ROLE OF ARTIFICIAL INTELLIGENCE IN DIABETES MANAGEMENT
DOI:
https://doi.org/10.29121/ijetmr.v7.i7.2020.728Keywords:
Diabetes Mellitus, Hyperglycemia, Machine Learning, Artificial Intelligence, Hypoglycemia.Abstract
According to some of the surveys researchers also claimed that at the end of 2040 there will be marked high in the number of patients worldwide at around 600 million. The vast majority of our day by day exercises have gotten computerized. Computerized wellbeing considers the ever-expanding cooperative energy between cutting edge clinical advancements, development, and computerized correspondence. DM is a condition instigated by unregulated diabetes that may prompt multi-organ disappointment in patients. Because of advances in AI and man-made brainpower which empowers the early discovery and analysis of DM through a computerized procedure which is more favorable than a manual finding. Standards of AI have been utilized to assemble calculations to help prescient models for the danger of creating diabetes or its resulting difficulties. Computer-based intelligence will present a change in perspective in diabetes care from ordinary administration systems to building focused on information-driven exactness care. As per the patient's very own need, an appropriate diabetes care plan requires various fields of experts together to make up the arrangement. So, on the off chance that it is done physically, it would limit the experience and information on these experts and devour bunches of costly clinical assets also.
Downloads
References
Yu KH, Beam AL, Kohane IS, Artificial intelligence in healthcare, Nat Biomed Eng, 2018;2(10):719-31. DOI: https://doi.org/10.1038/s41551-018-0305-z
Rigla M, Garca-Saez G, Pons B, Artificial intelligence methodologies and their application to diabetes, J Diabetes Sci Technol, 2018;12(2):303-10. DOI: https://doi.org/10.1177/1932296817710475
Yau JW, Rogers SL, Kawasaki R, Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care, 2012;35:556–64.
Bergenstal RM, Garg S, Weinzimer SA, Buckingham BA, Bode BW, Tamborlane WV, Kaufman FR, Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients with Type 1 Diabetes, JAMA, 2016;316:1407–1408. DOI: https://doi.org/10.1001/jama.2016.11708
Brown SA, Kovatchev BP, Raghinaru D, Lum JW, Buckingham BA, Kudva YC, Laffel LM, Levy CJ, Pinsker JE, Wadwa RP, Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes, New Engl J Med, 2019;381:1707-1717. DOI: https://doi.org/10.1056/NEJMoa1907863
Papatheodorou K, Papanas N, Banach M, Papazoglou D, Edmonds M, Complications of Diabetes 2016, J Diabetes Res, 2016;2016:6989453. DOI: https://doi.org/10.1155/2016/6989453
International Diabetes Federation (IDF). IDF diabetes atlas, 9th edition. Brussels, Belgium: International Diabetes Federation. Available at: http://www.diabetesatlas.org, Accessed on 27th June 2020.
van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, Seydel ER, A holistic framework to improve the uptake and impact of eHealth technologies, J Med Internet Res, 2011;13(4):e111. DOI: https://doi.org/10.2196/jmir.1672
Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K, Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here, Popul Health Manag, 2019;22(3):229-242. DOI: https://doi.org/10.1089/pop.2018.0129
Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes, JAMA, 2017;318(22):2211-23. DOI: https://doi.org/10.1001/jama.2017.18152
Afzali S, Yildiz O, An effective sample preparation method for diabetes prediction, Int Arab J Inf Technol, 2018;15(6):968-973.
Theera-Umpon N, Poonkasem I, Auephanwiriyakul S, Patikulsila D, Hard exudate detection in retinal fundus images using supervised learning, Neural Computing and Applications, 2019:1-18. DOI: https://doi.org/10.1007/s00521-019-04402-7
Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H, Predicting diabetes mellitus with machine learning techniques, Frontiers in genetics, 2018;9:515. DOI: https://doi.org/10.3389/fgene.2018.00515
Alghamdi M, Al-Mallah M, Keteyian S, Brawner C, Ehrman J, Sakr S, Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project, PloS one, 2017;12(7):1-15. DOI: https://doi.org/10.1371/journal.pone.0179805
Alberti KGMM, Zimmet PZ, Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications Part 1: Diagnosis and Classification of Diabetes Mellitus Provisional Report of a WHO Consultation, Diabetic Medicine, 1998;15:539-53. DOI: https://doi.org/10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
Qureshi I, Ma J, Abbas Q, Recent development on detection methods for the diagnosis of diabetic retinopathy, Symmetry, 2019;11(6):749. DOI: https://doi.org/10.3390/sym11060749
Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S, Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone, JAMA ophthalmology, 2019;137(10):1182-1188. DOI: https://doi.org/10.1001/jamaophthalmol.2019.2923
Carracedo J, Alique M, Ramirez-Carracedo R, Bodega G, Ramirez R, Endothelial extracellular vesicles produced by senescent cells: pathophysiological role in the cardiovascular disease associated with all types of diabetes mellitus, C Vascular Pharma, 2019;17(5):447-454. DOI: https://doi.org/10.2174/1570161116666180820115726
Tortora GJ, Derrickson B, Principles of Anatomy & Physiology, 13th ed, USA: John Wiley & Sons, Inc; 2012:721.
Contreras I, Vehi J, Artificial Intelligence for Diabetes Management and Decision Support: Literature Review, J Med Internet Res, 2018;20(5):e10775. DOI: https://doi.org/10.2196/10775
Davenport T, Kalakota R, The potential for artificial intelligence in healthcare, Futur Healthc J, 2019; 6(2):94. DOI: https://doi.org/10.7861/futurehosp.6-2-94
Kulkarni S, Seneviratne N, Baig MS, Khan AHA, Artificial Intelligence in Medicine: Where Are We Now?, Acad Radiol, 2020;27(1):62-70. DOI: https://doi.org/10.1016/j.acra.2019.10.001
Mishra DK, Shukla S, A New Era of Medical by Artificial Intelligence, International Journal of Engineering Technologies and Management Research, 2020;7(6):125-30. DOI: https://doi.org/10.29121/ijetmr.v7.i6.2020.700
Ellahham S, Ellahham N, Simsekler MCE, Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges, Am J Med Qual, 2019:1-8. DOI: https://doi.org/10.1177/1062860619878515
Buch V, Varughese G, Maruthappu M, Artificial intelligence in diabetes care, Diabet. Med, 2018;35:495-497. DOI: https://doi.org/10.1111/dme.13587
Sriram RD, Reddy SSK, Artificial Intelligence and Digital Tools: Future of Diabetes Care, Clin Geriatr Med, 2020;36(3):513-25. DOI: https://doi.org/10.1016/j.cger.2020.04.009
Pesl P, Herrero P, Reddy M, Oliver N, Johnston DG, Toumazou C, Case-based reasoning for insulin Bolus Advice: evaluation of case parameters in a six-week pilot study, J Diabetes Sci Technol, 2017;11:37-42.
Dassau E, Pinsker JE, Kudva YC, Brown SA, Gondhalekar R, Dalla Man C, Twelve-week 24/7 ambulatory artificial pancreas with weekly adaptation of insulin delivery settings: effect on hemoglobin a1c and hypoglycemia, Diabetes Care, 2017;40:1719-1726. DOI: https://doi.org/10.2337/dc17-1188
Medtronic. ‘Medtronic and IBM Watson Health Partner to Develop New Ways to Tackle Diabetes’. Medtronic.com. Available at http://www.medtronic.com/us-en/about/news/ibm-diabetes.html. Accessed on 27 June 2020.
Baum A, Scarpa J, Bruzelius E, Tamler R, Basu S, Faghmous J, Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial, Lancet Diabetes Endocrinol, 2017;5:808-815. DOI: https://doi.org/10.1016/S2213-8587(17)30176-6
Gulshan V, Peng L, Coram M, Stumpe M, Wu D, Narayanaswamy A, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs, JAMA, 2016;316:2402. DOI: https://doi.org/10.1001/jama.2016.17216
Yap MH, Chatwin KE, Ng CC, Abbott CA, Bowling FL, Rajbhandari S, A New Mobile Application for Standardizing Diabetic Foot Images, J Diabetes Sci Technol, 2018;12:169-173. DOI: https://doi.org/10.1177/1932296817713761
Peddinti G, Cobb J, Yengo L, Froguel P, Kravic J, Balkau B, Early metabolic markers identify potential targets for the prevention of type 2 diabetes, Diabetologia, 2017;60:1740-1750. DOI: https://doi.org/10.1007/s00125-017-4325-0
Marling C, Wiley M, Bunescu R, Shubrook J, Schwartz F, Emerging applications for intelligent diabetes management, AI Mag, 2012;33(2):67. DOI: https://doi.org/10.1609/aimag.v33i2.2410
Schmidt R, Montani S, Bellazzi R, Portinale L, Gierl L, Cased-based reasoning for medical knowledge-based systems, Int J Med Inform, 2001;64(2):355-67. DOI: https://doi.org/10.1016/S1386-5056(01)00221-0
Pesl P, Herrero P, Reddy M, Oliver N, Johnston DG, Toumazou C, Georgiou P, Case-based reasoning for insulin bolus advice: evaluation of case parameters in a six-week pilot study, J Diabetes Sci Technol, 2017;11:37-42. DOI: https://doi.org/10.1177/1932296816629986
Shankaracharya, Odedra D, Samanta S, Vidyarthi AS, Computational intelligence in early diabetes diagnosis: a review, Rev Diabet Stud, 2010;7(4):252-62. DOI: https://doi.org/10.1900/RDS.2010.7.252
He J, Baxter SL, Xu J, Zhou X, Zhang K, The practical implementation of artificial intelligence technologies in medicine, Nat Med, 2019;25:30-36. DOI: https://doi.org/10.1038/s41591-018-0307-0
Vapnik VN, The Nature of Statistical Learning Theory, 1ed, Berlin: Springer-Verlag; 1995 . DOI: https://doi.org/10.1007/978-1-4757-2440-0_1
Statista (2020). Wearables in the India: statistics. Available: https://www.statista.com/outlook/319/119/wearables/india. Accessed on 29 June 2020.
Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B, Artificial Intelligence and Machine Learning Applied at the Point of Care, Front Pharmacol, 2020;11:759. DOI: https://doi.org/10.3389/fphar.2020.00759
Grzybowski A, Brona P, Lim G, Ruamviboonsuk P, Abramoff M, Ting DSW, Artificial intelligence for diabetic retinopathy screening: a review, Eye (Lond), 2020;34(3):451–60. DOI: https://doi.org/10.1038/s41433-019-0566-0
Keel S, Lee PY, Scheetz J, Zhixi Li Z, Kotowicz MA, MacIsaac RJ, He M, Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study, Sci Rep, 2018;8:4330. DOI: https://doi.org/10.1038/s41598-018-22612-2
Lam C, Yu C, Huang L, Rubin D, Retinal lesion detection with deep learning using image patches, Invest Ophthalmol Vis Sci, 2018;59:590-6. DOI: https://doi.org/10.1167/iovs.17-22721
IDx-DR (2020). About IDx-DR, Available: https://www.eyediagnosis.co/idx-dr-eu-1 Accessed on 01 July 2020.
FDA (2020). De Novo classification request for IDx-DR, Available: https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN180001.pdf. Accessed on 01 July 2020.
Nagaraj SB, Sidorenkov G, van Boven JFM, Denig P, Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms, Diabetes Obes Metab, 2019;21(12):2704-11. DOI: https://doi.org/10.1111/dom.13860
Lo-Ciganic WH, Donohue JM, Thorpe JM, Using machine learning to examine medication adherence thresholds and risk of hospitalization, Med Care, 2015;53:720-8. DOI: https://doi.org/10.1097/MLR.0000000000000394
Frøisland DH, Arsand E, Integrating visual dietary documentation in mobile-phone-based self-management application for adolescents with type 1 diabetes, J Diabetes Sci Technol, 2015;9(3):541–8. DOI: https://doi.org/10.1177/1932296815576956
Shah VN, Garg SK, Managing diabetes in the digital age, Clin Diabetes Endocrinol, 2015;1:16. DOI: https://doi.org/10.1186/s40842-015-0016-2
Published
How to Cite
Issue
Section
License
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere.
- That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
For More info, please visit CopyRight Section
