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Brodzka S, Baszyński J, Rektor K, Hołderna-Bona K, Stanek E, Kurhaluk N, Tkaczenko H, Malukiewicz G, Woźniak A, Kamiński P. Immunogenetic and Environmental Factors in Age-Related Macular Disease. Int J Mol Sci 2024; 25:6567. [PMID: 38928273 PMCID: PMC11203563 DOI: 10.3390/ijms25126567] [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: 03/24/2024] [Revised: 06/08/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Age-related macular degeneration (AMD) is a chronic disease, which often develops in older people, but this is not the rule. AMD pathogenesis changes include the anatomical and functional complex. As a result of damage, it occurs, in the retina and macula, among other areas. These changes may lead to partial or total loss of vision. This disease can occur in two clinical forms, i.e., dry (progression is slowly and gradually) and exudative (wet, progression is acute and severe), which usually started as dry form. A coexistence of both forms is possible. AMD etiology is not fully understood. Extensive genetic studies have shown that this disease is multifactorial and that genetic determinants, along with environmental and metabolic-functional factors, are important risk factors. This article reviews the impact of heavy metals, macro- and microelements, and genetic factors on the development of AMD. We present the current state of knowledge about the influence of environmental factors and genetic determinants on the progression of AMD in the confrontation with our own research conducted on the Polish population from Kuyavian-Pomeranian and Lubusz Regions. Our research is concentrated on showing how polluted environments of large agglomerations affects the development of AMD. In addition to confirming heavy metal accumulation, the growth of risk of acute phase factors and polymorphism in the genetic material in AMD development, it will also help in the detection of new markers of this disease. This will lead to a better understanding of the etiology of AMD and will help to establish prevention and early treatment.
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Affiliation(s)
- Sylwia Brodzka
- Division of Ecology and Environmental Protection, Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Skłodowska-Curie St. 9, PL 85-094 Bydgoszcz, Poland; (S.B.); (J.B.); (K.H.-B.); (E.S.)
- Department of Biotechnology, Institute of Biological Sciences, Faculty of Biological Sciences, University of Zielona Góra, Prof. Z. Szafran St. 1, PL 65-516 Zielona Góra, Poland;
| | - Jędrzej Baszyński
- Division of Ecology and Environmental Protection, Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Skłodowska-Curie St. 9, PL 85-094 Bydgoszcz, Poland; (S.B.); (J.B.); (K.H.-B.); (E.S.)
| | - Katarzyna Rektor
- Department of Biotechnology, Institute of Biological Sciences, Faculty of Biological Sciences, University of Zielona Góra, Prof. Z. Szafran St. 1, PL 65-516 Zielona Góra, Poland;
| | - Karolina Hołderna-Bona
- Division of Ecology and Environmental Protection, Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Skłodowska-Curie St. 9, PL 85-094 Bydgoszcz, Poland; (S.B.); (J.B.); (K.H.-B.); (E.S.)
| | - Emilia Stanek
- Division of Ecology and Environmental Protection, Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Skłodowska-Curie St. 9, PL 85-094 Bydgoszcz, Poland; (S.B.); (J.B.); (K.H.-B.); (E.S.)
| | - Natalia Kurhaluk
- Institute of Biology, Pomeranian University in Słupsk, Arciszewski St. 22 B, PL 76-200 Słupsk, Poland; (N.K.); (H.T.)
| | - Halina Tkaczenko
- Institute of Biology, Pomeranian University in Słupsk, Arciszewski St. 22 B, PL 76-200 Słupsk, Poland; (N.K.); (H.T.)
| | - Grażyna Malukiewicz
- Department of Eye Diseases, University Hospital No. 1, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Skłodowska-Curie St. 9, PL 85-092 Bydgoszcz, Poland;
| | - Alina Woźniak
- Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Karłowicz St. 24, PL 85-092 Bydgoszcz, Poland;
| | - Piotr Kamiński
- Division of Ecology and Environmental Protection, Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Skłodowska-Curie St. 9, PL 85-094 Bydgoszcz, Poland; (S.B.); (J.B.); (K.H.-B.); (E.S.)
- Department of Biotechnology, Institute of Biological Sciences, Faculty of Biological Sciences, University of Zielona Góra, Prof. Z. Szafran St. 1, PL 65-516 Zielona Góra, Poland;
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Liu Y, Xie H, Zhao X, Tang J, Yu Z, Wu Z, Tian R, Chen Y, Chen M, Ntentakis DP, Du Y, Chen T, Hu Y, Zhang S, Lei B, Zhang G. Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system. EPMA J 2024; 15:39-51. [PMID: 38463622 PMCID: PMC10923762 DOI: 10.1007/s13167-024-00350-y] [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: 09/01/2023] [Accepted: 01/21/2024] [Indexed: 03/12/2024]
Abstract
Purpose We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists. Methods We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results. Results Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities. Conclusions IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00350-y.
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Affiliation(s)
- Yaling Liu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Hai Xie
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xinyu Zhao
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Jiannan Tang
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Zhen Yu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Zhenquan Wu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Ruyin Tian
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Yi Chen
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
| | - Miaohong Chen
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
| | - Dimitrios P. Ntentakis
- Retina Service, Ines and Fred Yeatts Retina Research Laboratory, Angiogenesis Laboratory, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA USA
| | - Yueshanyi Du
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Tingyi Chen
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
| | - Yarou Hu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Sifan Zhang
- Guizhou Medical University, Guiyang, Guizhou China
- Southern University of Science and Technology School of Medicine, Shenzhen, China
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Guoming Zhang
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
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Xie L, Vaghefi E, Yang S, Han D, Marshall J, Squirrell D. Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design. Clin Ophthalmol 2023; 17:455-469. [PMID: 36755888 PMCID: PMC9901462 DOI: 10.2147/opth.s396537] [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: 11/16/2022] [Accepted: 01/12/2023] [Indexed: 02/04/2023] Open
Abstract
Purpose To create an ensemble of Convolutional Neural Networks (CNNs), capable of detecting and stratifying the risk of progressive age-related macular degeneration (AMD) from retinal photographs. Design Retrospective cohort study. Methods Three individual CNNs are trained to accurately detect 1) advanced AMD, 2) drusen size and 3) the presence or otherwise of pigmentary abnormalities, from macular centered retinal images were developed. The CNNs were then arranged in a "cascading" architecture to calculate the Age-related Eye Disease Study (AREDS) Simplified 5-level risk Severity score (Risk Score 0 - Risk Score 4), for test images. The process was repeated creating a simplified binary "low risk" (Scores 0-2) and "high risk" (Risk Score 3-4) classification. Participants There were a total of 188,006 images, of which 118,254 images were deemed gradable, representing 4591 patients, from the AREDS1 dataset. The gradable images were split into 50%/25%/25% ratios for training, validation and test purposes. Main Outcome Measures The ability of the ensemble of CNNs using retinal images to predict an individual's risk of experiencing progression of their AMD based on the AREDS 5-step Simplified Severity Scale. Results When assessed against the 5-step Simplified Severity Scale, the results generated by the ensemble of CNN's achieved an accuracy of 80.43% (quadratic kappa 0.870). When assessed against a simplified binary (Low Risk/High Risk) classification, an accuracy of 98.08%, sensitivity of ≥85% and specificity of ≥99% was achieved. Conclusion We have created an ensemble of neural networks, trained on the AREDS 1 dataset, that is able to accurately calculate an individual's score on the AREDS 5-step Simplified Severity Scale for AMD. If the results presented were replicated, then this ensemble of CNNs could be used as a screening tool that has the potential to significantly improve health outcomes by identifying asymptomatic individuals who would benefit from AREDS2 macular supplements.
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Affiliation(s)
- Li Xie
- Toku Eyes Limited, Auckland, New Zealand
| | - Ehsan Vaghefi
- Toku Eyes Limited, Auckland, New Zealand,School of Optometry and Vision Science, The University of Auckland, Auckland, New Zealand,Correspondence: Ehsan Vaghefi, Tel +6493737599, Email
| | - Song Yang
- Toku Eyes Limited, Auckland, New Zealand
| | - David Han
- Toku Eyes Limited, Auckland, New Zealand,School of Optometry and Vision Science, The University of Auckland, Auckland, New Zealand
| | | | - David Squirrell
- Toku Eyes Limited, Auckland, New Zealand,Department of Ophthalmology, Auckland District Health Board, Auckland, New Zealand
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Sivaprasad S, Chandra S, Kwon J, Khalid N, Chong V. Perspectives from clinical trials: is geographic atrophy one disease? Eye (Lond) 2023; 37:402-407. [PMID: 35641821 PMCID: PMC9905504 DOI: 10.1038/s41433-022-02115-1] [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: 02/11/2022] [Revised: 04/27/2022] [Accepted: 05/13/2022] [Indexed: 11/09/2022] Open
Abstract
Geographic atrophy (GA) is currently an untreatable condition. Emerging evidence from recent clinical trials show that anti-complement therapy may be a successful treatment option. However, several trials in this therapy area have failed as well. This raises several questions. Firstly, does complement therapy work for all patients with GA? Secondly, is GA one disease? Can we assume that these failed clinical trials are due to ineffective interventions or are they due to flawed clinical trial designs, heterogeneity in GA progression rates or differences in study cohorts? In this article we try to answer these questions by providing an overview of the challenges of designing and interpreting outcomes of randomised controlled trials (RCTs) in GA. These include differing inclusion-exclusion criteria, heterogeneous progression rates of the disease, outcome choices and confounders.
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Affiliation(s)
- Sobha Sivaprasad
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
- University College London, Institute of Ophthalmology, London, UK.
| | - Shruti Chandra
- National Institute of Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- University College London, Institute of Ophthalmology, London, UK
| | - Jeha Kwon
- Oxford University Hospitals NHS Trust, Oxford, UK
| | | | - Victor Chong
- University College London, Institute of Ophthalmology, London, UK
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Russo S, Bonassi S. Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients 2022; 14:nu14091705. [PMID: 35565673 PMCID: PMC9105182 DOI: 10.3390/nu14091705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 02/06/2023] Open
Abstract
Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.
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Affiliation(s)
- Stefania Russo
- EcoVision Lab, Photogrammetry and Remote Sensing Group, ETH Zürich, 8092 Zurich, Switzerland
- Correspondence:
| | - Stefano Bonassi
- Department of Human Sciences and Quality of Life Promotion, San Raffaele University, 00166 Rome, Italy;
- Unit of Clinical and Molecular Epidemiology, IRCCS San Raffaele Roma, 00163 Rome, Italy
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Affiliation(s)
- Anna Galanopoulos
- Department of Ophthalmology Royal Adelaide Hospital Adelaide South Australia Australia
| | - Justine R. Smith
- Flinders University College of Medicine and Public Health Adelaide South Australia Australia
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