectively. The sensitivity, specificity, and F1-score for MD diagnosis were 92.27, 98.5, and 91.51%, respectively. Conclusion The EfficientNet-B4 model was used to design a six-category model of common retinal diseases. It can be used to diagnose the normal fundus and five common retinal diseases based on fundus images. It can help primary doctors in the screening for common retinal diseases, and give suitable suggestions and recommendations. Timely referral can improve the efficiency of diagnosis of eye diseases in rural areas and avoid delaying treatment.
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Affiliation(s)
- Shaojun Zhu
- School of Information Engineering, Huzhou University, Huzhou, China.,Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Bing Lu
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Chenghu Wang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Maonian Wu
- School of Information Engineering, Huzhou University, Huzhou, China.,Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou, China.,Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Qin Jiang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Ruili Wei
- Department of Ophthalmology, Shanghai Changzheng Hospital, Huangpu, China
| | - Qixin Cao
- Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China
| | - Weihua Yang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
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Masayoshi K, Katada Y, Ozawa N, Ibuki M, Negishi K, Kurihara T. Automatic segmentation of non-perfusion area from fluorescein angiography using deep learning with uncertainty estimation. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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19
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Chen Q, Yu WH, Lin S, Liu BS, Wang Y, Wei QJ, He XX, Ding F, Yang G, Chen YX, Li XR, Hu BJ. Artificial intelligence can assist with diagnosing retinal vein occlusion. Int J Ophthalmol 2021; 14:1895-1902. [PMID: 34926205 DOI: 10.18240/ijo.2021.12.13] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/26/2021] [Indexed: 12/23/2022] Open
Abstract
AIM To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models. RESULTS Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively. CONCLUSION DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.
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Affiliation(s)
- Qiong Chen
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Wei-Hong Yu
- Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100032, China
| | - Song Lin
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Bo-Shi Liu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Yong Wang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Qi-Jie Wei
- Vistel AI Lab, Visionary Intelligence Ltd, Beijing 100081, China
| | - Xi-Xi He
- Vistel AI Lab, Visionary Intelligence Ltd, Beijing 100081, China
| | - Fei Ding
- Vistel AI Lab, Visionary Intelligence Ltd, Beijing 100081, China.,School of Information, Renmin University of China, Beijing 100081, China
| | - Gang Yang
- School of Information, Renmin University of China, Beijing 100081, China
| | - You-Xin Chen
- Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100032, China
| | - Xiao-Rong Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
| | - Bo-Jie Hu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China
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20
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Kalra G, Zarranz-Ventura J, Chahal R, Bernal-Morales C, Lupidi M, Chhablani J. Optical coherence tomography (OCT) angiolytics: a review of OCT angiography quantitative biomarkers. Surv Ophthalmol 2021; 67:1118-1134. [PMID: 34748794 DOI: 10.1016/j.survophthal.2021.11.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 10/29/2021] [Accepted: 11/01/2021] [Indexed: 02/08/2023]
Abstract
Optical coherence tomography angiography (OCTA) provides a non-invasive method to obtain angiography of the chorioretinal vasculature leading to its recent widespread adoption. With a growing number of studies exploring the use of OCTA, various biomarkers quantifying the vascular characteristics have come to light. In the current report, we summarize the biomarkers currently described for retinal and choroidal vasculature using OCTA systems and the methods used to obtain them. Further, we present a critical review of these methods and key findings in common retinal diseases and appraise future directions, including applications of artificial intelligence in OCTA .
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Affiliation(s)
- Gagan Kalra
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA; Government Medical College and Hospital, Chandigarh, India
| | - Javier Zarranz-Ventura
- Institut Clinic d'Oftalmologia (ICOF) Hospital Clinic, Barcelona, Spain; Institut de Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Rutvi Chahal
- Government Medical College and Hospital, Chandigarh, India
| | - Carolina Bernal-Morales
- Institut Clinic d'Oftalmologia (ICOF) Hospital Clinic, Barcelona, Spain; Institut de Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marco Lupidi
- Department of Surgical and Biomedical Sciences, University of Perugia, S.Maria della Misericordia Hospital, Perugia, Italy
| | - Jay Chhablani
- University of Pittsburgh Medical Center Eye Center, University of Pittsburgh, Pittsburgh, PA, USA.
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21
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Chakroborty S, Gupta M, Devishamani CS, Patel K, Ankit C, Ganesh Babu TC, Raman R. Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography. Indian J Ophthalmol 2021; 69:2999-3008. [PMID: 34708735 PMCID: PMC8725112 DOI: 10.4103/ijo.ijo_1482_21] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Diabetic macular edema (DME), being a frequent manifestation of DR, disrupts the retinal symmetry. This event is particularly triggered by vascular endothelial growth factors (VEGF). Intravitreal injections of anti-VEGFs have been the most practiced treatment but an expensive option. A major challenge associated with this treatment is determining an optimal treatment regimen and differentiating patients who do not respond to anti-VEGF. As it has a significant burden for both the patient and the health care providers if the patient is not responding, any clinically acceptable method to predict the treatment outcomes holds huge value in the efficient management of DME. In such situations, artificial intelligence (AI) or machine learning (ML)-based algorithms come useful as they can analyze past clinical details of the patients and help clinicians to predict the patient's response to an anti-VEGF agent. The work presented here attempts to review the literature that is available from the peer research community to discuss solutions provided by AI/ML methodologies to tackle challenges in DME management. Lastly, a possibility for using two different types of data has been proposed, which is believed to be the key differentiators as compared to the similar and recent contributions from the peer research community.
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Affiliation(s)
- Sandipan Chakroborty
- Center for Applications and Research in India, Carl Zeiss India (Bangalore) Pvt. Ltd., Bengaluru, Karnataka, India
| | - Mansi Gupta
- Center for Applications and Research in India, Carl Zeiss India (Bangalore) Pvt. Ltd., Bengaluru, Karnataka, India
| | | | - Krunalkumar Patel
- Center for Applications and Research in India, Carl Zeiss India (Bangalore) Pvt. Ltd., Bengaluru, Karnataka, India
| | - Chavan Ankit
- Center for Applications and Research in India, Carl Zeiss India (Bangalore) Pvt. Ltd., Bengaluru, Karnataka, India
| | - T C Ganesh Babu
- Center for Applications and Research in India, Carl Zeiss India (Bangalore) Pvt. Ltd., Bengaluru, Karnataka, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
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22
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Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning-based artificial intelligence. Graefes Arch Clin Exp Ophthalmol 2021; 260:1113-1123. [PMID: 34636995 DOI: 10.1007/s00417-021-05427-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/20/2021] [Accepted: 09/19/2021] [Indexed: 10/20/2022] Open
Abstract
PURPOSE To create a model for prediction of postoperative visual acuity (VA) after vitrectomy for macular hole (MH) treatment using preoperative optical coherence tomography (OCT) images, using deep learning (DL)-based artificial intelligence. METHODS This was a retrospective single-center study. We evaluated 259 eyes that underwent vitrectomy for MHs. We divided the eyes into four groups, based on their 6-month postoperative Snellen VA values: (A) ≥ 20/20; (B) 20/25-20/32; (C) 20/32-20/63; and (D) ≤ 20/100. Training data were randomly selected, comprising 20 eyes in each group. Test data were also randomly selected, comprising 52 total eyes in the same proportions as those of each group in the total database. Preoperative OCT images with corresponding postoperative VA values were used to train the original DL network. The final prediction of postoperative VA was subjected to regression analysis based on inferences made with DL network output. We created a model for predicting postoperative VA from preoperative VA, MH size, and age using multivariate linear regression. Precision values were determined, and correlation coefficients between predicted and actual postoperative VA values were calculated in two models. RESULTS The DL and multivariate models had precision values of 46% and 40%, respectively. The predicted postoperative VA values on the basis of DL and on preoperative VA and MH size were correlated with actual postoperative VA at 6 months postoperatively (P < .0001 and P < .0001, r = .62 and r = .55, respectively). CONCLUSION Postoperative VA after MH treatment could be predicted via DL using preoperative OCT images with greater accuracy than multivariate linear regression using preoperative VA, MH size, and age.
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23
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Le D, Son T, Yao X. Machine learning in optical coherence tomography angiography. Exp Biol Med (Maywood) 2021; 246:2170-2183. [PMID: 34279136 DOI: 10.1177/15353702211026581] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.
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Affiliation(s)
- David Le
- Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Taeyoon Son
- Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Xincheng Yao
- Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.,Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
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24
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Ran A, Cheung CY. Deep Learning-Based Optical Coherence Tomography and Optical Coherence Tomography Angiography Image Analysis: An Updated Summary. Asia Pac J Ophthalmol (Phila) 2021; 10:253-260. [PMID: 34383717 DOI: 10.1097/apo.0000000000000405] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
ABSTRACT Deep learning (DL) is a subset of artificial intelligence based on deep neural networks. It has made remarkable breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, there are rising interests in applying DL methods to analyze optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images. Studies showed that OCT and OCTA image evaluation by DL algorithms achieved good performance for disease detection, prognosis prediction, and image quality control, suggesting that the incorporation of DL technology could potentially enhance the accuracy of disease evaluation and the efficiency of clinical workflow. However, substantial issues, such as small training sample size, data preprocessing standardization, model robustness, results explanation, and performance cross-validation, are yet to be tackled before deploying these DL models in real-time clinics. This review summarized recent studies on DL-based image analysis models for OCT and OCTA images and discussed the potential challenges of clinical deployment and future research directions.
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Affiliation(s)
- Anran Ran
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR
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25
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Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021; 4:65. [PMID: 33828217 PMCID: PMC8027892 DOI: 10.1038/s41746-021-00438-z] [Citation(s) in RCA: 241] [Impact Index Per Article: 80.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/25/2021] [Indexed: 12/19/2022] Open
Abstract
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
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Affiliation(s)
- Ravi Aggarwal
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Guy Martin
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | | | - Dominic King
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK.
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
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26
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Imamura H, Tabuchi H, Nagasato D, Masumoto H, Baba H, Furukawa H, Maruoka S. Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning. Graefes Arch Clin Exp Ophthalmol 2021; 259:1569-1577. [PMID: 33576859 DOI: 10.1007/s00417-021-05078-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 11/23/2020] [Accepted: 01/06/2021] [Indexed: 10/22/2022] Open
Abstract
PURPOSE We assessed the ability of deep learning (DL) models to distinguish between tear meniscus of lacrimal duct obstruction (LDO) patients and normal subjects using anterior segment optical coherence tomography (ASOCT) images. METHODS The study included 117 ASOCT images (19 men and 98 women; mean age, 66.6 ± 13.6 years) from 101 LDO patients and 113 ASOCT images (29 men and 84 women; mean age, 38.3 ± 19.9 years) from 71 normal subjects. We trained to construct 9 single and 502 ensemble DL models with 9 different network structures, and calculated the area under the curve (AUC), sensitivity, and specificity to compare the distinguishing abilities of these single and ensemble DL models. RESULTS For the highest single DL model (DenseNet169), the AUC, sensitivity, and specificity for distinguishing LDO were 0.778, 64.6%, and 72.1%, respectively. For the highest ensemble DL model (VGG16, ResNet50, DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, and Xception), the AUC, sensitivity, and specificity for distinguishing LDO were 0.824, 84.8%, and 58.8%, respectively. The heat maps indicated that these DL models placed their focus on the tear meniscus region of the ASOCT images. CONCLUSION The combination of DL and ASOCT images could distinguish between tear meniscus of LDO patients and normal subjects with a high level of accuracy. These results suggest that DL might be useful for automatic screening of patients for LDO.
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Affiliation(s)
- Hitoshi Imamura
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan
| | - Hitoshi Tabuchi
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan.,Department of Technology and Design Thinking for Medicine, Hiroshima University Graduate School, Hiroshima, Japan
| | - Daisuke Nagasato
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan. .,Department of Technology and Design Thinking for Medicine, Hiroshima University Graduate School, Hiroshima, Japan.
| | - Hiroki Masumoto
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan.,Department of Technology and Design Thinking for Medicine, Hiroshima University Graduate School, Hiroshima, Japan
| | - Hiroaki Baba
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan
| | - Hiroki Furukawa
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan
| | - Sachiko Maruoka
- Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji City, Hyogo, 671-1227, Japan
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27
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Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning. Sci Rep 2020; 10:19369. [PMID: 33168888 PMCID: PMC7652944 DOI: 10.1038/s41598-020-76513-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 10/29/2020] [Indexed: 12/22/2022] Open
Abstract
This study examined whether age and brachial-ankle pulse-wave velocity (baPWV) can be predicted with ultra-wide-field pseudo-color (UWPC) images using deep learning (DL). We examined 170 UWPC images of both eyes of 85 participants (40 men and 45 women, mean age: 57.5 ± 20.9 years). Three types of images were included (total, central, and peripheral) and analyzed by k-fold cross-validation (k = 5) using Visual Geometry Group-16. After bias was eliminated using the generalized linear mixed model, the standard regression coefficients (SRCs) between actual age and baPWV and predicted age and baPWV from the UWPC images by the neural network were calculated, and the prediction accuracies of the DL model for age and baPWV were examined. The SRC between actual age and predicted age by the neural network was 0.833 for all images, 0.818 for central images, and 0.649 for peripheral images (all P < 0.001) and between the actual baPWV and the predicted baPWV was 0.390 for total images, 0.419 for central images, and 0.312 for peripheral images (all P < 0.001). These results show the potential prediction capability of DL for age and vascular aging and could be useful for disease prevention and early treatment.
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28
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Jung JJ, Soh YQ, Sha P, Yu S, Durbin MK, Hoang QV. Effects of Induced Astigmatism on Spectral Domain-OCT Angiography Quantitative Metrics. Am J Ophthalmol 2020; 219:49-58. [PMID: 32681911 DOI: 10.1016/j.ajo.2020.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 07/02/2020] [Accepted: 07/03/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To analyze the effect of induced astigmatism on en-face spectral-domain optical coherence tomography angiography quantitative metrics. DESIGN Prospective crossover study. METHODS Normal eyes without astigmatism and with 0.75, 1.75, and 2.75 diopters (D) of with-the-rule (WTR) astigmatism were imaged using a 3 × 3-mm scan pattern SD-OCTA CIRRUS 5000 HD-OCT with AngioPlex (Carl Zeiss Meditec, Dublin, CA, USA). Quantitative parameters, including foveal avascular zone metrics, parafoveal vessel length density (VD), and perfusion density (PD) were corrected for magnification secondary to axial length and analyzed. Univariate linear regressions were performed within each eye to correlate quantitative metrics to the level of an induced astigmatic cylinder. RESULTS Fifteen eyes from 15 patients were imaged. Every 1-D increase in induced WTR astigmatism was associated with a statistically significant decrease in VD and PD within all Early Treatment Diabetic Retinopathy Study inner ring quadrants; however, especially more so nasally (VD: 0.63; P < .001; PD: 0.0089; P = .001). For every 1-D increase in induced astigmatism, the resulting decrease in the inner ring superior quadrant was 12% greater for VD and 16% greater for PD versus that in the inferior quadrant. The resulting decrease in the inner ring nasal quadrant was 40% greater for VD and 48% greater for PD versus that in the temporal quadrant. CONCLUSIONS Increasing levels of induced WTR astigmatism correlated with globally diminishing VD and PD, was more symmetrical for vertical than horizontal quadrants, and was most pronounced nasally. This may be due to a high prevalence of horizontally oriented vessels nasally and the horizontal optical defocus induced by WTR astigmatism.
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29
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Gao M, Guo Y, Hormel TT, Sun J, Hwang TS, Jia Y. Reconstruction of high-resolution 6×6-mm OCT angiograms using deep learning. BIOMEDICAL OPTICS EXPRESS 2020; 11:3585-3600. [PMID: 33014553 PMCID: PMC7510902 DOI: 10.1364/boe.394301] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/20/2020] [Accepted: 05/23/2020] [Indexed: 05/06/2023]
Abstract
Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6-mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.
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Affiliation(s)
- Min Gao
- Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Yukun Guo
- Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Tristan T. Hormel
- Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jiande Sun
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
| | - Thomas S. Hwang
- Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
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30
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Sogawa T, Tabuchi H, Nagasato D, Masumoto H, Ikuno Y, Ohsugi H, Ishitobi N, Mitamura Y. Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography. PLoS One 2020; 15:e0227240. [PMID: 32298265 PMCID: PMC7161961 DOI: 10.1371/journal.pone.0227240] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/29/2020] [Indexed: 12/20/2022] Open
Abstract
This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 910 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL's renowned model, Visual Geometry Group-16: nHM, 146 images; HM, 531 images; mCNV, 122 images; and RS, 111 images (n = 910). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined. The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.970; sensitivity, 90.6%; specificity, 94.2%. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 1.000; sensitivity, 100.0%; specificity, 100.0%. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 96.5%; mCNV images, 77.9%; and RS, 67.6% with mean, 88.9%.Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.
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Affiliation(s)
- Takahiro Sogawa
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan
| | - Hitoshi Tabuchi
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan
- Department of Technology and Design Thinking for Medicine, Hiroshima University Graduate School, Hiroshima, Japan
| | - Daisuke Nagasato
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan
- Department of Technology and Design Thinking for Medicine, Hiroshima University Graduate School, Hiroshima, Japan
| | - Hiroki Masumoto
- Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan
- Department of Technology and Design Thinking for Medicine, Hiroshima University Graduate School, Hiroshima, Japan
| | | | | | | | - Yoshinori Mitamura
- Department of Ophthalmology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
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31
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Sawai Y, Miyata M, Uji A, Ooto S, Tamura H, Ueda-Arakawa N, Muraoka Y, Miyake M, Takahashi A, Kawashima Y, Kadomoto S, Oritani Y, Kawai K, Yamashiro K, Tsujikawa A. Usefulness of Denoising Process to Depict Myopic Choroidal Neovascularisation Using a Single Optical Coherence Tomography Angiography Image. Sci Rep 2020; 10:6172. [PMID: 32277172 PMCID: PMC7148361 DOI: 10.1038/s41598-020-62607-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 03/12/2020] [Indexed: 01/07/2023] Open
Abstract
Quality of single optical coherence tomography angiography (OCTA) images of myopic choroidal neovascularisation (mCNV) is poorer than in averaged images, although obtaining averaged images takes much time. This study evaluated the clinical usefulness of novel denoising process for depicting mCNV. This study included 20 eyes of 20 patients with mCNV. Ten en face images taken in a 3 × 3 mm macular cube were obtained from outer-retina-to-choriocapillaris layer. Three image types were prepared for analysis; single images before and after the denoising process accomplished deep learning (single and denoising groups, respectively) and up to 10 images were averaged (averaging group). Pairwise comparisons showed vessel density, vessel length density, and fractal dimension (FD) were higher; whereas, vessel density index (VDI) was lower in single group than in denoising and averaging groups. Detectable CNV indices, contrast-to-nose ratio, and CNV diagnostic scores were higher in denoising and averaging groups than in single group. No significant differences were detected in VDI, FD, or CNV diagnostic scores between denoising and averaging groups. The denoising process can utilise single OCTA images to provide results comparable to averaged OCTA images, which is clinically useful for shortening examination times with quality similar to averaging.
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Affiliation(s)
- Yuka Sawai
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Manabu Miyata
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akihito Uji
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Sotaro Ooto
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hiroshi Tamura
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Naoko Ueda-Arakawa
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuki Muraoka
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masahiro Miyake
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ayako Takahashi
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yu Kawashima
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shin Kadomoto
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yasuyuki Oritani
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kentaro Kawai
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kenji Yamashiro
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan ,Department of Ophthalmology, Red Cross Otsu Hospital, Otsu, Japan
| | - Akitaka Tsujikawa
- 0000 0004 0372 2033grid.258799.8Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
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