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Shats D, Balasubramanian T, Sidelnikov D, Das U, Onyekaba NA, Forbes HE, Lu N, Williams K, Levin MR, Sundararajan S, Vij S, Gadagkar H, Rege A, Saeedi O, Chen V, Alexander JL. Association of Speckle-Based Blood Flow Measurements and Fluorescein Angiography in Infants with Retinopathy of Prematurity. OPHTHALMOLOGY SCIENCE 2024; 4:100463. [PMID: 38591050 PMCID: PMC11000102 DOI: 10.1016/j.xops.2023.100463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/01/2023] [Accepted: 12/26/2023] [Indexed: 04/10/2024]
Abstract
Purpose To determine the correlation between blood flow metrics measured by intravenous fluorescein angiography (IVFA) and the blood flow velocity index (BFVi) obtained by laser speckle contrast imaging (LSCI) in infants with retinopathy of prematurity (ROP). Design Prospective comparative pilot study. Subjects Seven eyes from 7 subjects with ROP. Methods Unilateral LSCI and IVFA data were obtained from each subject in the neonatal intensive care unit. Five LSCI-based metrics and 5 IVFA-based metrics were extracted from images to quantify blood flow patterns in the same region of interest. Correlation between LSCI-based and IVFA-based blood flow metrics was compared between 2 subgroups of ROP severity: moderate ROP (defined as stage ≤ 2 without Plus disease) and severe ROP (defined as stage ≥3 or Plus disease). Main Outcome Measures Pearson and Kendall rank correlation coefficients between IVFA and LSCI metrics; Student t test P values comparing LSCI metrics between "severe" and "moderate" ROP groups. Results Pearson correlations between IVFA and LSCI included arterial-venous transit time (AVTT) and peak BFVi (pBFVi; r = -0.917; P = 0.004), AVTT and dip BFVi (dBFVi; r = -0.920; P = 0.003), AVTT and mean BFVi (r = -0.927- P = 0.003), and AVTT and volumetric rise index (r = -0.779; P = 0.039). Kendall rank correlation between AVTT and dBFVi was r = -0.619 (P = 0.051). pBFVi was higher in severe ROP than in moderate ROP (8.4 ± 0.6 and 4.4 ± 1.8, respectively; P = 0.0045 using the 2-sample t test with pooled variance and P = 0.0952 using the Wilcoxon rank-sum test). Conclusions Correlation was found between blood flow metrics obtained by IVFA and noninvasive LSCI techniques. We demonstrate the feasibility of obtaining quantitative metrics using LSCI in infants with ROP in this pilot study; however, further investigation is needed to evaluate its potential use in clinical assessment of ROP severity. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Daniel Shats
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Tara Balasubramanian
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Danielle Sidelnikov
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Urjita Das
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ndidi-Amaka Onyekaba
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - He E. Forbes
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Noela Lu
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Kristin Williams
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Moran R. Levin
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Sripriya Sundararajan
- Department of Neonatology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Shitiz Vij
- Vasoptic Medical, Inc., Columbia, Maryland
| | | | | | - Osamah Saeedi
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Victoria Chen
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
| | - Janet L. Alexander
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland
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Zhao X, Chen S, Zhang S, Liu Y, Hu Y, Yuan D, Xie L, Luo X, Zheng M, Tian R, Chen Y, Tan T, Yu Z, Sun Y, Wu Z, Zhang G. A fundus image dataset for intelligent retinopathy of prematurity system. Sci Data 2024; 11:543. [PMID: 38802420 PMCID: PMC11130119 DOI: 10.1038/s41597-024-03362-5] [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: 12/13/2023] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.
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Affiliation(s)
- Xinyu Zhao
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Shaobin Chen
- Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region of China, Macao, China
| | - Sifan Zhang
- Department of Biology, New York University, New York, NY, US
| | - Yaling Liu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yarou Hu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Duo Yuan
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Liqiong Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Xiayuan Luo
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Mianying Zheng
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Ruyin Tian
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yi Chen
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region of China, Macao, China
| | - Zhen Yu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Yue Sun
- Faculty of Applied Sciences, Macao Polytechnic University, Macao Special Administrative Region of China, Macao, China.
| | - Zhenquan Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
| | - Guoming Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
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Binenbaum G, Stahl A, Coyner AS, He J, Ying GS, Ostmo S, Chan RVP, Toth C, Vinekar A, Campbell JP. P-Score: A Reference-Image-Based Clinical Grading Scale for Vascular Change in Retinopathy of Prematurity. Ophthalmology 2024:S0161-6420(24)00314-2. [PMID: 38795976 DOI: 10.1016/j.ophtha.2024.05.019] [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: 03/12/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
PURPOSE The International Classification of Retinopathy of Prematurity Third Edition (ICROP3) acknowledged that plus-like ROP vascular changes occur along a spectrum. Historically, clinician-experts demonstrate variable agreement for plus diagnosis. We developed a 9-photo reference-image set for grading plus-like changes and compared intergrader agreement of the set to standard grading with no-plus/pre-plus/plus. DESIGN Retinal photographic grading and expert consensus opinion PARTICIPANTS: Development: 34 international ICROP3 committee members. VALIDATION 30 ophthalmologists with ROP expertise (15 ICROP3 committee members, 15 non-ICROP3 members) METHODS: Nine ROP fundus images (P1 through P9) representing increasing degrees of zone I vascular tortuosity and dilation, based on ICROP3-committee's 34 members' gradings and consensus image review, were used to establish standard photographs for the "Plus (P) Score." Study participants graded 150 fundus photographs two ways, separated by a 1-week washout period: (1) no-plus/pre-plus/plus disease, (2) choosing the closest P-Score image. MAIN OUTCOME MEASURES Intergrader agreement measured by intraclass correlation coefficient (ICC) RESULTS: Intergrader agreement was higher using P-Score (ICC 0.75, 95% CI 0.71-0.79) than no-plus/pre-plus/plus (ICC 0.67, 95% CI 0.62-0.72). Mean P-Scores for images whose mode gradings were no-plus, pre-plus, and plus, were 2.5 (SD 0.7), 4.8 (SD 0.8), and 7.4 (SD 0.8), respectively. CONCLUSIONS Intergrader agreement of plus-like vascular change in ROP using the P-Score is high. We recommend incorporation of this 9-image reference set into ICROP3 and clinician daily practice alongside zone/stage/plus. P-score is not yet meant to replace plus diagnosis for treatment decisions, but its use at our institutions has permitted better comparison between examinations for progression and regression, communication between examiners, and documentation of vascular change without fundus imaging. P-score also could provide more detailed ROP classification for clinical trials, consistent with the spectrum of plus-like change that is now formally part of ICROP.
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Affiliation(s)
- Gil Binenbaum
- Division of Ophthalmology, Children's Hospital of Philadelphia, Philadelphia, PA; Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA.
| | - Andreas Stahl
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Aaron S Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland, OR
| | - Jocelyn He
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA
| | - Gui-Shuang Ying
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland, OR
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Cynthia Toth
- Department of Ophthalmology, Duke University, Durham NC
| | - Anand Vinekar
- Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, Karnataka, India
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, OR
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Burt SS, Woodward M, Ni S, Jackson J, Coyner AS, Ostmo SR, Liang G, Bayhaqi Y, Jia Y, Huang D, Chiang MF, Young BK, Jian Y, Campbell JP. Isolated retinal neovascularization in retinopathy of prematurity: clinical associations and prognostic implications. Ophthalmol Retina 2024:S2468-6530(24)00234-3. [PMID: 38735640 DOI: 10.1016/j.oret.2024.04.025] [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: 04/17/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE Isolated retinal neovascularization (IRNV) is a common finding in patients with stage 2 and 3 retinopathy of prematurity (ROP). This study aims to further classify the clinical course and significance of these lesions (previously described as "popcorn" based on clinical appearance) in patients with ROP as visualized with ultra-widefield optical coherence tomography (UWF-OCT). DESIGN Single center, retrospective case series. PARTICIPANTS Images were collected from 136 babies in the Oregon Health and Science University neonatal intensive care unit. METHODS A prototype UWF-OCT device captured en face scans (>140°), which were reviewed for the presence of IRNV along with standard zone, stage, and plus classification. In a cross-sectional analysis we compared demographics and the clinical course of eyes with and without IRNV. Longitudinally, we compared ROP severity using a clinician-assigned vascular severity score (VSS) and compared the risk of progression among eyes with and without IRNV using multivariable logistic regression (MLR). MAIN OUTCOME MEASURES Differences in clinical demographics and disease progression between patients with and without IRNV. RESULTS Of the 136 patients, 60 developed stage 2 or worse ROP during their disease course, 22 of whom had IRNV visualized on UWF-OCT (37%). On average, patients with IRNV had lower birth weights (BW) (660.1g vs 916.8g, p = 0.001), gestational age (GA) (24.9 vs 26.1 weeks, p = 0.01), and were more likely to present with ROP in zone I (63.4% vs 15.8%, p < 0.001). They were also more likely to progress to stage 3 (68.2% vs 13.2%, p < 0.001) and receive treatment (54.5% vs 15.8%, p = 0.002). Eyes with IRNV had a higher peak VSS (5.61 vs 3.73, p < 0.001) and averaged a higher VSS throughout their disease course. On MLR, IRNV was independently associated with progression to stage 3 (p = 0.02) and requiring treatment (p = 0.03), controlling for GA, BW, and initial zone 1 disease. CONCLUSION In this single center study, we found that IRNV occurs in higher risk babies and was an independent risk factor for ROP progression and treatment. These findings may have implications for OCT-based ROP classifications in the future.
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Affiliation(s)
- Spencer S Burt
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Mani Woodward
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Shuibin Ni
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - John Jackson
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Aaron S Coyner
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Susan R Ostmo
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Guangru Liang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Yakub Bayhaqi
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Yali Jia
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - David Huang
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA; National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Benjamin K Young
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Yifan Jian
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA
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Marra KV, Chen JS, Robles-Holmes HK, Miller J, Wei G, Aguilar E, Ideguchi Y, Ly KB, Prenner S, Erdogmus D, Ferrara N, Campbell JP, Friedlander M, Nudleman E. Development of a Semi-automated Computer-based Tool for the Quantification of Vascular Tortuosity in the Murine Retina. OPHTHALMOLOGY SCIENCE 2024; 4:100439. [PMID: 38361912 PMCID: PMC10867761 DOI: 10.1016/j.xops.2023.100439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 10/10/2023] [Accepted: 11/27/2023] [Indexed: 02/17/2024]
Abstract
Purpose The murine oxygen-induced retinopathy (OIR) model is one of the most widely used animal models of ischemic retinopathy, mimicking hallmark pathophysiology of initial vaso-obliteration (VO) resulting in ischemia that drives neovascularization (NV). In addition to NV and VO, human ischemic retinopathies, including retinopathy of prematurity (ROP), are characterized by increased vascular tortuosity. Vascular tortuosity is an indicator of disease severity, need to treat, and treatment response in ROP. Current literature investigating novel therapeutics in the OIR model often report their effects on NV and VO, and measurements of vascular tortuosity are less commonly performed. No standardized quantification of vascular tortuosity exists to date despite this metric's relevance to human disease. This proof-of-concept study aimed to apply a previously published semi-automated computer-based image analysis approach (iROP-Assist) to develop a new tool to quantify vascular tortuosity in mouse models. Design Experimental study. Subjects C57BL/6J mice subjected to the OIR model. Methods In a pilot study, vasculature was manually segmented on flat-mount images of OIR and normoxic (NOX) mice retinas and segmentations were analyzed with iROP-Assist to quantify vascular tortuosity metrics. In a large cohort of age-matched (postnatal day 12 [P12], P17, P25) NOX and OIR mice retinas, NV, VO, and vascular tortuosity were quantified and compared. In a third experiment, vascular tortuosity in OIR mice retinas was quantified on P17 following intravitreal injection with anti-VEGF (aflibercept) or Immunoglobulin G isotype control on P12. Main Outcome Measures Vascular tortuosity. Results Cumulative tortuosity index was the best metric produced by iROP-Assist for discriminating between OIR mice and NOX controls. Increased vascular tortuosity correlated with disease activity in OIR. Treatment of OIR mice with aflibercept rescued vascular tortuosity. Conclusions Vascular tortuosity is a quantifiable feature of the OIR model that correlates with disease severity and may be quickly and accurately quantified using the iROP-Assist algorithm. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Kyle V. Marra
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
- School of Medicine, University of California San Diego, San Diego, California
| | - Jimmy S. Chen
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Hailey K. Robles-Holmes
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Joseph Miller
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Guoqin Wei
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Edith Aguilar
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Yoichiro Ideguchi
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Kristine B. Ly
- College of Optometry, Pacific University, Forest Grove, Oregon
| | - Sofia Prenner
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Napoleone Ferrara
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Martin Friedlander
- Department of Molecular Medicine, The Scripps Research Institute, San Diego, California
| | - Eric Nudleman
- Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, San Diego, California
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Driban M, Yan A, Selvam A, Ong J, Vupparaboina KK, Chhablani J. Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review. Int J Retina Vitreous 2024; 10:36. [PMID: 38654344 PMCID: PMC11036694 DOI: 10.1186/s40942-024-00554-4] [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: 03/04/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.
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Affiliation(s)
- Matthew Driban
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Yan
- Department of Medicine, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Amrish Selvam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Coyner AS, Murickan T, Oh MA, Young BK, Ostmo SR, Singh P, Chan RVP, Moshfeghi DM, Shah PK, Venkatapathy N, Chiang MF, Kalpathy-Cramer J, Campbell JP. Multinational External Validation of Autonomous Retinopathy of Prematurity Screening. JAMA Ophthalmol 2024; 142:327-335. [PMID: 38451496 PMCID: PMC10921347 DOI: 10.1001/jamaophthalmol.2024.0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/15/2023] [Indexed: 03/08/2024]
Abstract
Importance Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.
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Affiliation(s)
- Aaron S. Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Tom Murickan
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Minn A. Oh
- Casey Eye Institute, Oregon Health & Science University, Portland
| | | | - Susan R. Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Praveer Singh
- Ophthalmology, University of Colorado School of Medicine, Aurora
| | - R. V. Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago
| | - Darius M. Moshfeghi
- Byers Eye Institute, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, California
| | - Parag K. Shah
- Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | | | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland
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Kim J, Villarreal M, Arya S, Hernandez A, Moreira A. Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics. J Clin Med 2024; 13:1077. [PMID: 38398389 PMCID: PMC10889493 DOI: 10.3390/jcm13041077] [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: 12/28/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.
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Affiliation(s)
- Jennifer Kim
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Mariela Villarreal
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Shreyas Arya
- Division of Neonatal-Perinatal Medicine, Dayton Children’s Hospital, Dayton, OH 45404, USA
| | - Antonio Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Alvaro Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
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9
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Li X, Owen LA, Taylor KD, Ostmo S, Chen YDI, Coyner AS, Sonmez K, Hartnett ME, Guo X, Ipp E, Roll K, Genter P, Chan RVP, DeAngelis MM, Chiang MF, Campbell JP, Rotter JI. Genome-wide association identifies novel ROP risk loci in a multiethnic cohort. Commun Biol 2024; 7:107. [PMID: 38233474 PMCID: PMC10794688 DOI: 10.1038/s42003-023-05743-9] [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: 05/07/2023] [Accepted: 12/26/2023] [Indexed: 01/19/2024] Open
Abstract
We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10-8) and 9 with significance of p < 5×10-6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10-9); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.
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Affiliation(s)
- Xiaohui Li
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Leah A Owen
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, USA.
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA.
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA.
- Department of Ophthalmology, University at Buffalo the State University of New York, Buffalo, NY, USA.
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Aaron S Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Kemal Sonmez
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | | | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Eli Ipp
- Division of Endocrinology and Metabolism, Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kathryn Roll
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Pauline Genter
- Division of Endocrinology and Metabolism, Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Margaret M DeAngelis
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Department of Ophthalmology, University at Buffalo the State University of New York, Buffalo, NY, USA
- Department of Biochemistry; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, NY, USA
- Department of Neuroscience; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, NY, USA
- Department of Genetics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, NY, USA
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA.
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10
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Chu Y, Hu S, Li Z, Yang X, Liu H, Yi X, Qi X. Image Analysis-Based Machine Learning for the Diagnosis of Retinopathy of Prematurity: A Meta-analysis and Systematic Review. Ophthalmol Retina 2024:S2468-6530(24)00014-9. [PMID: 38237772 DOI: 10.1016/j.oret.2024.01.013] [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: 08/15/2023] [Revised: 01/02/2024] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
TOPIC To evaluate the performance of machine learning (ML) in the diagnosis of retinopathy of prematurity (ROP) and to assess whether it can be an effective automated diagnostic tool for clinical applications. CLINICAL RELEVANCE Early detection of ROP is crucial for preventing tractional retinal detachment and blindness in preterm infants, which has significant clinical relevance. METHODS Web of Science, PubMed, Embase, IEEE Xplore, and Cochrane Library were searched for published studies on image-based ML for diagnosis of ROP or classification of clinical subtypes from inception to October 1, 2022. The quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies was used to determine the risk of bias (RoB) of the included original studies. A bivariate mixed effects model was used for quantitative analysis of the data, and the Deek's test was used for calculating publication bias. Quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. RESULTS Twenty-two studies were included in the systematic review; 4 studies had high or unclear RoB. In the area of indicator test items, only 2 studies had high or unclear RoB because they did not establish predefined thresholds. In the area of reference standards, 3 studies had high or unclear RoB. Regarding applicability, only 1 study was considered to have high or unclear applicability in terms of patient selection. The sensitivity and specificity of image-based ML for the diagnosis of ROP were 93% (95% confidence interval [CI]: 0.90-0.94) and 95% (95% CI: 0.94-0.97), respectively. The area under the receiver operating characteristic curve (AUC) was 0.98 (95% CI: 0.97-0.99). For the classification of clinical subtypes of ROP, the sensitivity and specificity were 93% (95% CI: 0.89-0.96) and 93% (95% CI: 0.89-0.95), respectively, and the AUC was 0.97 (95% CI: 0.96-0.98). The classification results were highly similar to those of clinical experts (Spearman's R = 0.879). CONCLUSIONS Machine learning algorithms are no less accurate than human experts and hold considerable potential as automated diagnostic tools for ROP. However, given the quality and high heterogeneity of the available evidence, these algorithms should be considered as supplementary tools to assist clinicians in diagnosing ROP. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Yihang Chu
- Central South University of Forestry and Technology, Changsha, Hunan, China; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Shipeng Hu
- Central South University of Forestry and Technology, Changsha, Hunan, China
| | - Zilan Li
- Department of Biochemistry, McGill University, Montreal, Quebec, Canada
| | - Xiao Yang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hui Liu
- Central South University of Forestry and Technology, Changsha, Hunan, China.
| | - Xianglong Yi
- Department of Ophthalmology, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, China.
| | - Xinwei Qi
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
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11
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Nguyen TTP, Young BK, Coyner A, Ostmo S, Chan RVP, Kalpathy-Cramer J, Chiang MF, Campbell JP. Discrepancies in Diagnosis of Treatment-Requiring Retinopathy of Prematurity. Ophthalmol Retina 2024; 8:88-91. [PMID: 37689182 PMCID: PMC10841666 DOI: 10.1016/j.oret.2023.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/21/2023] [Accepted: 09/01/2023] [Indexed: 09/11/2023]
Abstract
52% of treated eyes with retinopathy of prematurity in a multicenter cohort didn’t require intervention per evaluation by an independent reading center. An artificial intelligence system detected worse vascular severity in the group designed as treatment-requiring by reading center.
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Affiliation(s)
- Thanh-Tin P Nguyen
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Benjamin K Young
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Aaron Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - R V Paul Chan
- Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | | | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland; National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
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12
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Chen JS, Marra KV, Robles-Holmes HK, Ly KB, Miller J, Wei G, Aguilar E, Bucher F, Ideguchi Y, Coyner AS, Ferrara N, Campbell JP, Friedlander M, Nudleman E. Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy. OPHTHALMOLOGY SCIENCE 2024; 4:100338. [PMID: 37869029 PMCID: PMC10585474 DOI: 10.1016/j.xops.2023.100338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/01/2023] [Accepted: 05/19/2023] [Indexed: 10/24/2023]
Abstract
Objective To develop a generative adversarial network (GAN) to segment major blood vessels from retinal flat-mount images from oxygen-induced retinopathy (OIR) and demonstrate the utility of these GAN-generated vessel segmentations in quantifying vascular tortuosity. Design Development and validation of GAN. Subjects Three datasets containing 1084, 50, and 20 flat-mount mice retina images with various stains used and ages at sacrifice acquired from previously published manuscripts. Methods Four graders manually segmented major blood vessels from flat-mount images of retinas from OIR mice. Pix2Pix, a high-resolution GAN, was trained on 984 pairs of raw flat-mount images and manual vessel segmentations and then tested on 100 and 50 image pairs from a held-out and external test set, respectively. GAN-generated and manual vessel segmentations were then used as an input into a previously published algorithm (iROP-Assist) to generate a vascular cumulative tortuosity index (CTI) for 20 image pairs containing mouse eyes treated with aflibercept versus control. Main Outcome Measures Mean dice coefficients were used to compare segmentation accuracy between the GAN-generated and manually annotated segmentation maps. For the image pairs treated with aflibercept versus control, mean CTIs were also calculated for both GAN-generated and manual vessel maps. Statistical significance was evaluated using Wilcoxon signed-rank tests (P ≤ 0.05 threshold for significance). Results The dice coefficient for the GAN-generated versus manual vessel segmentations was 0.75 ± 0.27 and 0.77 ± 0.17 for the held-out test set and external test set, respectively. The mean CTI generated from the GAN-generated and manual vessel segmentations was 1.12 ± 0.07 versus 1.03 ± 0.02 (P = 0.003) and 1.06 ± 0.04 versus 1.01 ± 0.01 (P < 0.001), respectively, for eyes treated with aflibercept versus control, demonstrating that vascular tortuosity was rescued by aflibercept when quantified by GAN-generated and manual vessel segmentations. Conclusions GANs can be used to accurately generate vessel map segmentations from flat-mount images. These vessel maps may be used to evaluate novel metrics of vascular tortuosity in OIR, such as CTI, and have the potential to accelerate research in treatments for ischemic retinopathies. Financial Disclosures The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Jimmy S. Chen
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - Kyle V. Marra
- Molecular Medicine, the Scripps Research Institute, San Diego, California
- School of Medicine, University of California San Diego, San Diego, California
| | - Hailey K. Robles-Holmes
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - Kristine B. Ly
- College of Optometry, Pacific University, Forest Grove, Oregon
| | - Joseph Miller
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - Guoqin Wei
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Edith Aguilar
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Felicitas Bucher
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yoichi Ideguchi
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Aaron S. Coyner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Napoleone Ferrara
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
| | - J. Peter Campbell
- Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Martin Friedlander
- Molecular Medicine, the Scripps Research Institute, San Diego, California
| | - Eric Nudleman
- Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, San Diego, California
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13
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Hoyek S, Cruz NFSD, Patel NA, Al-Khersan H, Fan KC, Berrocal AM. Identification of novel biomarkers for retinopathy of prematurity in preterm infants by use of innovative technologies and artificial intelligence. Prog Retin Eye Res 2023; 97:101208. [PMID: 37611892 DOI: 10.1016/j.preteyeres.2023.101208] [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: 06/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/25/2023]
Abstract
Retinopathy of prematurity (ROP) is a leading cause of preventable vision loss in preterm infants. While appropriate screening is crucial for early identification and treatment of ROP, current screening guidelines remain limited by inter-examiner variability in screening modalities, absence of local protocol for ROP screening in some settings, a paucity of resources and an increased survival of younger and smaller infants. This review summarizes the advancements and challenges of current innovative technologies, artificial intelligence (AI), and predictive biomarkers for the diagnosis and management of ROP. We provide a contemporary overview of AI-based models for detection of ROP, its severity, progression, and response to treatment. To address the transition from experimental settings to real-world clinical practice, challenges to the clinical implementation of AI for ROP are reviewed and potential solutions are proposed. The use of optical coherence tomography (OCT) and OCT angiography (OCTA) technology is also explored, providing evaluation of subclinical ROP characteristics that are often imperceptible on fundus examination. Furthermore, we explore several potential biomarkers to reduce the need for invasive procedures, to enhance diagnostic accuracy and treatment efficacy. Finally, we emphasize the need of a symbiotic integration of biologic and imaging biomarkers and AI in ROP screening, where the robustness of biomarkers in early disease detection is complemented by the predictive precision of AI algorithms.
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Affiliation(s)
- Sandra Hoyek
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Natasha F S da Cruz
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Nimesh A Patel
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Hasenin Al-Khersan
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Kenneth C Fan
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
| | - Audina M Berrocal
- Bascom Palmer Eye Institute, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA.
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14
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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15
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Soleimani M, Cheraqpour K, Sadeghi R, Pezeshgi S, Koganti R, Djalilian AR. Artificial Intelligence and Infectious Keratitis: Where Are We Now? Life (Basel) 2023; 13:2117. [PMID: 38004257 PMCID: PMC10672455 DOI: 10.3390/life13112117] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/27/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
Infectious keratitis (IK), which is one of the most common and catastrophic ophthalmic emergencies, accounts for the leading cause of corneal blindness worldwide. Different pathogens, including bacteria, viruses, fungi, and parasites, can cause IK. The diagnosis and etiology detection of IK pose specific challenges, and delayed or incorrect diagnosis can significantly worsen the outcome. Currently, this process is mainly performed based on slit-lamp findings, corneal smear and culture, tissue biopsy, PCR, and confocal microscopy. However, these diagnostic methods have their drawbacks, including experience dependency, tissue damage, cost, and time consumption. Diagnosis and etiology detection of IK can be especially challenging in rural areas or in countries with limited resources. In recent years, artificial intelligence (AI) has opened new windows in medical fields such as ophthalmology. An increasing number of studies have utilized AI in the diagnosis of anterior segment diseases such as IK. Several studies have demonstrated that AI algorithms can diagnose and detect the etiology of IK accurately and fast, which can be valuable, especially in remote areas and in countries with limited resources. Herein, we provided a comprehensive update on the utility of AI in IK.
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Affiliation(s)
- Mohammad Soleimani
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran; (M.S.); (K.C.); (R.S.)
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA;
| | - Kasra Cheraqpour
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran; (M.S.); (K.C.); (R.S.)
| | - Reza Sadeghi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran; (M.S.); (K.C.); (R.S.)
| | - Saharnaz Pezeshgi
- School of Medicine, Tehran University of Medical Sciences, Tehran 1461884513, Iran;
| | - Raghuram Koganti
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA;
| | - Ali R. Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA;
- Cornea Service, Stem Cell Therapy and Corneal Tissue Engineering Laboratory, Illinois Eye and Ear Infirmary, Chicago, IL 60612, USA
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16
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Khosravi P, Huck NA, Shahraki K, Hunter SC, Danza CN, Kim SY, Forbes BJ, Dai S, Levin AV, Binenbaum G, Chang PD, Suh DW. Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study. Int J Mol Sci 2023; 24:15105. [PMID: 37894785 PMCID: PMC10606803 DOI: 10.3390/ijms242015105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/29/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.
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Affiliation(s)
- Pooya Khosravi
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
| | - Nolan A. Huck
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Kourosh Shahraki
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Stephen C. Hunter
- School of Medicine, University of California, 900 University Ave, Riverside, CA 92521, USA;
| | - Clifford Neil Danza
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - So Young Kim
- Department of Ophthalmology, College of Medicine, Soonchunhyang University, Cheonan 31151, Chungcheongnam-do, Republic of Korea;
| | - Brian J. Forbes
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Shuan Dai
- Department of Ophthalmology, Queensland Children’s Hospital, South Brisbane, QLD 4101, Australia;
| | - Alex V. Levin
- Department of Ophthalmology, Flaum Eye Institute, Golisano Children’s Hospital, Rochester, NY 14642, USA;
| | - Gil Binenbaum
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Peter D. Chang
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
- Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA 92697, USA
| | - Donny W. Suh
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
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17
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Rao DP, Savoy FM, Tan JZE, Fung BPE, Bopitiya CM, Sivaraman A, Vinekar A. Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population. Front Pediatr 2023; 11:1197237. [PMID: 37794964 PMCID: PMC10545957 DOI: 10.3389/fped.2023.1197237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/29/2023] [Indexed: 10/06/2023] Open
Abstract
Purpose The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP). Participants Images were collected from infants enrolled in the KIDROP tele-ROP screening program. Methods We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (n = 25,982, 69.33%), validation (n = 4,006, 10.69%), and an independent test set (n = 7,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1-3) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist. Results Of the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%-92.59%) and 91.22% (95% CI: 90.42%-91.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%-83.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%-96.61%) and the AUROC was 0.970. Conclusion The novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.
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Affiliation(s)
- Divya Parthasarathy Rao
- Artificial Intelligence Research and Development, Remidio Innovative Solutions Inc., Glen Allen, VA, United States
| | - Florian M. Savoy
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Joshua Zhi En Tan
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Brian Pei-En Fung
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Chiran Mandula Bopitiya
- Artificial Intelligence Research and Development, Medios Technologies Pvt. Ltd., Singapore, Singapore
| | - Anand Sivaraman
- Artificial Intelligence Research and Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, India
| | - Anand Vinekar
- Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, India
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18
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Woods J, Biswas S. Retinopathy of prematurity: from oxygen management to molecular manipulation. Mol Cell Pediatr 2023; 10:12. [PMID: 37712996 PMCID: PMC10504188 DOI: 10.1186/s40348-023-00163-5] [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: 11/24/2022] [Accepted: 08/11/2023] [Indexed: 09/16/2023] Open
Abstract
INTRODUCTION Retinopathy of prematurity (ROP) is a vasoproliferative disorder of the premature retina with the potential to progress to extraretinal neovascularisation. This review serves as an introduction to retinopathy of prematurity (ROP), outlining key parts of ROP pathophysiology, diagnosis and treatment. ROP is traditionally diagnosed by indirect ophthalmoscopy and classified using anatomical zones, stages of disease, and the presence or absence of "plus disease" (dilation and tortuosity of the major retinal arterioles and venules). ROP has a bi-phasic pathophysiology: initial hyperoxia causes reduced retinal vascularisation, followed by pathological vaso-proliferation resulting from subsequent hypoxia and driven by vascular endothelial growth factor (VEGF). ADVANCEMENTS IN MANAGEMENT This review summarises previous trials to establish optimum oxygen exposure levels in newborns and more recently the development of anti-VEGF agents locally delivered to block pathological neovascularisation, which is technically easier to administer and less destructive than laser treatment. FUTURE DIRECTIONS There remains an ongoing concern regarding the potential unwanted systemic effects of intravitreally administered anti-VEGF on the overall development of the premature baby. Ongoing dosing studies may lessen these fears by identifying the minimally effective dose required to block extraretinal neovascularisation.
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Affiliation(s)
- Jonathan Woods
- University of Birmingham Medical School, Medical School, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Susmito Biswas
- Manchester Royal Eye Hospital, Manchester University Hospital NHS Foundation Trust, Oxford Rd, Manchester, M13 9WL, UK
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19
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Shah S, Slaney E, VerHage E, Chen J, Dias R, Abdelmalik B, Weaver A, Neu J. Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature. Neonatology 2023; 120:558-565. [PMID: 37490881 DOI: 10.1159/000531441] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/30/2023] [Indexed: 07/27/2023]
Abstract
Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the screening burden on ophthalmologists and neonatologists and improve the detection of treatment-requiring ROP. Neural networks such as convolutional neural networks and deep learning (DL) systems are used to calculate a vascular severity score (VSS), an important component of various risk models. These DL systems have been validated in various studies, which are reviewed here. Most importantly, we discuss a promising study that validated a DL system that could predict the development of ROP despite a lack of clinical evidence of disease on the first retinal examination. Additionally, there is promise in utilizing these systems through telemedicine in more rural and resource-limited areas. This review highlights the value of these DL systems in early ROP diagnosis.
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Affiliation(s)
- Shivani Shah
- College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Elizabeth Slaney
- College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Erik VerHage
- Department of Pediatrics, University of Florida, Gainesville, Florida, USA
| | - Jinghua Chen
- Department of Ophthalmology, University of Florida, Gainesville, Florida, USA
| | - Raquel Dias
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
| | - Bishoy Abdelmalik
- College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Alex Weaver
- College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Josef Neu
- Department of Pediatrics, University of Florida, Gainesville, Florida, USA
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20
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Wang CT, Chang YH, Tan GSW, Lee SY, Chan RVP, Wu WC, Tsai ASH. Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases. Diagnostics (Basel) 2023; 13:diagnostics13081461. [PMID: 37189561 DOI: 10.3390/diagnostics13081461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/10/2023] [Accepted: 04/16/2023] [Indexed: 05/17/2023] Open
Abstract
Indirect ophthalmoscopy and handheld retinal imaging are the most common and traditional modalities for the evaluation and documentation of the pediatric fundus, especially for pre-verbal children. Optical coherence tomography (OCT) allows for in vivo visualization that resembles histology, and optical coherence tomography angiography (OCTA) allows for non-invasive depth-resolved imaging of the retinal vasculature. Both OCT and OCTA were extensively used and studied in adults, but not in children. The advent of prototype handheld OCT and OCTA have allowed for detailed imaging in younger infants and even neonates in the neonatal care intensive unit with retinopathy of prematurity (ROP). In this review, we discuss the use of OCTA and OCTA in various pediatric retinal diseases, including ROP, familial exudative vitreoretinopathy (FEVR), Coats disease and other less common diseases. For example, handheld portable OCT was shown to detect subclinical macular edema and incomplete foveal development in ROP, as well as subretinal exudation and fibrosis in Coats disease. Some challenges in the pediatric age group include the lack of a normative database and the difficulty in image registration for longitudinal comparison. We believe that technological improvements in the use of OCT and OCTA will improve our understanding and care of pediatric retina patients in the future.
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Affiliation(s)
- Chung-Ting Wang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City 333, Taiwan
| | - Yin-Hsi Chang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City 333, Taiwan
| | - Gavin S W Tan
- Singapore National Eye Centre, Singapore, Singapore 168751, Singapore
- DUKE NUS Medical School, Singapore 169857, Singapore
| | - Shu Yen Lee
- Singapore National Eye Centre, Singapore, Singapore 168751, Singapore
- DUKE NUS Medical School, Singapore 169857, Singapore
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Illinois Eye and Ear Infirmary, Chicago, IL 60612, USA
| | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City 333, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
| | - Andrew S H Tsai
- Singapore National Eye Centre, Singapore, Singapore 168751, Singapore
- DUKE NUS Medical School, Singapore 169857, Singapore
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21
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Wawer Matos PA, Reimer RP, Rokohl AC, Caldeira L, Heindl LM, Große Hokamp N. Artificial Intelligence in Ophthalmology - Status Quo and Future Perspectives. Semin Ophthalmol 2023; 38:226-237. [PMID: 36356300 DOI: 10.1080/08820538.2022.2139625] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
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Affiliation(s)
| | - Robert P Reimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Alexander C Rokohl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Ludwig M Heindl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
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22
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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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23
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Bujoreanu Bezman L, Tiutiuca C, Totolici G, Carneciu N, Bujoreanu FC, Ciortea DA, Niculet E, Fulga A, Alexandru AM, Stan DJ, Nechita A. Latest Trends in Retinopathy of Prematurity: Research on Risk Factors, Diagnostic Methods and Therapies. Int J Gen Med 2023; 16:937-949. [PMID: 36942030 PMCID: PMC10024537 DOI: 10.2147/ijgm.s401122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/17/2023] [Indexed: 03/15/2023] Open
Abstract
Retinopathy of prematurity (ROP) is a vasoproliferative disorder with an imminent risk of blindness, in cases where early diagnosis and treatment are not performed. The doctors' constant motivation to give these fragile beings a chance at life with optimal visual acuity has never stopped, since Terry first described this condition. Thus, throughout time, several specific advancements have been made in the management of ROP. Apart from the most known risk factors, this narrative review brings to light the latest research about new potential risk factors, such as: proteinuria, insulin-like growth factor 1 (IGF-1) and blood transfusions. Digital imaging has revolutionized the management of retinal pathologies, and it is more and more used in identifying and staging ROP, particularly in the disadvantaged regions by the means of telescreening. Moreover, optical coherence tomography (OCT) and automated diagnostic tools based on deep learning offer new perspectives on the ROP diagnosis. The new therapeutical trend based on the use of anti-VEGF agents is increasingly used in the treatment of ROP patients, and recent research sustains the theory according to which these agents do not interfere with the neurodevelopment of premature babies.
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Affiliation(s)
- Laura Bujoreanu Bezman
- Department of Ophthalmology, “Sfantul Apostol Andrei” Emergency Clinical Hospital, Galati, Romania
- Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Carmen Tiutiuca
- Department of Ophthalmology, “Sfantul Apostol Andrei” Emergency Clinical Hospital, Galati, Romania
- Clinical Surgical Department, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
- Correspondence: Carmen Tiutiuca, Clinical Surgical Department, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, 800008, Romania, Tel +40741330788, Email
| | - Geanina Totolici
- Department of Ophthalmology, “Sfantul Apostol Andrei” Emergency Clinical Hospital, Galati, Romania
- Clinical Surgical Department, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Nicoleta Carneciu
- Department of Ophthalmology, “Sfantul Apostol Andrei” Emergency Clinical Hospital, Galati, Romania
- Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Florin Ciprian Bujoreanu
- Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
- Florin Ciprian Bujoreanu, Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, 800008, Romania, Tel +40741395844, Email
| | - Diana Andreea Ciortea
- Department of Pediatrics, “Sfantul Ioan” Emergency Clinical Hospital for Children, Galati, Romania
- Clinical Medical Department, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Elena Niculet
- Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
- Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Ana Fulga
- Clinical Surgical Department, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
- Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Anamaria Madalina Alexandru
- Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
- Department of Neonatology, “Sfantul Apostol Andrei” Emergency Clinical Hospital, Galati, Romania
| | - Daniela Jicman Stan
- Doctoral School of Biomedical Sciences, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
| | - Aurel Nechita
- Department of Pediatrics, “Sfantul Ioan” Emergency Clinical Hospital for Children, Galati, Romania
- Clinical Medical Department, Faculty of Medicine and Pharmacy, “Dunărea de Jos” University, Galati, Romania
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24
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Nguyen TTP, Ni S, Ostmo S, Rajagopalan A, Coyner AS, Woodward M, Chiang MF, Jia Y, Huang D, Campbell JP, Jian Y. Association of Optical Coherence Tomography-Measured Fibrovascular Ridge Thickness and Clinical Disease Stage in Retinopathy of Prematurity. JAMA Ophthalmol 2022; 140:2797385. [PMID: 36227622 PMCID: PMC9562098 DOI: 10.1001/jamaophthalmol.2022.4173] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/18/2022] [Indexed: 11/30/2022]
Abstract
Importance Accurate diagnosis of retinopathy of prematurity (ROP) is essential to provide timely treatment and reduce the risk of blindness. However, the components of an ROP examination are subjective and qualitative. Objective To evaluate whether optical coherence tomography (OCT)-derived retinal thickness measurements at the vascular-avascular junction are associated with clinical diagnosis of ROP stage. Design, Setting, and Participants This cross-sectional longitudinal study compared OCT-based ridge thickness calculated from OCT B-scans by a masked examiner to the clinical diagnosis of 2 masked examiners using both traditional stage classifications and a more granular continuous scale at the neonatal intensive care unit (NICU) of Oregon Health & Science University (OHSU) Hospital. Infants who met ROP screening criteria in the OHSU NICU between June 2021 and April 2022 and had guardian consent were included. One OCT volume and en face image per patient per eye showing at least 1 to 2 clock hours of ridge were included in the final analysis. Main Outcomes and Measures Comparison of OCT-derived ridge thickness to the clinical diagnosis of ROP stage using an ordinal and continuous scale. Repeatability was assessed using 20 repeated examinations from the same visit and compared using intraclass correlation coefficient (ICC) and coefficient of variation (CV). Comparison of ridge thickness with ordinal categories was performed using generalized estimating equations and with continuous stage using Spearman correlation. Results A total of 128 separate OCT eye examinations from 50 eyes of 25 patients were analyzed. The ICC was 0.87 with a CV of 7.0%. Higher ordinal disease classification was associated with higher axial ridge thickness on OCT, with mean (SD) thickness measurements of 264.2 (11.2) μm (P < .001), 334.2 (11.4) μm (P < .001), and 495.0 (32.2) μm (P < .001) for stages 1, 2, and 3, respectively and with continuous stage labels (ρ = 0.739, P < .001). Conclusions and Relevance These results suggest that OCT-based quantification of peripheral stage in ROP may be an objective and quantitative biomarker that may be useful for clinical diagnosis and longitudinal monitoring and may have implications for disease classification in the future.
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Affiliation(s)
| | - Shuibin Ni
- Casey Eye Institute, Oregon Health & Science University, Portland
- Department of Biomedical Engineering, Oregon Health & Science University, Portland
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland
| | | | - Aaron S. Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Mani Woodward
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland
- Department of Biomedical Engineering, Oregon Health & Science University, Portland
| | - David Huang
- Casey Eye Institute, Oregon Health & Science University, Portland
- Department of Biomedical Engineering, Oregon Health & Science University, Portland
| | | | - Yifan Jian
- Casey Eye Institute, Oregon Health & Science University, Portland
- Department of Biomedical Engineering, Oregon Health & Science University, Portland
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25
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Chan RVP. Optical Coherence Tomography for Retinopathy of Prematurity and the Future of Retinopathy of Prematurity Screening. JAMA Ophthalmol 2022; 140:2797387. [PMID: 36227607 DOI: 10.1001/jamaophthalmol.2022.4326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago
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