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Aldaghi T, Muzik J. Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review. JMIR Med Inform 2024; 12:e47701. [PMID: 38300703 PMCID: PMC10870205 DOI: 10.2196/47701] [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: 03/29/2023] [Revised: 10/24/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques. OBJECTIVE The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field. METHODS The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined. RESULTS The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications). CONCLUSIONS Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.
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Affiliation(s)
- Tahmineh Aldaghi
- Spin-off Companies and Research Results Commercialization Center, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jan Muzik
- Department of Information and Communication Technologies in Medicine, Faculty of Biomedical Engineering, Czech Technical University, Prague, Czech Republic
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Zhang Z, Wang Y, Zhang H, Samusak A, Rao H, Xiao C, Abula M, Cao Q, Dai Q. Artificial intelligence-assisted diagnosis of ocular surface diseases. Front Cell Dev Biol 2023; 11:1133680. [PMID: 36875760 PMCID: PMC9981656 DOI: 10.3389/fcell.2023.1133680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.
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Affiliation(s)
- Zuhui Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ying Wang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Hongzhen Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Arzigul Samusak
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Huimin Rao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Chun Xiao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Muhetaer Abula
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Qixin Cao
- Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China
| | - Qi Dai
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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Yang HK, Che SA, Hyon JY, Han SB. Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease. Diagnostics (Basel) 2022; 12:3167. [PMID: 36553174 PMCID: PMC9777416 DOI: 10.3390/diagnostics12123167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.
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Affiliation(s)
- Hee Kyung Yang
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Song A Che
- Department of Ophthalmology, Kangwon National University School of Medicine, Kangwon National University Hospital, Chuncheon 24289, Republic of Korea
| | - Joon Young Hyon
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Sang Beom Han
- Department of Ophthalmology, Kangwon National University School of Medicine, Kangwon National University Hospital, Chuncheon 24289, Republic of Korea
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Brahim I, Lamard M, Benyoussef A, Quellec G. Automation of dry eye disease quantitative assessment: A review. Clin Exp Ophthalmol 2022; 50:653-666. [PMID: 35656580 PMCID: PMC9542292 DOI: 10.1111/ceo.14119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/09/2022] [Accepted: 05/14/2022] [Indexed: 12/11/2022]
Abstract
Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of which are unfortunately invasive, non‐reproducible and lack accuracy. The following review describes methods that diagnose and measure the extent of eye dryness, enabling clinicians to quantify its severity. Our aim with this paper is to review classical methods as well as those that incorporate automation. For only four ways of quantifying DED, we take a deeper look into what main elements can benefit from automation and the different ways studies have incorporated it. Like numerous medical fields, Artificial Intelligence (AI) appears to be the path towards quality DED diagnosis. This review categorises diagnostic methods into the following: classical, semi‐automated and promising AI‐based automated methods.
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Affiliation(s)
- Ikram Brahim
- Inserm, UMR 1101 Brest France
- Inserm, UMR 1227 Brest France
- Université Bretagne Occidentale Brest France
| | - Mathieu Lamard
- Inserm, UMR 1101 Brest France
- Université Bretagne Occidentale Brest France
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Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Masoud S. Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes. Med Biol Eng Comput 2022; 60:2423-2444. [DOI: 10.1007/s11517-022-02602-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 06/07/2022] [Indexed: 11/30/2022]
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6
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García-Marqués JV, Talens-Estarelles C, García-Lázaro S, Cerviño A. Validation of a new objective method to assess lipid layer thickness without the need of an interferometer. Graefes Arch Clin Exp Ophthalmol 2022; 260:655-676. [PMID: 34487223 PMCID: PMC8786769 DOI: 10.1007/s00417-021-05378-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 11/30/2022] Open
Abstract
PURPOSE This study aimed to develop and validate new metrics to objectively assess the lipid layer thickness (LLT) through the analysis of grey intensity values obtained from the Placido disk pattern reflected onto the tear film. METHODS Ocular surface parameters were measured using Oculus Keratograph 5 M in 94 healthy volunteers (43.8 ± 26.8 years). Subjects' LLT was subjectively classified into 4 groups using an interferometry-based grading scale. New metrics based on the intensity of the Placido disk images were calculated and compared between groups. The repeatability of the new metrics and their diagnostic ability was analysed through receiver operating characteristics (ROC) curves. The level of agreement between the new objective tool and the existing subjective classification scale was analysed by means accuracy, weighted Kappa index and F-measure. RESULTS Mean pixel intensity, median pixel intensity and relative energy at 5.33 s after blinking achieved the highest performance, with a correlation with LLT between r = 0.655 and 0.674 (p < 0.001), sensitivity between 0.92 and 0.94, specificity between 0.79 and 0.81, area under the ROC curve between 0.89 and 0.91, accuracy between 0.76 and 0.77, weighted Kappa index of 0.77 and F-measure between 0.86 and 0.87. CONCLUSION The analysis of grey intensity values in videokeratography can be used as an objective tool to assess LLT. These new metrics could be included in a battery of clinical tests as an easy, repeatable, objective and accessible method to improve the detection and monitoring of dry eye disease and meibomian gland dysfunction.
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Affiliation(s)
- José Vicente García-Marqués
- Department of Optics and Optometry and Vision Sciences, University of Valencia, C/Dr Moliner, 50, 46100, Burjassot, Valencia, Spain
| | - Cristian Talens-Estarelles
- Department of Optics and Optometry and Vision Sciences, University of Valencia, C/Dr Moliner, 50, 46100, Burjassot, Valencia, Spain
| | - Santiago García-Lázaro
- Department of Optics and Optometry and Vision Sciences, University of Valencia, C/Dr Moliner, 50, 46100, Burjassot, Valencia, Spain
| | - Alejandro Cerviño
- Department of Optics and Optometry and Vision Sciences, University of Valencia, C/Dr Moliner, 50, 46100, Burjassot, Valencia, Spain.
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Storås AM, Strümke I, Riegler MA, Grauslund J, Hammer HL, Yazidi A, Halvorsen P, Gundersen KG, Utheim TP, Jackson CJ. Artificial intelligence in dry eye disease. Ocul Surf 2021; 23:74-86. [PMID: 34843999 DOI: 10.1016/j.jtos.2021.11.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 12/21/2022]
Abstract
Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term 'AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
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Affiliation(s)
- Andrea M Storås
- SimulaMet, Oslo, Norway; Department of Computer Science, Oslo Metropolitan University, Norway.
| | | | | | - Jakob Grauslund
- Department of Ophthalmology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Ophthalmology, Vestfold University Trust, Tønsberg, Norway
| | - Hugo L Hammer
- SimulaMet, Oslo, Norway; Department of Computer Science, Oslo Metropolitan University, Norway
| | - Anis Yazidi
- Department of Computer Science, Oslo Metropolitan University, Norway
| | - Pål Halvorsen
- SimulaMet, Oslo, Norway; Department of Computer Science, Oslo Metropolitan University, Norway
| | | | - Tor P Utheim
- Department of Computer Science, Oslo Metropolitan University, Norway; Department of Medical Biochemistry, Oslo University Hospital, Norway; Department of Ophthalmology, Oslo University Hospital, Norway
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Wu X, Liu L, Zhao L, Guo C, Li R, Wang T, Yang X, Xie P, Liu Y, Lin H. Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:714. [PMID: 32617334 PMCID: PMC7327317 DOI: 10.21037/atm-20-976] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) techniques has gained tremendous global interest in this era. Recent studies have demonstrated the potential of AI systems to provide improved capability in various tasks, especially in image recognition field. As an image-centric subspecialty, ophthalmology has become one of the frontiers of AI research. Trained on optical coherence tomography, slit-lamp images and even ordinary eye images, AI can achieve robust performance in the detection of glaucoma, corneal arcus and cataracts. Moreover, AI models based on other forms of data also performed satisfactorily. Nevertheless, several challenges with AI application in ophthalmology have also arisen, including standardization of data sets, validation and applicability of AI models, and ethical issues. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and our prospects.
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Affiliation(s)
- Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lixue Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ruiyang Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ting Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaonan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Peichen Xie
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
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