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Nisanova A, Yavary A, Deaner J, Ali FS, Gogte P, Kaplan R, Chen KC, Nudleman E, Grewal D, Gupta M, Wolfe J, Klufas M, Yiu G, Soltani I, Emami-Naeini P. Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy. OPHTHALMOLOGY SCIENCE 2024; 4:100470. [PMID: 38827487 PMCID: PMC11141253 DOI: 10.1016/j.xops.2024.100470] [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: 09/05/2023] [Revised: 12/30/2023] [Accepted: 01/12/2024] [Indexed: 06/04/2024]
Abstract
Purpose Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts. Design Retrospective multicenter study. Participants Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022. Methods We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians' experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python. Main Outcome Measures Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models. Results The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17). Conclusions We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools. 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)
- Arina Nisanova
- School of Medicine, University of California Davis, Davis, California
| | - Arefeh Yavary
- Department of Computer Science, University of California Davis, Davis, California
| | - Jordan Deaner
- Mid Atlantic Retina, Wills Eye Hospital, Philadelphia, Pennsylvania
| | | | | | - Richard Kaplan
- New York Eye and Ear Infirmary of Mount Sinai, New York, New York
| | | | - Eric Nudleman
- Shiley Eye Center, University of California San Diego, La Jolla, California
| | | | - Meenakashi Gupta
- New York Eye and Ear Infirmary of Mount Sinai, New York, New York
| | - Jeremy Wolfe
- Associated Retinal Consultants, Royal Oak, Michigan
| | - Michael Klufas
- Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Glenn Yiu
- Tschannen Eye Institute, University of California Davis, Sacramento, California
| | - Iman Soltani
- Department of Mechanical and Aerospace Engineering, University of California Davis, Davis, California
| | - Parisa Emami-Naeini
- Tschannen Eye Institute, University of California Davis, Sacramento, California
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Singh M, Kumar A, Khanna NN, Laird JR, Nicolaides A, Faa G, Johri AM, Mantella LE, Fernandes JFE, Teji JS, Singh N, Fouda MM, Singh R, Sharma A, Kitas G, Rathore V, Singh IM, Tadepalli K, Al-Maini M, Isenovic ER, Chaturvedi S, Garg D, Paraskevas KI, Mikhailidis DP, Viswanathan V, Kalra MK, Ruzsa Z, Saba L, Laine AF, Bhatt DL, Suri JS. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine 2024; 73:102660. [PMID: 38846068 PMCID: PMC11154124 DOI: 10.1016/j.eclinm.2024.102660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding No funding received.
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Affiliation(s)
- Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Bennett University, 201310, Greater Noida, India
| | - Ashish Kumar
- Bennett University, 201310, Greater Noida, India
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Gavino Faa
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura E. Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | | | - Jagjit S. Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA
| | - George Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, DY1, Dudley, UK
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | | | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 110010, Serbia
| | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | | | | | - Dimitri P. Mikhailidis
- Department of Clinical Biochemistry, Royal Free Hospital Campus, University College London Medical School, University College London (UCL), London, UK
| | | | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Andrew F. Laine
- Departments of Biomedical and Radiology, Columbia University, New York, NY, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
- Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
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SenthilKumar G, Hammond ST, Zirgibel Z, Cohen KE, Beyer AM, Freed JK. Is the peripheral microcirculation a window into the human coronary microvasculature? J Mol Cell Cardiol 2024; 193:67-77. [PMID: 38848808 DOI: 10.1016/j.yjmcc.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/13/2024] [Accepted: 06/04/2024] [Indexed: 06/09/2024]
Abstract
An increasing body of evidence suggests a pivotal role for the microvasculature in the development of cardiovascular disease. A dysfunctional coronary microvascular network, specifically within endothelial cells-the inner most cell layer of vessels-is considered a strong, independent risk factor for future major adverse cardiac events. However, challenges exist with evaluating this critical vascular bed, as many of the currently available techniques are highly invasive and cost prohibitive. The more easily accessible peripheral microcirculation has surfaced as a potential surrogate in which to study mechanisms of coronary microvascular dysfunction and likewise may be used to predict poor cardiovascular outcomes. In this review, we critically evaluate a variety of prognostic, physiological, and mechanistic studies in humans to answer whether the peripheral microcirculation can add insight into coronary microvascular health. A conceptual framework is proposed that the health of the endothelium specifically may link the coronary and peripheral microvascular beds. This is supported by evidence showing a correlation between human coronary and peripheral endothelial function in vivo. Although not a replacement for investigating and understanding coronary microvascular function, the microvascular endothelium from the periphery responds similarly to (patho)physiological stress and may be leveraged to explore potential therapeutic pathways to mitigate stress-induced damage.
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Affiliation(s)
- Gopika SenthilKumar
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, United States; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States; Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Stephen T Hammond
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States; Division of Cardiovascular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Zachary Zirgibel
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, United States; Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Katie E Cohen
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States; Division of Cardiovascular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Andreas M Beyer
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States; Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States; Division of Cardiovascular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Julie K Freed
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, United States; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, United States; Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States.
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Engelmann J, Moukaddem D, Gago L, Strang N, Bernabeu MO. Applicability of Oculomics for Individual Risk Prediction: Repeatability and Robustness of Retinal Fractal Dimension Using DART and AutoMorph. Invest Ophthalmol Vis Sci 2024; 65:10. [PMID: 38842831 PMCID: PMC11160956 DOI: 10.1167/iovs.65.6.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 05/06/2024] [Indexed: 06/07/2024] Open
Abstract
Purpose To investigate whether fractal dimension (FD)-based oculomics could be used for individual risk prediction by evaluating repeatability and robustness. Methods We used two datasets: "Caledonia," healthy adults imaged multiple times in quick succession for research (26 subjects, 39 eyes, 377 color fundus images), and GRAPE, glaucoma patients with baseline and follow-up visits (106 subjects, 196 eyes, 392 images). Mean follow-up time was 18.3 months in GRAPE; thus it provides a pessimistic lower bound because vasculature could change. FD was computed with DART and AutoMorph. Image quality was assessed with QuickQual, but no images were initially excluded. Pearson, Spearman, and intraclass correlation (ICC) were used for population-level repeatability. For individual-level repeatability, we introduce measurement noise parameter λ, which is within-eye standard deviation (SD) of FD measurements in units of between-eyes SD. Results In Caledonia, ICC was 0.8153 for DART and 0.5779 for AutoMorph, Pearson/Spearman correlation (first and last image) 0.7857/0.7824 for DART, and 0.3933/0.6253 for AutoMorph. In GRAPE, Pearson/Spearman correlation (first and next visit) was 0.7479/0.7474 for DART, and 0.7109/0.7208 for AutoMorph (all P < 0.0001). Median λ in Caledonia without exclusions was 3.55% for DART and 12.65% for AutoMorph and improved to up to 1.67% and 6.64% with quality-based exclusions, respectively. Quality exclusions primarily mitigated large outliers. Worst quality in an eye correlated strongly with λ (Pearson 0.5350-0.7550, depending on dataset and method, all P < 0.0001). Conclusions Repeatability was sufficient for individual-level predictions in heterogeneous populations. DART performed better on all metrics and might be able to detect small, longitudinal changes, highlighting the potential of robust methods.
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Affiliation(s)
- Justin Engelmann
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Diana Moukaddem
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, United Kingdom
| | - Lucas Gago
- Departament de Matemátiques i Informática, Universitat de Barcelona, Barcelona, Spain
| | - Niall Strang
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, United Kingdom
| | - Miguel O. Bernabeu
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Bayes Centre, University of Edinburgh, Edinburgh, United Kingdom
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Patterson EJ, Bounds AD, Wagner SK, Kadri-Langford R, Taylor R, Daly D. Oculomics: A Crusade Against the Four Horsemen of Chronic Disease. Ophthalmol Ther 2024; 13:1427-1451. [PMID: 38630354 PMCID: PMC11109082 DOI: 10.1007/s40123-024-00942-x] [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: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 05/22/2024] Open
Abstract
Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.
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Affiliation(s)
| | | | - Siegfried K Wagner
- Moorfields Eye Hospital NHS Trust, 162 City Road, London, EC1V 2PD, UK
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London, EC1V 9EL, UK
| | | | - Robin Taylor
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
| | - Dan Daly
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
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Xie Z, Zhang T, Kim S, Lu J, Zhang W, Lin CH, Wu MR, Davis A, Channa R, Giancardo L, Chen H, Wang S, Chen R, Zhi D. iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images. PLoS Genet 2024; 20:e1011273. [PMID: 38728357 PMCID: PMC11111076 DOI: 10.1371/journal.pgen.1011273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 05/22/2024] [Accepted: 04/25/2024] [Indexed: 05/12/2024] Open
Abstract
Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10-8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images.
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Affiliation(s)
- Ziqian Xie
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tao Zhang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Sangbae Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Jiaxiong Lu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Wanheng Zhang
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Cheng-Hui Lin
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Man-Ru Wu
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Alexander Davis
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Luca Giancardo
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Han Chen
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Sui Wang
- Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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Shi D, Zhou Y, He S, Wagner SK, Huang Y, Keane PA, Ting DS, Zhang L, Zheng Y, He M. Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk. OPHTHALMOLOGY SCIENCE 2024; 4:100441. [PMID: 38420613 PMCID: PMC10899028 DOI: 10.1016/j.xops.2023.100441] [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: 07/04/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 03/02/2024]
Abstract
Purpose We aim to use fundus fluorescein angiography (FFA) to label the capillaries on color fundus (CF) photographs and train a deep learning model to quantify retinal capillaries noninvasively from CF and apply it to cardiovascular disease (CVD) risk assessment. Design Cross-sectional and longitudinal study. Participants A total of 90732 pairs of CF-FFA images from 3893 participants for segmentation model development, and 49229 participants in the UK Biobank for association analysis. Methods We matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on 7 vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events in the UK Biobank. Main Outcome Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio (HR; 95% confidence interval [CI]) for Cox regression analysis. Results On the FundusCapi dataset, the segmentation performance was AUC = 0.95, accuracy = 0.94, sensitivity = 0.90, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (P < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91 [0.84-0.98] and 0.68 [0.54-0.86], respectively). Conclusions Using paired CF-FFA images, we automated the laborious manual labeling process and enabled noninvasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events. 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)
- Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yukun Zhou
- Centre for Medical Image Computing, University College London, London, UK
| | - Shuang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Siegfried K. Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Yu Huang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Pearse A. Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Daniel S.W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, and Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Lei Zhang
- Faculty of Medicine, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mingguang He
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
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Prasad DK, Manjunath MP, Kulkarni MS, Kullambettu S, Srinivasan V, Chakravarthi M, Ramesh A. A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques. Diagnostics (Basel) 2024; 14:928. [PMID: 38732342 PMCID: PMC11083022 DOI: 10.3390/diagnostics14090928] [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: 03/19/2024] [Revised: 04/10/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.
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Affiliation(s)
- Deepthi K. Prasad
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Madhura Prakash Manjunath
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Meghna S. Kulkarni
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Spoorthi Kullambettu
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | - Venkatakrishnan Srinivasan
- Research and Development, Image Processing and Analysis, Forus Health Private Ltd., Bengaluru 560070, India; (M.P.M.); (M.S.K.); (S.K.); (V.S.)
| | | | - Anusha Ramesh
- Department of OBGyn, St. John’s Medical College, Bengaluru 560034, India;
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9
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Zhang Y, Li S, Wu W, Zhao Y, Han J, Tong C, Luo N, Zhang K. Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES. BioData Min 2024; 17:12. [PMID: 38644481 PMCID: PMC11034020 DOI: 10.1186/s13040-024-00363-3] [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: 01/26/2024] [Accepted: 04/09/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Recent researches have found a strong correlation between the triglyceride-glucose (TyG) index or the atherogenic index of plasma (AIP) and cardiovascular disease (CVD) risk. However, there is a lack of research on non-invasive and rapid prediction of cardiovascular risk. We aimed to develop and validate a machine-learning model for predicting cardiovascular risk based on variables encompassing clinical questionnaires and oculomics. METHODS We collected data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training dataset (80% from the year 2008 to 2011 KNHANES) was used for machine learning model development, with internal validation using the remaining 20%. An external validation dataset from the year 2012 assessed the model's predictive capacity for TyG-index or AIP in new cases. We included 32122 participants in the final dataset. Machine learning models used 25 algorithms were trained on oculomics measurements and clinical questionnaires to predict the range of TyG-index and AIP. The area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score were used to evaluate the performance of our machine learning models. RESULTS Based on large-scale cohort studies, we determined TyG-index cut-off points at 8.0, 8.75 (upper one-third values), 8.93 (upper one-fourth values), and AIP cut-offs at 0.318, 0.34. Values surpassing these thresholds indicated elevated cardiovascular risk. The best-performing algorithm revealed TyG-index cut-offs at 8.0, 8.75, and 8.93 with internal validation AUCs of 0.812, 0.873, and 0.911, respectively. External validation AUCs were 0.809, 0.863, and 0.901. For AIP at 0.34, internal and external validation achieved similar AUCs of 0.849 and 0.842. Slightly lower performance was seen for the 0.318 cut-off, with AUCs of 0.844 and 0.836. Significant gender-based variations were noted for TyG-index at 8 (male AUC=0.832, female AUC=0.790) and 8.75 (male AUC=0.874, female AUC=0.862) and AIP at 0.318 (male AUC=0.853, female AUC=0.825) and 0.34 (male AUC=0.858, female AUC=0.831). Gender similarity in AUC (male AUC=0.907 versus female AUC=0.906) was observed only when the TyG-index cut-off point equals 8.93. CONCLUSION We have established a simple and effective non-invasive machine learning model that has good clinical value for predicting cardiovascular risk in the general population.
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Affiliation(s)
- Yuqi Zhang
- School of Computer Science & Engineering, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Sijin Li
- Department of Cardiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weijie Wu
- Department of Cardiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yanqing Zhao
- Department of Interventional Radiology & Vascular Surgery, Peking University Third Hospital, Beijing, China
| | - Jintao Han
- Department of Interventional Radiology & Vascular Surgery, Peking University Third Hospital, Beijing, China
| | - Chao Tong
- School of Computer Science & Engineering, Beihang University, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
| | - Niansang Luo
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Kun Zhang
- Department of Cardiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
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10
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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11
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Engelmann J, Kearney S, McTrusty A, McKinlay G, Bernabeu MO, Strang N. Retinal Fractal Dimension Is a Potential Biomarker for Systemic Health-Evidence From a Mixed-Age, Primary-Care Population. Transl Vis Sci Technol 2024; 13:19. [PMID: 38607632 PMCID: PMC11019596 DOI: 10.1167/tvst.13.4.19] [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: 09/05/2023] [Accepted: 03/03/2024] [Indexed: 04/13/2024] Open
Abstract
Purpose To investigate whether fractal dimension (FD), a retinal trait relating to vascular complexity and a potential "oculomics" biomarker for systemic disease, is applicable to a mixed-age, primary-care population. Methods We used cross-sectional data (96 individuals; 183 eyes; ages 18-81 years) from a university-based optometry clinic in Glasgow, Scotland, to study the association between FD and systemic health. We computed FD from color fundus images using Deep Approximation of Retinal Traits (DART), an artificial intelligence-based method designed to be more robust to poor image quality. Results Despite DART being designed to be more robust, a significant association (P < 0.001) between image quality and FD remained. Consistent with previous literature, age was associated with lower FD (P < 0.001 univariate and when adjusting for image quality). However, FD variance was higher in older patients, and some patients over 60 had FD comparable to those of patients in their 20s. Prevalent systemic conditions were significantly (P = 0.037) associated with lower FD when adjusting for image quality and age. Conclusions Our work suggests that FD as a biomarker for systemic health extends to mixed-age, primary-care populations. FD decreases with age but might not substantially decrease in everyone. This should be further investigated using longitudinal data. Finally, image quality was associated with FD, but it is unclear whether this finding is measurement error caused by image quality or confounded by age and health. Future work should investigate this to clarify whether adjusting for image quality is appropriate. Translational Relevance FD could potentially be used in regular screening settings, but questions around image quality remain.
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Affiliation(s)
- Justin Engelmann
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Stephanie Kearney
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, UK
| | - Alice McTrusty
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Greta McKinlay
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, UK
| | - Miguel O. Bernabeu
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
- The Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Niall Strang
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, UK
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12
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Dai G, Yu S, Hu S, Luan X, Yan H, Wang X, Song P, Liu X, He X. A Novel Method for the Measurement of Retinal Arteriolar Bifurcation. Ophthalmol Ther 2024; 13:917-933. [PMID: 38294630 PMCID: PMC10912395 DOI: 10.1007/s40123-023-00881-z] [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: 11/23/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024] Open
Abstract
INTRODUCTION The purpose of this research was to develop protocols for evaluating the bifurcation parameters of retinal arteriole and establish a reference range of normal values. METHODS In this retrospective study, we measured a total of 1314 retinal arteriolar bifurcations from 100 fundus photographs. We selected 200 from these bifurcations for testing inter-measurer and inter-method agreement. Additionally, we calculated the normal reference range for retinal arteriolar bifurcation parameters and analyzed the effects of gender, age, and anatomical features on retinal arteriolar bifurcation. RESULTS The measurement method proposed in this study has demonstrated nearly perfect consistency among different measurers, with interclass correlation coefficient (ICC) for all bifurcation parameters of retinal arteriole exceeding 0.95. Among healthy individuals, the retinal arteriolar caliber was narrowest in young adults and increased in children, teenagers, and the elderly; retinal arteriolar caliber was greater in females than in males; and the diameter of the inferior temporal branch exceeded that of the superior temporal branch. The angle between the two branches of retinal arteriolar bifurcation was also greater in females than in males. When using the center of the optic disc as a reference point, the angle between the two branches of the retinal arteriole at the proximal or distal ends increased. In contrast, the estimated optimum theoretical values of retinal arteriolar bifurcation were not affected by these factors. CONCLUSIONS The method for the measurement of retinal arteriolar bifurcation in this study was highly accurate and reproducible. The diameter and branching angle of the retinal arteriolar bifurcation were more susceptible to the influence of gender, age, and anatomical features. In comparison, the estimated optimum theoretical values of retinal arteriolar bifurcation were relatively stable. Video available for this article.
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Affiliation(s)
- Guangzheng Dai
- Dragonfleye Healthcare Technology LLC, Shenyang, China
- He Eye Specialist Hospital, Shenyang, China
| | - Sile Yu
- Department of Public Health, He University, Shenyang, 110034, China
| | - Shenming Hu
- Department of Public Health, He University, Shenyang, 110034, China
| | - Xinze Luan
- Department of Public Health, He University, Shenyang, 110034, China
| | - Hairu Yan
- Dragonfleye Healthcare Technology LLC, Shenyang, China
| | - Xiaoting Wang
- Department of Public Health, He University, Shenyang, 110034, China
| | | | - Xinying Liu
- Dragonfleye Healthcare Technology LLC, Shenyang, China
| | - Xingru He
- Department of Public Health, He University, Shenyang, 110034, China.
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13
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El Husseini N, Schaich CL, Craft S, Rapp SR, Hayden KM, Sharrett R, Cotch MF, Wong TY, Luchsinger JA, Espeland MA, Baker LD, Bertoni AG, Hughes TM. Retinal vessel caliber and cognitive performance: the multi-ethnic study of atherosclerosis (MESA). Sci Rep 2024; 14:4120. [PMID: 38374377 PMCID: PMC10876697 DOI: 10.1038/s41598-024-54412-2] [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: 07/11/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Retinal vessel calibers share anatomic and physiologic characteristics with the cerebral vasculature and can be visualized noninvasively. In light of the known microvascular contributions to brain health and cognitive function, we aimed to determine if, in a community based-study, retinal vessel calibers and change in caliber over 8 years are associated with cognitive function or trajectory. Participants in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort who completed cognitive testing at Exam 5 (2010-2012) and had retinal vascular caliber measurements (Central Retinal Artery and Vein Equivalents; CRAE and CRVE) at Exam 2 (2002-2004) and Exam 5 were included. Using multivariable linear regression, we evaluated the association of CRAE and CRVE from Exam 2 and Exam 5 and their change between the two exams with scores on tests of global cognitive function (Cognitive Abilities Screening Instrument; CASI), processing speed (Digit Symbol Coding; DSC) and working memory (Digit Span; DS) at Exam 5 and with subsequent change in cognitive scores between Exam 5 and Exam 6 (2016-2018).The main effects are reported as the difference in cognitive test score per SD increment in retinal vascular caliber with 95% confidence intervals (CI). A total of 4334 participants (aged 61.6 ± 9.2 years; 53% female; 41% White) completed cognitive testing and at least one retinal assessment. On multivariable analysis, a 1 SD larger CRAE at exam 5 was associated with a lower concomitant CASI score (- 0.24, 95% CI - 0.46, - 0.02). A 1 SD larger CRVE at exam 2 was associated with a lower subsequent CASI score (- 0.23, 95%CI - 0.45, - 0.01). A 1 SD larger CRVE at exam 2 or 5 was associated with a lower DSC score [(- 0.56, 95% CI - 1.02, - 0.09) and - 0.55 (95% CI - 1.03, - 0.07) respectively]. The magnitude of the associations was relatively small (2.8-3.1% of SD). No significant associations were found between retinal vessel calibers at Exam 2 and 5 with the subsequent score trajectory of cognitive tests performance over an average of 6 years. Wider retinal venular caliber was associated with concomitant and future measures of slower processing speed but not with later cognitive trajectory. Future studies should evaluate the utility of these measures in risk stratification models from a clinical perspective as well as for screening on a population level.
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Affiliation(s)
- Nada El Husseini
- Department of Neurology, Duke University Medical Center, Duke South, Purple Zone, Suite 0109, Durham, NC, 27710, USA.
| | - Christopher L Schaich
- Department of Surgery, Hypertension and Vascular Research Center, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Suzanne Craft
- Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Stephen R Rapp
- Psychiatry and Behavioral Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Kathleen M Hayden
- Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Richey Sharrett
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Tien Y Wong
- Department of Ophthalmology and Visual Sciences, National University of Singapore, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Jose A Luchsinger
- Division of General Medicine, Columbia University Medical Center, New York, NY, USA
| | - Mark A Espeland
- Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Laura D Baker
- Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Alain G Bertoni
- Epidemiology and Prevention, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Timothy M Hughes
- Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA
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14
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Huang Y, Cheung CY, Li D, Tham YC, Sheng B, Cheng CY, Wang YX, Wong TY. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye (Lond) 2024; 38:464-472. [PMID: 37709926 PMCID: PMC10858189 DOI: 10.1038/s41433-023-02724-4] [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: 07/02/2023] [Revised: 07/26/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.
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Affiliation(s)
- Yu Huang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ching Yu Cheng
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore.
- Tsinghua Medicine, Tsinghua University, Beijing, China.
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
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15
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Wang X, Fang J, Yang L. Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons. Open Life Sci 2024; 19:20220773. [PMID: 38299009 PMCID: PMC10828665 DOI: 10.1515/biol-2022-0773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/20/2023] [Accepted: 10/19/2023] [Indexed: 02/02/2024] Open
Abstract
The purpose of this study was to explore the related research progress of ocular complications (OCs) caused by type 2 diabetes mellitus (T2DM), tear and tarsal function, and the application of deep learning (DL) in the diagnosis of diabetes and OCs caused by it, to provide reference for the prevention and control of OCs in T2DM patients. This study reviewed the pathogenesis and treatment of diabetes retinopathy, keratopathy, dry eye disease, glaucoma, and cataract, analyzed the relationship between OCs and tear function and tarsal function, and discussed the application value of DL in the diagnosis of diabetes and OCs. Diabetes retinopathy is related to hyperglycemia, angiogenic factors, oxidative stress, hypertension, hyperlipidemia, and other factors. The increase in water content in the corneal stroma leads to corneal relaxation, loss of transparency, and elasticity, and can lead to the occurrence of corneal lesions. Dry eye syndrome is related to abnormal stability of the tear film and imbalance in neural and immune regulation. Elevated intraocular pressure, inflammatory reactions, atrophy of the optic nerve head, and damage to optic nerve fibers are the causes of glaucoma. Cataract is a common eye disease in the elderly, which is a visual disorder caused by lens opacity. Oxidative stress is an important factor in the occurrence of cataracts. In clinical practice, blood sugar control, laser therapy, and drug therapy are used to control the above eye complications. The function of tear and tarsal plate will be affected by eye diseases. Retinopathy and dry eye disease caused by diabetes will cause dysfunction of tear and tarsal plate, which will affect the eye function of patients. Furthermore, DL can automatically diagnose and classify eye diseases, automatically analyze fundus images, and accurately diagnose diabetes retinopathy, macular degeneration, and other diseases by analyzing and processing eye images and data. The treatment of T2DM is difficult and prone to OCs, which seriously threatens the normal life of patients. The occurrence of OCs is closely related to abnormal tear and tarsal function. Based on DL, clinical diagnosis and treatment of diabetes and its OCs can be carried out, which has positive application value.
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Affiliation(s)
- Xiaohong Wang
- Department of Operating Room, Xinchang County Peoples Hospital, Xinchang, 312500, Shaoxing City, Zhejiang, China
| | - Jian Fang
- Department of Ophthalmolgy, Xinchang County Peoples Hospital, Xinchang, 312500, Shaoxing City, Zhejiang, China
| | - Lina Yang
- Department of Ophthalmolgy, Xinchang County Peoples Hospital, Xinchang, 312500, Shaoxing City, Zhejiang, China
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16
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Zekavat SM, Jorshery SD, Rauscher FG, Horn K, Sekimitsu S, Koyama S, Nguyen TT, Costanzo MC, Jang D, Burtt NP, Kühnapfel A, Shweikh Y, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè AV, Wiggs JL, Del Priore L, Scholz M, Wang JC, Natarajan P, Zebardast N. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Sci Transl Med 2024; 16:eadg4517. [PMID: 38266105 DOI: 10.1126/scitranslmed.adg4517] [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: 12/27/2022] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.
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Affiliation(s)
- Seyedeh Maryam Zekavat
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Saman Doroodgar Jorshery
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Computer Science/Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Franziska G Rauscher
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
- Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig 04103, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
| | | | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Trang T Nguyen
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Maria C Costanzo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dongkeun Jang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Noël P Burtt
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andreas Kühnapfel
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT 06511, USA
| | - Vineet Raghu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale School of Medicine, New Haven, CT 06511, USA
- School of Public Health, Yale University, New Haven, CT 06510, USA
| | - Marzyeh Ghassemi
- Departments of Computer Science/Medicine, University of Toronto, Toronto, ON M5S 1A1, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tobias Elze
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Ayellet V Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lucian Del Priore
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT 06510, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig 04107, Germany
- Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig 04103, Germany
| | - Jay C Wang
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT 06510, USA
- Northern California Retina Vitreous Associates, Mountain View, CA 94040, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Sullivan BA, Beam K, Vesoulis ZA, Aziz KB, Husain AN, Knake LA, Moreira AG, Hooven TA, Weiss EM, Carr NR, El-Ferzli GT, Patel RM, Simek KA, Hernandez AJ, Barry JS, McAdams RM. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol 2024; 44:1-11. [PMID: 38097685 PMCID: PMC10872325 DOI: 10.1038/s41372-023-01848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.
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Affiliation(s)
- Brynne A Sullivan
- Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Khyzer B Aziz
- Division of Neonatology, Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
| | - Ameena N Husain
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Lindsey A Knake
- Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Alvaro G Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Thomas A Hooven
- Division of Newborn Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elliott M Weiss
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Treuman Katz Center for Pediatric Bioethics and Palliative Care, Seattle Children's Research Institute, Seattle, WA, USA
| | - Nicholas R Carr
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - George T El-Ferzli
- Division of Neonatology, Department of Pediatrics, Ohio State University, Nationwide Children's Hospital, Columbus, OH, USA
| | - Ravi M Patel
- Division of Neonatology, Department of Pediatrics, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Kelsey A Simek
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Antonio J Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - James S Barry
- Division of Neonatology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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18
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Li W, Lai Z, Tang N, Tang F, Huang G, Lu P, Jiang L, Lei D, Xu F. Diabetic retinopathy related homeostatic dysregulation and its association with mortality among diabetes patients: A cohort study from NHANES. Diabetes Res Clin Pract 2024; 207:111081. [PMID: 38160736 DOI: 10.1016/j.diabres.2023.111081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/17/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024]
Abstract
AIMS To develop a metric termed the diabetic retinopathy-related homeostatic dysregulation (DRHD) value, and estimate its association with future risk of mortality in individuals with type 2 diabetes. METHODS With the data of the NHANES, the biomarkers associated with DR were identified from 40 clinical parameters using LASSO regression. Subsequently, the DRHD value was constructed utilizing the Mahalanobis distance approach. In the retrospective cohortof 6420 type 2 diabetes patients, we estimated the associations between DRHD values and mortality related to all-cause, cardiovascular disease (CVD) and diabetes-specific causes using Cox proportional hazards regression models. RESULTS A set of 14 biomarkers associated with DR was identified for the construction of DRHD value. During an average of 8 years of follow-up, the multivariable-adjusted HRs and corresponding 95 % CIs for the highest quartiles of DRHD values were 2.04 (1.76, 2.37), 2.32 (1.78, 3.01), and 2.29 (1.72, 3.04) for all-cause, CVD and diabetes-specific mortality, respectively. Furthermore, we developed a web-based calculator for the DRHD value to enhance its accessibility and usability (https://dzwxl-drhd.streamlit.app/). CONCLUSIONS Our study constructed the DRHD value as a measure to assess homeostatic dysregulation among individuals with type 2 diabetes. The DRHD values exhibited potential as a prognostic indicator for retinopathy and for mortality in patients affected by type 2 diabetes.
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Affiliation(s)
- Wenxiang Li
- Nanjing Medical University, Nanjing 210000, China
| | - Zhaoguang Lai
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Ningning Tang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Fen Tang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Guangyi Huang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Peng Lu
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Li Jiang
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China
| | - Daizai Lei
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China.
| | - Fan Xu
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning 530021, China.
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19
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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20
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Triantafyllou A, Anyfanti P, Koletsos N, Malliora A, Lamprou S, Dipla K, Gkaliagkousi E. Clinical Significance of Altered Vascular Morphology and Function in Normotension. Curr Hypertens Rep 2023; 25:287-297. [PMID: 37392357 PMCID: PMC10505095 DOI: 10.1007/s11906-023-01251-7] [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] [Accepted: 06/05/2023] [Indexed: 07/03/2023]
Abstract
PURPOSE OF REVIEW To review current literature examining the presence of subclinical micro- and macrovascular alterations in normotensive individuals and their clinical significance in terms of hypertension prediction. Emphasis is placed on alterations that can be detected in peripheral vascular beds using non-invasive, easily applicable methodology, as these are in general easier to capture and evaluate in clinical practice compared to more complex invasive or functional tests. RECENT FINDINGS Arterial stiffness, increased carotid intima-media thickness, and altered retinal microvascular diameters predict the progression from the normotensive to the hypertensive state. By contrast, there is substantial lack of relevant prospective studies for skin microvascular alterations. Although conclusions regarding causality cannot be safely deduced from available studies, detection of morphological and functional vascular alterations in normotensive individuals emerges as a sensitive indicator of progression to hypertension and hence increased CVD risk. An increasing amount of evidence suggests that early detection of subclinical micro- and macrovascular alterations would be clinically useful for the early identification of individuals at high risk for future hypertension onset. Methodological issues and gaps in knowledge need to be addressed before detection of such changes could guide the development of strategies to prevent new-onset hypertension in normotensive individuals.
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Affiliation(s)
- A Triantafyllou
- Third Department of Internal Medicine, Papageorgiou General Hospital, Aristotle University of Thessaloniki, 56429, Thessaloniki, Greece.
| | - P Anyfanti
- Second Medical Department, Hippokration Hospital, Aristotle University of Thessaloniki, 54642, Thessaloniki, Greece
| | - N Koletsos
- Third Department of Internal Medicine, Papageorgiou General Hospital, Aristotle University of Thessaloniki, 56429, Thessaloniki, Greece
| | - A Malliora
- Third Department of Internal Medicine, Papageorgiou General Hospital, Aristotle University of Thessaloniki, 56429, Thessaloniki, Greece
| | - S Lamprou
- Third Department of Internal Medicine, Papageorgiou General Hospital, Aristotle University of Thessaloniki, 56429, Thessaloniki, Greece
| | - K Dipla
- Physiology & Biochemistry Laboratory, Department of Sport Sciences at Serres, Aristotle University of Thessaloniki, 62100, Serres, Greece
| | - E Gkaliagkousi
- Third Department of Internal Medicine, Papageorgiou General Hospital, Aristotle University of Thessaloniki, 56429, Thessaloniki, Greece
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21
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Fu Y, Yusufu M, Wang Y, He M, Shi D, Wang R. Association of retinal microvascular density and complexity with incident coronary heart disease. Atherosclerosis 2023; 380:117196. [PMID: 37562159 DOI: 10.1016/j.atherosclerosis.2023.117196] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND AND AIMS The high mortality rate and huge disease burden of coronary heart disease (CHD) highlight the importance of its early detection and timely intervention. Given the non-invasive nature of fundus photography and recent development in the quantification of retinal microvascular parameters with deep learning techniques, our study aims to investigate the association between incident CHD and retinal microvascular parameters. METHODS UK Biobanks participants with gradable fundus images and without a history of diagnosed CHD at recruitment were included for analysis. A fully automated artificial intelligence system was used to extract quantitative measurements that represent the density and complexity of the retinal microvasculature, including fractal dimension (Df), number of vascular segments (NS), vascular skeleton density (VSD) and vascular area density (VAD). RESULTS A total of 57,947 participants (mean age 55.6 ± 8.1 years; 56% female) without a history of diagnosed CHD were included. During a median follow-up of 11.0 (interquartile range, 10.88 to 11.19) years, 3211 incident CHD events occurred. In multivariable Cox proportional hazards models, we found decreasing Df (adjusted HR = 0.80, 95% CI, 0.65-0.98, p = 0.033), lower NS of arteries (adjusted HR = 0.69, 95% CI, 0.54-0.88, p = 0.002) and venules (adjusted HR = 0.77, 95% CI, 0.61-0.97, p = 0.024), and reduced arterial VSD (adjusted HR = 0.72, 95% CI, 0.57-0.91, p = 0.007) and venous VSD (adjusted HR = 0.78, 95% CI, 0.62-0.98, p = 0.034) were related to an increased risk of incident CHD. CONCLUSIONS Our study revealed a significant association between retinal microvascular parameters and incident CHD. As the lower complexity and density of the retinal vascular network may indicate an increased risk of incident CHD, this may empower its prediction with the quantitative measurements of retinal structure.
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Affiliation(s)
- Yuechuan Fu
- Department of Ophthalmology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Mayinuer Yusufu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Mingguang He
- The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China.
| | - Danli Shi
- The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China.
| | - Ruobing Wang
- Department of Ophthalmology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
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22
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Zhong P, Tan S, Zhu Z, Zhang J, Chen S, Huang W, He M, Wang W. Brain and Cognition Signature Fingerprinting Vascular Health in Diabetic Individuals: An International Multi-Cohort Study. Am J Geriatr Psychiatry 2023; 31:570-582. [PMID: 37230837 DOI: 10.1016/j.jagp.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/16/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023]
Abstract
OBJECTIVE To evaluate the correlation between cognitive signatures and the risk of diabetic vascular complications and mortality, based on a multicountry prospective study. METHODS The participants comprised 27,773 diabetics from the UK Biobank (UKB) and 1307 diabetics from the Guangzhou Diabetic Eye Study (GDES) cohort. The exposures were brain volume and cognitive screening tests for UKB participants, whilst the global cognitive score (GCS) measuring orientation to time and attention, episodic memory, and visuospatial abilities were determined for GDES participants. The outcomes for the UKB group were mortality, as well as macrovascular (myocardial infarction [MI] and stroke), microvascular (end-stage renal disease [ESRD], and diabetic retinopathy [DR]) events. The outcomes for the GDES group were retinal and renal microvascular damage. RESULTS In the UKB group, a 1-SD reduction in brain gray matter volume was associated with 34%-77% higher risks of incident MI, ESRD, and DR. The presence of impaired memory was associated with 18%-73% higher risk of mortality and ESRD; impaired reaction was associated with 1.2-1.7-fold higher risks of mortality, stroke, ESRD, and DR. In the GDES group, the lowest GCS tertile exhibited 1.4-2.2-fold higher risk of developing referable DR and a twofold faster decline in renal function and retinal capillary density compared with the highest tertile. Restricting data analysis to individuals aged less than 65 years produced consistent results. CONCLUSION Cognitive decline significantly elevates the risk of diabetic vascular complications and is correlated with retinal and renal microcirculation damage. Cognitive screening tests are strongly recommended as routine tools for management of diabetes.
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Affiliation(s)
- Pingting Zhong
- State Key Laboratory of Ophthalmology (PZ, SC, WH, MH, WW), Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shaoying Tan
- School of Optometry (ST, MH), The Hong Kong Polytechnic University, Hong Kong, China; Research Centre for SHARP Vision (ST, MH), The Hong Kong Polytechnic University, Hong Kong, China; Centre for Eye and Vision Research (CEVR) (ST, MH), 17W Hong Kong Science Park, Hong Kong
| | - Zhuoting Zhu
- Centre for Eye Research Australia (ZZ, JZ, MH), Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Junyao Zhang
- Centre for Eye Research Australia (ZZ, JZ, MH), Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Shida Chen
- State Key Laboratory of Ophthalmology (PZ, SC, WH, MH, WW), Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology (PZ, SC, WH, MH, WW), Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology (PZ, SC, WH, MH, WW), Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China; School of Optometry (ST, MH), The Hong Kong Polytechnic University, Hong Kong, China; Research Centre for SHARP Vision (ST, MH), The Hong Kong Polytechnic University, Hong Kong, China; Centre for Eye and Vision Research (CEVR) (ST, MH), 17W Hong Kong Science Park, Hong Kong; Centre for Eye Research Australia (ZZ, JZ, MH), Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology (PZ, SC, WH, MH, WW), Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
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23
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Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel) 2023; 11:1739. [PMID: 37372857 DOI: 10.3390/healthcare11121739] [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: 04/15/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.
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Affiliation(s)
- Huimin Li
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznan, Poland
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China
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24
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Zekavat SM, Jorshery SD, Shweikh Y, Horn K, Rauscher FG, Sekimitsu S, Kayoma S, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè AV, Wiggs JL, Scholz M, Priore LD, Wang JC, Natarajan P, Zebardast N. Insights into human health from phenome- and genome-wide analyses of UK Biobank retinal optical coherence tomography phenotypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.16.23290063. [PMID: 37292770 PMCID: PMC10246137 DOI: 10.1101/2023.05.16.23290063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The human retina is a complex multi-layered tissue which offers a unique window into systemic health and disease. Optical coherence tomography (OCT) is widely used in eye care and allows the non-invasive, rapid capture of retinal measurements in exquisite detail. We conducted genome- and phenome-wide analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed phenome-wide association analyses, associating retinal thicknesses with 1,866 incident ICD-based conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association analyses, identifying inherited genetic markers which influence the retina, and replicated our associations among 6,313 individuals from the LIFE-Adult Study. And lastly, we performed comparative association of phenome- and genome- wide associations to identify putative causal links between systemic conditions, retinal layer thicknesses, and ocular disease. Independent associations with incident mortality were detected for photoreceptor thinning and ganglion cell complex thinning. Significant phenotypic associations were detected between retinal layer thinning and ocular, neuropsychiatric, cardiometabolic and pulmonary conditions. Genome-wide association of retinal layer thicknesses yielded 259 loci. Consistency between epidemiologic and genetic associations suggested putative causal links between thinning of the retinal nerve fiber layer with glaucoma, photoreceptor segment with AMD, as well as poor cardiometabolic and pulmonary function with PS thinning, among other findings. In conclusion, retinal layer thinning predicts risk of future ocular and systemic disease. Furthermore, systemic cardio-metabolic-pulmonary conditions promote retinal thinning. Retinal imaging biomarkers, integrated into electronic health records, may inform risk prediction and potential therapeutic strategies.
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Affiliation(s)
- Seyedeh Maryam Zekavat
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saman Doroodgar Jorshery
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Computer Science/Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Germany and Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany
| | - Franziska G. Rauscher
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Germany and Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany
| | | | - Satoshi Kayoma
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Vineet Raghu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- School of Public Health, Yale University, New Haven, CT, USA
| | - Marzyeh Ghassemi
- Departments of Computer Science/Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tobias Elze
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ayellet V. Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Janey L. Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology University of Leipzig, Germany and Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany
| | - Lucian Del Priore
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA
| | - Jay C. Wang
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA
- Northern California Retina Vitreous Associates, Mountain View, CA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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25
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Villaplana-Velasco A, Pigeyre M, Engelmann J, Rawlik K, Canela-Xandri O, Tochel C, Lona-Durazo F, Mookiah MRK, Doney A, Parra EJ, Trucco E, MacGillivray T, Rannikmae K, Tenesa A, Pairo-Castineira E, Bernabeu MO. Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes. Commun Biol 2023; 6:523. [PMID: 37188768 PMCID: PMC10185685 DOI: 10.1038/s42003-023-04836-9] [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: 08/18/2022] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, Df, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of Df is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of Df and analyse its relationship with CAD. We replicated 5 Df loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to Df variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between Df and CAD, and between Df and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of Df loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, Df, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that Df provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of Df, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.
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Affiliation(s)
- Ana Villaplana-Velasco
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Marie Pigeyre
- Population Health Research Institute (PHRI), Department of Medicine, Faculty of Health Sciences, McMaster University, McMaster University, Hamilton, Ontario, Canada
| | - Justin Engelmann
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Claire Tochel
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | | | | | - Alex Doney
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Esteban J Parra
- University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Tom MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Kristiina Rannikmae
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Erola Pairo-Castineira
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Miguel O Bernabeu
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK.
- The Bayes Centre, The University of Edinburgh, Edinburgh, Scotland, UK.
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26
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Chen Y, Liu Y, Cong L, Liu A, Song X, Liu W, Hua R, Shen Q, Shao Y, Xue Y, Yao Q, Zhang Y. Sleeve gastrectomy improved microvascular phenotypes from obesity cohort, detected with optical coherence tomography angiography. J Diabetes 2023; 15:313-324. [PMID: 36872300 PMCID: PMC10101840 DOI: 10.1111/1753-0407.13374] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/02/2023] [Accepted: 02/17/2023] [Indexed: 03/07/2023] Open
Abstract
AIMS To examine how metabolic status is associated with microvascular phenotype and to identify variables associated with vascular remodeling after bariatric surgery, using noninvasive optical coherence tomography angiography (OCTA). METHODS The study included 136 obese subjects scheduled for bariatric surgery and 52 normal-weight controls. Patients with obesity were divided into metabolically healthy obesity (MHO) and metabolic syndrome (MetS) groups according to the diagnosis criteria of the Chinese Diabetes Society. Retinal microvascular parameters were measured by OCTA, including superficial capillary plexus (SCP) and deep capillary plexus (DCP) vessel densities. Follow-ups were performed at the baseline and 6 months after bariatric surgery. RESULTS Fovea SCP, average DCP, fovea DCP, parafovea DCP, and perifovea DCP vessel densities were significantly lower in the MetS group, compared to controls (19.91% vs. 22.49%, 51.60% vs. 54.20%, 36.64% vs. 39.14%, 56.24% vs. 57.65% and 52.59% vs. 55.58%, respectively, all p < .05). Parafovea SCP, average DCP, parafovea DCP, and perifovea DCP vessel densities significantly improved in patients with obesity 6 months after surgery, compared to baseline (54.21% vs. 52.97%, 54.43% vs. 50.95%, 58.29% vs. 55.54% and 55.76% vs. 51.82%, respectively, all p < .05). Multivariable analyses showed that baseline blood pressure and insulin were independent predictors of vessel density changes 6 months after surgery. CONCLUSIONS Retinal microvascular impairment occurred mainly in MetS rather than MHO patients. Retinal microvascular phenotype improved 6 months after bariatric surgery and baseline blood pressure and insulin status may be key determinants. OCTA may be a reliable method to evaluate the microvascular complications associated with obesity.
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Affiliation(s)
- Yaying Chen
- Department of OphthalmologyHuadong Hospital, Fudan UniversityShanghaiChina
- Department of OphthalmologyHuashan Hospital, Fudan UniversityShanghaiChina
| | - Yanyang Liu
- Center for Obesity and Metabolic SurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Lin Cong
- Department of OphthalmologyHuashan Hospital, Fudan UniversityShanghaiChina
| | - Ailin Liu
- Department of UltrasoundHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiangyuan Song
- Department of OphthalmologyHuashan Hospital, Fudan UniversityShanghaiChina
| | - Wenting Liu
- Department of OphthalmologyHuadong Hospital, Fudan UniversityShanghaiChina
| | - Rong Hua
- Center for Obesity and Metabolic SurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Qiwei Shen
- Center for Obesity and Metabolic SurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Yikai Shao
- Center for Obesity and Metabolic SurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Yiwen Xue
- Department of OphthalmologyHuashan Hospital, Fudan UniversityShanghaiChina
| | - Qiyuan Yao
- Center for Obesity and Metabolic SurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Yuyan Zhang
- Department of OphthalmologyHuadong Hospital, Fudan UniversityShanghaiChina
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Chan YK, Cheng CY, Sabanayagam C. Eyes as the windows into cardiovascular disease in the era of big data. Taiwan J Ophthalmol 2023; 13:151-167. [PMID: 37484607 PMCID: PMC10361436 DOI: 10.4103/tjo.tjo-d-23-00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
Cardiovascular disease (CVD) is a major cause of mortality and morbidity worldwide and imposes significant socioeconomic burdens, especially with late diagnoses. There is growing evidence of strong correlations between ocular images, which are information-dense, and CVD progression. The accelerating development of deep learning algorithms (DLAs) is a promising avenue for research into CVD biomarker discovery, early CVD diagnosis, and CVD prognostication. We review a selection of 17 recent DLAs on the less-explored realm of DL as applied to ocular images to produce CVD outcomes, potential challenges in their clinical deployment, and the path forward. The evidence for CVD manifestations in ocular images is well documented. Most of the reviewed DLAs analyze retinal fundus photographs to predict CV risk factors, in particular hypertension. DLAs can predict age, sex, smoking status, alcohol status, body mass index, mortality, myocardial infarction, stroke, chronic kidney disease, and hematological disease with significant accuracy. While the cardio-oculomics intersection is now burgeoning, very much remain to be explored. The increasing availability of big data, computational power, technological literacy, and acceptance all prime this subfield for rapid growth. We pinpoint the specific areas of improvement toward ubiquitous clinical deployment: increased generalizability, external validation, and universal benchmarking. DLAs capable of predicting CVD outcomes from ocular inputs are of great interest and promise to individualized precision medicine and efficiency in the provision of health care with yet undetermined real-world efficacy with impactful initial results.
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Affiliation(s)
- Yarn Kit Chan
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charumathi Sabanayagam
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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28
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Wang J, Chen T, Qi X, Li Y, Yang X, Meng X. Retinal vascular fractal dimension measurements in patients with obstructive sleep apnea syndrome: a retrospective case-control study. J Clin Sleep Med 2023; 19:479-490. [PMID: 36458734 PMCID: PMC9978437 DOI: 10.5664/jcsm.10370] [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: 06/06/2022] [Revised: 10/25/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022]
Abstract
STUDY OBJECTIVES We performed a case-control study to investigate the correlation between the apnea-hypopnea index (AHI) and the retinal vascular fractal dimension (FD). METHODS We selected 527 individuals who underwent polysomnography during health checkups at the Huadong Sanatorium from January to December 2021 as the study population, of whom 468 were included and 59 were excluded. All participants underwent a detailed health examination, including medical history assessment, physical examination, assessment of lifestyle factors, fundus photography, and laboratory examinations. The retinal vasculature was quantitatively assessed using Singapore I Vessel Assessment (SIVA) software. The relationship between the AHI and the retinal vessel quantitative was examined by multiple linear regression analyses and restricted cubic spline. RESULTS Among the 468 studied individuals, the average age was 51.51 (43-58) years, with 369 (78.85%) men and 99 (21.15%) women. According to the AHI indicator, 355 individuals were diagnosed with obstructive sleep apnea (OSA) syndrome, with an average AHI of 17.00 (9.200-30.130) events/h; 113 individuals were classified as controls, with an average AHI of 2.13 (0.88-3.63) events/h. In multiple linear regression, following varying degrees of adjustment for confounding factors, FD was reduced by 0.013 (P = .012; 95% confidence interval [CI]: -0.024 to -0.003), FD arteriole (FDa) was reduced by 0.013 (P = .019; 95% CI: -0.024 to -0.002), and FD venule (FDv) was reduced by 0.014 (P = .08; 95% CI: -0.024 to -0.004) in the high-AHI group compared with the low-AHI group. All tests for trend P values were < .05. The restricted cubic spline in the overall OSA population and the individuals without diabetes revealed a U-shaped pattern of decreasing, then increasing, FD, FDa, and FDv with a rising AHI. In the OSA individual with diabetes, FD, FDa, and FDv gradually decreased with increasing AHI. CONCLUSIONS The FD is associated with AHI in OSA individuals. The link between AHI and FD varied for OSA individuals with and without diabetes. CITATION Wang J, Chen T, Qi X, Li Y, Yang X, Meng X. Retinal vascular fractal dimension measurements in patients with obstructive sleep apnea syndrome: a retrospective case-control study. J Clin Sleep Med. 2023;19(3):479-490.
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Affiliation(s)
- Jing Wang
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Tingli Chen
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Xing Qi
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Yihan Li
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Xiaolong Yang
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Xiangming Meng
- Department of Otolaryngology, Wuxi Huishan District People’s Hospital, Luoshe Town, Huishan District, Wuxi, China
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29
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Vukadinovic M, Renjith G, Yuan V, Kwan A, Cheng SC, Li D, Clarke SL, Ouyang D. Impact of Measurement Imprecision on Genetic Association Studies of Cardiac Function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286058. [PMID: 36824841 PMCID: PMC9949184 DOI: 10.1101/2023.02.16.23286058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
Background Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses. Methods Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses. Results Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies. Conclusions Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.
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Affiliation(s)
- Milos Vukadinovic
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Gauri Renjith
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Victoria Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA
| | - Alan Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Susan C Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Shoa L Clarke
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
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30
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286035. [PMID: 36824893 PMCID: PMC9949187 DOI: 10.1101/2023.02.16.23286035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M. O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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31
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Abstract
The eye is the window through which light is transmitted and visual sensory signalling originates. It is also a window through which elements of the cardiovascular and nervous systems can be directly inspected, using ophthalmoscopy or retinal imaging. Measurements of ocular parameters may therefore offer important information on the physiology and homeostasis of these two important systems. Here we report the results of a genetic characterisation of retinal vasculature. Four genome-wide association studies performed on different aspects of retinal vasculometry phenotypes, such as arteriolar and venular tortuosity and width, found significant similarities between retinal vascular characteristics and cardiometabolic health. Our analyses identified 119 different regions of association with traits of retinal vasculature, including 89 loci associated arteriolar tortuosity, the strongest of which was rs35131825 (p = 2.00×10-108), 2 loci with arteriolar width (rs12969347, p = 3.30×10-09 and rs5442, p = 1.9E-15), 17 other loci associated with venular tortuosity and 11 novel associations with venular width. Our causal inference analyses also found that factors linked to arteriolar tortuosity cause elevated diastolic blood pressure and not vice versa.
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32
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Lian TY, Yan Y, Ding D, Ma YJ, Zhang X, Jing ZC. Building a modern six-dimensional biobank fosters the future of precision medicine. Sci Bull (Beijing) 2022; 67:2490-2493. [PMID: 36604021 DOI: 10.1016/j.scib.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Affiliation(s)
- Tian-Yu Lian
- The Medical Science Research Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yi Yan
- Heart Center and Shanghai Institute of Pediatric Congenital Heart Disease, Shanghai Children's Medical Center, National Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Dong Ding
- The Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yue-Jiao Ma
- The Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Xue Zhang
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Zhi-Cheng Jing
- The Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China.
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Günthner R, Streese L, Angermann S, Lorenz G, Braunisch MC, Matschkal J, Hausinger R, Stadler D, Haller B, Heemann U, Kotliar K, Hanssen H, Schmaderer C. Mortality prediction of retinal vessel diameters and function in a long-term follow-up of haemodialysis patients. Cardiovasc Res 2022; 118:3239-3249. [PMID: 35576475 DOI: 10.1093/cvr/cvac073] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/07/2022] [Accepted: 04/20/2022] [Indexed: 01/25/2023] Open
Abstract
AIM Retinal vessel diameters are candidate biomarkers of mortality prediction in large population-based studies. We aimed to investigate the predictive value of retinal vessel diameters and flicker-induced retinal arteriolar and venular dilation on all-cause mortality in long-term follow-up of haemodialysis patients. METHODS AND RESULTS Retinal vessel diameters as well as maximum arteriolar (aMax) and venular dilation (vMax) were investigated in 275 and 214 haemodialysis patients, respectively. Patients were observed in a long-term follow-up for a median period of 73 months. About 36% (76/214) and 41% (113/275) of patients died. Arteriolar and venular diameters were 175 ± 19 and 208 ± 20 µm, respectively. Median aMax and vMax were 1.6 (0.3-3.3) and 3.2 (2.0-5.1)%. Patients within the lowest tertile of vMax showed lower 5-year survival rates compared with the highest tertile (50.6 vs. 82.1%) and also exhibited a higher incidence of infection-related deaths (21.7 vs. 4.0%). Univariate hazard ratio (HR) per standard deviation increase of vMax for all-cause mortality was 0.69 (0.54-0.88) and was even more pronounced for infection-related mortality [HR 0.53 (0.33-0.83)]. Regarding all-cause mortality, multivariate adjustment for eight non-retinal mortality predictors including interleukin-6 did not attenuate the HR relevantly [0.73 (0.54-0.98)]. Arteriolar and venular diameters did not predict all-cause nor cardiovascular and infection-related mortality. CONCLUSIONS Long-term follow-up of patients on haemodialysis demonstrated the potential of retinal venular dilation capacity for mortality prediction, which was most pronounced for infection-related mortality. In the same cohort, retinal arteriolar and venular diameters showed no predictive value for hard endpoints. Retinal venular dilation but not arteriolar and venular diameters is a valuable diagnostic biomarker for risk prediction in patients with end-stage renal disease and should be considered for monitoring of critically ill patients.
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Affiliation(s)
- Roman Günthner
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Lukas Streese
- Division Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Susanne Angermann
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Georg Lorenz
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Matthias C Braunisch
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Julia Matschkal
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Renate Hausinger
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - David Stadler
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Bernhard Haller
- Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Uwe Heemann
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Konstantin Kotliar
- Department of Medical Engineering and Technomathematics, Aachen University of Applied Sciences, Jülich, Germany
| | - Henner Hanssen
- Division Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Christoph Schmaderer
- Department of Nephrology, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany
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Barriada RG, Masip D. An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images. Diagnostics (Basel) 2022; 13:diagnostics13010068. [PMID: 36611360 PMCID: PMC9818382 DOI: 10.3390/diagnostics13010068] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most prevalent causes of premature death. Early detection is crucial to prevent and address CVDs in a timely manner. Recent advances in oculomics show that retina fundus imaging (RFI) can carry relevant information for the early diagnosis of several systemic diseases. There is a large corpus of RFI systematically acquired for diagnosing eye-related diseases that could be used for CVDs prevention. Nevertheless, public health systems cannot afford to dedicate expert physicians to only deal with this data, posing the need for automated diagnosis tools that can raise alarms for patients at risk. Artificial Intelligence (AI) and, particularly, deep learning models, became a strong alternative to provide computerized pre-diagnosis for patient risk retrieval. This paper provides a novel review of the major achievements of the recent state-of-the-art DL approaches to automated CVDs diagnosis. This overview gathers commonly used datasets, pre-processing techniques, evaluation metrics and deep learning approaches used in 30 different studies. Based on the reviewed articles, this work proposes a classification taxonomy depending on the prediction target and summarizes future research challenges that have to be tackled to progress in this line.
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Rudnicka AR, Welikala R, Barman S, Foster PJ, Luben R, Hayat S, Khaw KT, Whincup P, Strachan D, Owen CG. Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke. Br J Ophthalmol 2022; 106:1722-1729. [PMID: 36195457 PMCID: PMC9685715 DOI: 10.1136/bjo-2022-321842] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/03/2022] [Indexed: 02/02/2023]
Abstract
AIMS We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. METHODS AI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). RESULTS UKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 statistics between 0.75-0.77 and 0.33-0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. CONCLUSION RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.
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Affiliation(s)
| | - Roshan Welikala
- Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK
| | - Sarah Barman
- Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK
| | - Paul J Foster
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, London, UK
| | - Robert Luben
- MRC Epidemiology Unit, Cambridge University, Cambridge, UK
| | - Shabina Hayat
- Department of Psychiatry, Cambridge Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Kay-Tee Khaw
- MRC Epidemiology Unit, Cambridge University, Cambridge, UK
| | - Peter Whincup
- Population Health Research Institute, St George's University of London, London, UK
| | - David Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Christopher G Owen
- Population Health Research Institute, St George's University of London, London, UK
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36
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Chaikijurajai T, Ehlers JP, Tang WHW. Retinal Microvasculature: A Potential Window Into Heart Failure Prevention. JACC. HEART FAILURE 2022; 10:785-791. [PMID: 36328644 DOI: 10.1016/j.jchf.2022.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/06/2022] [Accepted: 07/11/2022] [Indexed: 06/16/2023]
Abstract
Endothelial dysfunction and microvascular disease have been shown to play an important role in the development and progression of heart failure (HF). Retinal imaging provides a unique opportunity to noninvasively assess vascular structure and function, vessel features, and microcirculation within the retina. Accumulating evidence suggests that retinal vessel caliber, microvascular features, and vascular characteristics extracted from various imaging modalities are associated with alterations in left ventricular structure and function in stage B HF, as well as incident development of symptomatic HF in the general population. Moreover, dynamic retinal vessel analysis has been shown to differentiate HF patients based on their phenotypes. Given the increasing availability of rapid image acquisition devices (eg, nonmydriatic widefield systems and smartphone-based retinal cameras) and the integration of artificial intelligence-based interrogation/assessment techniques, retinal imaging is a promising noninvasive tool, in conjunction with cardiac imaging and biomarkers, to prevent HF and risk stratify those at risk of developing HF. This review focuses on the current evidence on retinal microvasculature changes, and potential clinical relevance and promising utility of retinal imaging in HF.
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Affiliation(s)
- Thanat Chaikijurajai
- Kaufman Center for Heart Failure Treatment and Recovery, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - W H Wilson Tang
- Kaufman Center for Heart Failure Treatment and Recovery, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
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Evolution of Quantitative Optical Coherence Tomography Angiography Markers with Glycemic Control: A Pilot Study. Biomedicines 2022; 10:biomedicines10102421. [PMID: 36289683 PMCID: PMC9598627 DOI: 10.3390/biomedicines10102421] [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: 08/30/2022] [Revised: 09/08/2022] [Accepted: 09/21/2022] [Indexed: 11/17/2022] Open
Abstract
Aim: We aimed to analyze changes in retinal microvascularization with intensive reduction of glycated hemoglobin A1c (HbA1c) in patients with poorly controlled diabetes using quantitative optical coherence tomography angiography (OCT-A) metrics. Method: This was a retrospective observational study in patients with uncontrolled diabetes admitted to the hospital for glycemic control. A second set of 15 healthy volunteers was included to serve as a control group. OCT-A was performed at inclusion and at 3 months to measure foveal avascular zone area (FAZA), vessel density (VD) of the superficial capillary plexus (SCP) and deep capillary plexus (DCP), acircularity index (AI), and fractal dimension (FD). Results: This analysis included 35 patients (35 eyes): 28 type-2 diabetics and 7 type-1 diabetics. Mean HbA1c was 13.1 ± 2.0% at inclusion and 7.0 ± 1.5% at 3 months. In the short period from inclusion to 3 months post-inclusion, patients showed significant decrease in VD−DCP (28.8% vs. 27.8%; p = 0.014), a significant increase in FAZA (0.300 mm2 vs. 0.310 mm2; p < 0.001), and a significant increase in AI (1.31 vs. 1.34; p < 0.01). Multivariate analysis found an increase in FAZA was correlated with baseline HbA1c level and age (R2 = 0.330), and a decrease in VD-DCP was correlated with HbA1c decrease and diabetes duration (R2 = 0.286). Conclusions: Rapid glycemic control in patients with uncontrolled diabetes led to possible short-term microvascular damage that correlated to both initial and decreased HbA1c.
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Wong DYL, Lam MC, Ran A, Cheung CY. Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions. Curr Opin Ophthalmol 2022; 33:440-446. [PMID: 35916571 DOI: 10.1097/icu.0000000000000886] [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: 11/26/2022]
Abstract
PURPOSE OF REVIEW Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.
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Affiliation(s)
- Dragon Y L Wong
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
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39
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KI-gestützte Analyse retinaler Mikrovaskulatur erlaubt Vorhersage von Krankheitsrisiken. AKTUELLE KARDIOLOGIE 2022. [DOI: 10.1055/a-1765-6286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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40
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Trovato GM. Eyeing the retinal vessels: A window on the heart and beyond. Atherosclerosis 2022; 348:51-52. [DOI: 10.1016/j.atherosclerosis.2022.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 11/02/2022]
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41
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Affiliation(s)
- Michael C. Honigberg
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Seyedeh M. Zekavat
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT
| | - Vineet K. Raghu
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Pradeep Natarajan
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
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de Lemos JA, McGuire DK, Hill JA. Celebrating The Next Generation of Cardiovascular Investigators. Circulation 2022; 145:91-93. [PMID: 35007160 DOI: 10.1161/circulationaha.121.058678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- James A de Lemos
- University of Texas Southwestern Medical Center, Dallas (J.A.d.L., D.K.M., J.A.H.)
| | - Darren K McGuire
- University of Texas Southwestern Medical Center, Dallas (J.A.d.L., D.K.M., J.A.H.).,Parkland Health and Hospital System, Dallas, TX (D.K.M.)
| | - Joseph A Hill
- University of Texas Southwestern Medical Center, Dallas (J.A.d.L., D.K.M., J.A.H.)
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