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Zhang ZR, Li JJ, Li KR. Artificial intelligence in individualized retinal disease management. Int J Ophthalmol 2024; 17:1519-1530. [PMID: 39156787 PMCID: PMC11286449 DOI: 10.18240/ijo.2024.08.19] [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: 01/04/2024] [Accepted: 03/06/2024] [Indexed: 08/20/2024] Open
Abstract
Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.
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Affiliation(s)
- Zi-Ran Zhang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
- Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jia-Jun Li
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
- Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Ke-Ran Li
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
- Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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Chandra S, Gurudas S, Burton BJL, Menon G, Pearce I, Mckibbin M, Kotagiri A, Talks J, Grabowska A, Ghanchi F, Gale R, Giani A, Chong V, Yamaguchi TCN, Pal B, Thottarath S, Pakeer RM, Chandak S, Montesel A, Sivaprasad S. Associations of presenting visual acuity with morphological changes on OCT in neovascular age-related macular degeneration: PRECISE Study Report 2. Eye (Lond) 2024; 38:757-765. [PMID: 37853106 PMCID: PMC10920623 DOI: 10.1038/s41433-023-02769-5] [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: 04/08/2023] [Revised: 09/04/2023] [Accepted: 09/21/2023] [Indexed: 10/20/2023] Open
Abstract
PURPOSE To study associations of optical coherence tomography (OCT) features with presenting visual acuity (VA) in treatment naive neovascular age-related macular degeneration (nAMD). METHODS Patients with nAMD initiated on aflibercept therapy were recruited from December 2019 to August 2021. Demographic and OCT (Spectralis, Heidelberg Engineering) features associated with good VA (VA ≥ 68 ETDRS letters, Snellen ≥ 6/12) and poor VA (VA < 54 letters, Snellen < 6/18) were analysed using Generalised Estimating Equations to account for inter-eye correlation. RESULTS Of 2274 eyes of 2128 patients enrolled, 2039 eyes of 1901 patients with complete data were analysed. Mean age was 79.4 (SD 7.8) years, female:male 3:2 and mean VA 58.0 (SD 14.5) letters. On multivariable analysis VA < 54 letters was associated with increased central subfield thickness (CST) (OR 1.40 per 100 µm; P < 0.001), foveal intraretinal fluid (OR 2.14; P < 0.001), polypoidal vasculopathy (PCV) relative to Type 1 macular neovascularisation (MNV) (OR 1.66; P = 0.049), presence of foveal subretinal hyperreflective material (SHRM) (OR 1.73; P = 0.002), foveal fibrosis (OR 3.85; P < 0.001), foveal atrophy (OR 5.54; P < 0.001), loss of integrity of the foveal ellipsoid zone (EZ) or external limiting membrane (ELM) relative to their preservation (OR 3.83; P < 0.001) and absence of subretinal drusenoid deposits (SDD) (presence vs absence; OR 0.75; P = 0.04). These features were associated with reduced odds of VA ≥ 68 letters except MNV subtypes and SDD. CONCLUSION Presence of baseline fovea-involving atrophy, fibrosis, intraretinal fluid, SHRM, PCV EZ/ELM loss and increased CST determine poor presenting VA. This highlights the need for early detection and treatment prior to structural changes that worsen baseline VA.
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Affiliation(s)
- Shruti Chandra
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
- Institute of Ophthalmology, University College, London, UK
| | - Sarega Gurudas
- Institute of Ophthalmology, University College, London, UK
| | | | - Geeta Menon
- Frimley Health NHS Foundation Trust, Surrey, UK
| | - Ian Pearce
- The Royal Liverpool and Broadgreen University Hospitals NHS Foundation Trust, Liverpool, UK
| | | | - Ajay Kotagiri
- South Tyneside and Sunderland NHS Foundation Trust, Sunderland, UK
| | - James Talks
- Newcastle Hospitals NHS Foundation Trust, Newcastle, UK
| | - Anna Grabowska
- King's College Hospital NHS Foundation Trust, London, UK
| | - Faruque Ghanchi
- Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Richard Gale
- Hull York Medical School and York, University of York and Scarborough Teaching Hospital NHS Foundation Trust, York, UK
| | - Andrea Giani
- Boehringer Ingelheim, Binger Str. 173, 55216, Ingelheim am, Rhein, Germany
| | - Victor Chong
- Institute of Ophthalmology, University College, London, UK
| | | | - Bishwanath Pal
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Sridevi Thottarath
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Raheeba Muhamed Pakeer
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Swati Chandak
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Andrea Montesel
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Sobha Sivaprasad
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK.
- Institute of Ophthalmology, University College, London, UK.
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Li D, Ran AR, Cheung CY, Prince JL. Deep learning in optical coherence tomography: Where are the gaps? Clin Exp Ophthalmol 2023; 51:853-863. [PMID: 37245525 PMCID: PMC10825778 DOI: 10.1111/ceo.14258] [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: 03/31/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.
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Affiliation(s)
- Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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Hubbard DC, Cox P, Redd TK. Assistive applications of artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:261-266. [PMID: 36728651 PMCID: PMC10065924 DOI: 10.1097/icu.0000000000000939] [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: 02/03/2023]
Abstract
PURPOSE OF REVIEW Assistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications. RECENT FINDINGS Artificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies. SUMMARY Over the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.
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Affiliation(s)
- Donald C Hubbard
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Parker Cox
- Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Travis K Redd
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
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Yeh TC, Chen SJ, Chou YB, Luo AC, Deng YS, Lee YH, Chang PH, Lin CJ, Tai MC, Chen YC, Ko YC. PREDICTING VISUAL OUTCOME AFTER SURGERY IN PATIENTS WITH IDIOPATHIC EPIRETINAL MEMBRANE USING A NOVEL CONVOLUTIONAL NEURAL NETWORK. Retina 2023; 43:767-774. [PMID: 36727822 DOI: 10.1097/iae.0000000000003714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
PURPOSE To develop a deep convolutional neural network that enables the prediction of postoperative visual outcomes after epiretinal membrane surgery based on preoperative optical coherence tomography images and clinical parameters to refine surgical decision making. METHODS A total of 529 patients with idiopathic epiretinal membrane who underwent standard vitrectomy with epiretinal membrane peeling surgery by two surgeons between January 1, 2014, and June 1, 2020, were enrolled. The newly developed Heterogeneous Data Fusion Net was introduced to predict postoperative visual acuity outcomes (improvement ≥2 lines in Snellen chart) 12 months after surgery based on preoperative cross-sectional optical coherence tomography images and clinical factors, including age, sex, and preoperative visual acuity. The predictive accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network model were evaluated. RESULTS The developed model demonstrated an overall accuracy for visual outcome prediction of 88.68% (95% CI, 79.0%-95.7%) with an area under the receiver operating characteristic curve of 97.8% (95% CI, 86.8%-98.0%), sensitivity of 87.0% (95% CI, 67.9%-95.5%), specificity of 92.9% (95% CI, 77.4%-98.0%), precision of 0.909, recall of 0.870, and F1 score of 0.889. The heatmaps identified the critical area for prediction as the ellipsoid zone of photoreceptors and the superficial retina, which was subjected to tangential traction of the proliferative membrane. CONCLUSION The novel Heterogeneous Data Fusion Net demonstrated high accuracy in the automated prediction of visual outcomes after weighing and leveraging multiple clinical parameters, including optical coherence tomography images. This approach may be helpful in establishing personalized therapeutic strategies for epiretinal membrane management.
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Affiliation(s)
- Tsai-Chu Yeh
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Shih-Jen Chen
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Yu-Bai Chou
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - An-Chun Luo
- Industrial Technology Research Institute, Taipei City, Taiwan
| | - Yu-Shan Deng
- Industrial Technology Research Institute, Taipei City, Taiwan
| | - Yu-Hsien Lee
- Industrial Technology Research Institute, Taipei City, Taiwan
| | - Po-Han Chang
- Industrial Technology Research Institute, Taipei City, Taiwan
| | - Chun-Ju Lin
- Industrial Technology Research Institute, Taipei City, Taiwan
| | - Ming-Chi Tai
- Industrial Technology Research Institute, Taipei City, Taiwan
- Department of Materials Science and Engineering, National Tsing-Hua University, Taipei City, Taiwan; and
| | - Ying-Chi Chen
- Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan
| | - Yu-Chieh Ko
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
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Moon S, Lee Y, Hwang J, Kim CG, Kim JW, Yoon WT, Kim JH. Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network. Sci Rep 2023; 13:5639. [PMID: 37024576 PMCID: PMC10079864 DOI: 10.1038/s41598-023-32398-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/27/2023] [Indexed: 04/08/2023] Open
Abstract
To develop an artificial intelligence (AI) model that predicts anti-vascular endothelial growth factor (VEGF) agent-specific anatomical treatment outcomes in neovascular age-related macular degeneration (AMD), thereby assisting clinicians in selecting the most suitable anti-VEGF agent for each patient. This retrospective study included patients diagnosed with neovascular AMD who received three loading injections of either ranibizumab or aflibercept. Training was performed using optical coherence tomography (OCT) images with an attention generative adversarial network (GAN) model. To test the performance of the AI model, the sensitivity and specificity to predict the presence of retinal fluid after treatment were calculated for the AI model, an experienced (Examiner 1), and a less experienced (Examiner 2) human examiners. A total of 1684 OCT images from 842 patients (419 treated with ranibizumab and 423 treated with aflibercept) were used as the training set. Testing was performed using images from 98 patients. In patients treated with ranibizumab, the sensitivity and specificity, respectively, were 0.615 and 0.667 for the AI model, 0.385 and 0.861 for Examiner 1, and 0.231 and 0.806 for Examiner 2. In patients treated with aflibercept, the sensitivity and specificity, respectively, were 0.857 and 0.881 for the AI model, 0.429 and 0.976 for Examiner 1, and 0.429 and 0.857 for Examiner 2. In 18.5% of cases, the fluid status of synthetic posttreatment images differed between ranibizumab and aflibercept. The AI model using GAN might predict anti-VEGF agent-specific short-term treatment outcomes with relatively higher sensitivity than human examiners. Additionally, there was a difference in the efficacy in fluid resolution between the anti-VEGF agents. These results suggest the potential of AI in personalized medicine for patients with neovascular AMD.
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Affiliation(s)
- Sehwan Moon
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
- MODULABS, Seoul, South Korea
| | - Youngsuk Lee
- INGRADIENT Inc., Seoul, South Korea
- MODULABS, Seoul, South Korea
| | - Jeongyoung Hwang
- AI Graduated School, Gwangju Institute of Science and Technology, Gwangju, South Korea
- MODULABS, Seoul, South Korea
| | - Chul Gu Kim
- Department of Ophthalmology, Kim's Eye Hospital, #156 Youngdeungpo-dong 4ga, Youngdeungpo-gu, Seoul, 150-034, South Korea
| | - Jong Woo Kim
- Department of Ophthalmology, Kim's Eye Hospital, #156 Youngdeungpo-dong 4ga, Youngdeungpo-gu, Seoul, 150-034, South Korea
| | - Won Tae Yoon
- Department of Ophthalmology, Kim's Eye Hospital, #156 Youngdeungpo-dong 4ga, Youngdeungpo-gu, Seoul, 150-034, South Korea.
- Kim's Eye Hospital Data Center, Seoul, South Korea.
| | - Jae Hui Kim
- Department of Ophthalmology, Kim's Eye Hospital, #156 Youngdeungpo-dong 4ga, Youngdeungpo-gu, Seoul, 150-034, South Korea.
- Kim's Eye Hospital Data Center, Seoul, South Korea.
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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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Sheng B, Chen X, Li T, Ma T, Yang Y, Bi L, Zhang X. An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front Public Health 2022; 10:971943. [PMID: 36388304 PMCID: PMC9650481 DOI: 10.3389/fpubh.2022.971943] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/04/2022] [Indexed: 01/25/2023] Open
Abstract
Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.
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Affiliation(s)
- Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
| | - Xiaosi Chen
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tingyao Li
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
| | - Tianxing Ma
- Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing, China
| | - Yang Yang
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Bi
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Xinyuan Zhang
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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