Login Register Cart 0
Generic placeholder image

Current Diabetes Reviews

Editor-in-Chief

ISSN (Print): 1573-3998
ISSN (Online): 1875-6417

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Artificial Intelligence in Efficient Diabetes Care

Author(s): Gopal Bhagwan Khodve and Sugato Banerjee*

Volume 19, Issue 9, 2023

Published on: 31 October, 2022

Article ID: e050922208561 Pages: 10

DOI: 10.2174/1573399819666220905163940

Price: $65

Abstract

Diabetes is a chronic disease that is not easily curable but can be managed efficiently. Artificial Intelligence is a powerful tool that may help in diabetes prediction, continuous glucose monitoring, Insulin injection guidance, and other areas of diabetes care. Diabetes, if not appropriately managed, leads to secondary complications like retinopathy, nephropathy, and neuropathy. Artificial intelligence helps minimize the risk of these complications through software and Artificial Intelligence-based devices. Artificial Intelligence can also help physicians in the early diagnosis and management of diabetes while reducing medical errors. Here we review the advancement of Artificial Intelligence in diabetes management.

Keywords: Artificial intelligence, diabetes, diabetes management, healthcare, Machine learning, Continuous Glucose Monitoring.

[1]
Li J, Huang J, Zheng L, Li X. Application of artificial intelligence in diabetes education and management: Present status and promising prospect. Front Public Health 2020; 8(173): 173.
[http://dx.doi.org/10.3389/fpubh.2020.00173] [PMID: 32548087]
[2]
Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 2018; 138: 271-81.
[http://dx.doi.org/10.1016/j.diabres.2018.02.023] [PMID: 29496507]
[3]
Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: A 21st century challenge. Lancet Diabetes Endocrinol 2014; 2(1): 56-64.
[http://dx.doi.org/10.1016/S2213-8587(13)70112-8] [PMID: 24622669]
[4]
Unwin N, Gan D, Whiting D. The IDF diabetes atlas: Providing evidence, raising awareness and promoting action. Diabetes Res Clin Pract 2010; 87(1): 2-3.
[http://dx.doi.org/10.1016/j.diabres.2009.11.006] [PMID: 19962207]
[5]
Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus-present and future perspectives. Nat Rev Endocrinol 2012; 8(4): 228-36.
[http://dx.doi.org/10.1038/nrendo.2011.183] [PMID: 22064493]
[6]
Ellahham S. Artificial intelligence: The future for diabetes care. Am J Med 2020; 133(8): 895-900.
[http://dx.doi.org/10.1016/j.amjmed.2020.03.033] [PMID: 32325045]
[7]
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): 229-42.
[http://dx.doi.org/10.1089/pop.2018.0129] [PMID: 30256722]
[8]
Nelson RJ, Yu Q. Consistency, mechanicalness, and the logic of the mind. Synthese 1980; 43(3): 433-51.
[http://dx.doi.org/10.1007/BF00870296]
[9]
Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: Literature review. J Med Internet Res 2018; 20(5): e10775.
[http://dx.doi.org/10.2196/10775] [PMID: 29848472]
[10]
Nomura A, Noguchi M, Kometani M, Furukawa K, Yoneda T. Artificial intelligence in current diabetes management and prediction. Curr Diab Rep 2021; 21(12): 61.
[http://dx.doi.org/10.1007/s11892-021-01423-2] [PMID: 34902070]
[11]
Gans D, Kralewski J, Hammons T, Dowd B. Medical groups’ adoption of electronic health records and information systems. Health Aff (Millwood) 2005; 24(5): 1323-33.
[http://dx.doi.org/10.1377/hlthaff.24.5.1323] [PMID: 16162580]
[12]
Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak 2019; 19(1): 211.
[http://dx.doi.org/10.1186/s12911-019-0918-5] [PMID: 30616584]
[13]
Magoulas GD, Prentza A. Machine learning in medical applications. In: Paliouras G, Karkaletsis V, Spyropoulos CD, Eds. Machine Learning and Its Applications: Advanced Lectures. Berlin, Heidelberg: Springer Berlin Heidelberg 2001; pp. 300-307.
[http://dx.doi.org/10.1007/3-540-44673-7_19]
[14]
Alexopoulos E, Dounias G, Vemmos K. In diagnosis of stroke using inductive. Mach Learn 1999.
[15]
Semerdjian J, Frank S. An ensemble classifier for predicting the onset of type II diabetes. arxiv 2017; 22, 5247.
[16]
Yu W, Liu T, Valdez R, Gwinn M, Khoury MJ. Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes. BMC Med Inform Decis Mak 2010; 10(1): 16.
[http://dx.doi.org/10.1186/1472-6947-10-16] [PMID: 20307319]
[17]
Kublanov VS, Dolganov AY, Belo D, Gamboa H. Comparison of machine learning methods for the arterial hypertension diagnostics. Appl Bionics Biomech 2017; 2017: 1-13.
[http://dx.doi.org/10.1155/2017/5985479] [PMID: 28831239]
[18]
Parthiban G, Srivatsa SJIJAIS. Applying machine learning methods in diagnosing heart disease for diabetic patients. Int J Appl Inf Syst 2012; 3: 25-30.
[http://dx.doi.org/10.5120/ijais12-450593]
[19]
Anderson M, Anderson SL. Machine ethics: Creating an ethical intelligent agent. AI Mag 2007; 28(4): 15.
[20]
Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif Manage Rev 2019; 61(4): 5-14.
[http://dx.doi.org/10.1177/0008125619864925]
[21]
McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag 2006; 27(4): 12.
[22]
Cioffi R, Travaglioni M, Piscitelli G, Petrillo A, De Felice F. Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability (Basel) 2020; 12(2): 492.
[http://dx.doi.org/10.3390/su12020492]
[23]
Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? Intern J Edu Technol Higher Edu 2019; 16(1): 39.
[http://dx.doi.org/10.1186/s41239-019-0171-0]
[24]
Russell S, Norvig P. Artificial Intelligence: A Modern Approach. Upper Saddle River, New Jersey: Pearson Education, Inc. 2002.
[25]
Kayid A. The role of Artificial Intelligence in future technology. 2020. Available from: https://medium.com/@berksudan/the-role-of-artificial-intelligence-in-future-technology-48d050ee22e4
[26]
Le AHT, Liu B, Huang HK. Integration of computer-aided diagnosis/detection (CAD) results in a PACS environment using CAD–PACS toolkit and DICOM SR. Int J CARS 2009; 4(4): 317-29.
[http://dx.doi.org/10.1007/s11548-009-0297-y] [PMID: 20033579]
[27]
Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One 2019; 14(2): e0212356.
[http://dx.doi.org/10.1371/journal.pone.0212356] [PMID: 30779785]
[28]
Wang Y, Wu X, Mo X. A novel adaptive-weighted-average framework for blood glucose prediction. Diabetes Technol Ther 2013; 15(10): 792-801.
[http://dx.doi.org/10.1089/dia.2013.0104] [PMID: 23883406]
[29]
Wogu IA, Katende JO, Elegbeleye A, et al. Artificial intelligence, other minds, and human factor development: The fate of man in the world of machines. In: Misra S, Adewumi A, Eds. Handbook of research on the role of human factors in IT project management. IGI Global 2020; pp. 205-220.
[http://dx.doi.org/10.4018/978-1-7998-1279-1.ch014]
[30]
Tushir MJETBd. Artificial intelligence in education: The journey so far and the future prospects. Emerging Trends in Big Data, IoT and Cyber Security. 112.
[31]
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2017; 2(4)
[32]
Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013; 309(13): 1351-2.
[http://dx.doi.org/10.1001/jama.2013.393] [PMID: 23549579]
[33]
Kolker E, Özdemir V, Kolker E. How healthcare can refocus on its super-customers (Patients, n =1) and customers (doctors and nurses) by leveraging lessons from Amazon, Uber, and Watson. OMICS 2016; 20(6): 329-33.
[http://dx.doi.org/10.1089/omi.2016.0077] [PMID: 27310474]
[34]
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019; 28(2): 73-81.
[35]
Turing AM. I.-Computing machinery and intelligence. Mind 1950; LIX(236): 433-60.
[http://dx.doi.org/10.1093/mind/LIX.236.433]
[36]
Kononenko I. Machine learning for medical diagnosis: History, state of the art and perspective. Artif Intell Med 2001; 23(1): 89-109.
[http://dx.doi.org/10.1016/S0933-3657(01)00077-X] [PMID: 11470218]
[37]
Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl 2004; 86(5): 334-8.
[http://dx.doi.org/10.1308/147870804290] [PMID: 15333167]
[38]
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69: S36-40.
[http://dx.doi.org/10.1016/j.metabol.2017.01.011] [PMID: 28126242]
[39]
Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des 2007; 13(14): 1497-508.
[http://dx.doi.org/10.2174/138161207780765954] [PMID: 17504169]
[40]
Singla R, Singla A, Gupta Y, Kalra S. Artificial intelligence/machine learning in diabetes care. Indian J Endocrinol Metab 2019; 23(4): 495-7.
[http://dx.doi.org/10.4103/ijem.IJEM_228_19] [PMID: 31741913]
[41]
Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 2017; 15: 104-16.
[http://dx.doi.org/10.1016/j.csbj.2016.12.005] [PMID: 28138367]
[42]
Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes risk calculator: A simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care 2008; 31(5): 1040-5.
[http://dx.doi.org/10.2337/dc07-1150] [PMID: 18070993]
[43]
Sisodia D, Sisodia DS. Prediction of diabetes using classification algorithms. Procedia Comput Sci 2018; 132: 1578-85.
[http://dx.doi.org/10.1016/j.procs.2018.05.122]
[44]
Hidalgo JI, Colmenar JM, Kronberger G, Winkler SM, Garnica O, Lanchares J. Data based prediction of blood glucose concentrations using evolutionary methods. J Med Syst 2017; 41(9): 142.
[http://dx.doi.org/10.1007/s10916-017-0788-2] [PMID: 28791547]
[45]
Ajjan RA. Therapeutics, How can we realize the clinical benefits of continuous glucose monitoring? Diabetes Technol Ther 2017; 19(S2): S-27-36.
[http://dx.doi.org/10.1089/dia.2017.0021] [PMID: 28541132]
[46]
Tamborlane WV, Beck RW, Bode BW, et al. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008; 359(14): 1464-76.
[http://dx.doi.org/10.1056/NEJMoa0805017] [PMID: 18779236]
[47]
Rodbard D. Continuous glucose monitoring: A review of successes, challenges, and opportunities. Diabetes Technol Ther 2016; 18 (Suppl. 2): S3-S13.
[48]
Steven J. Continuous glucose monitoring. Available from: https://www.niddk.nih.gov/health-information/diabetes/overview/managing-diabetes/continuous-glucose-monitoring
[49]
Klonoff DC, Ahn D, Drincic A, Practice C. Continuous glucose monitoring: A review of the technology and clinical use. Diabetes Res Clin Pract 2017; 133: 178-92.
[http://dx.doi.org/10.1016/j.diabres.2017.08.005] [PMID: 28965029]
[50]
Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors. Sensors (Basel) 2020; 20(14): 3870.
[http://dx.doi.org/10.3390/s20143870] [PMID: 32664432]
[51]
Makroum MA, Adda M, Bouzouane A, Ibrahim H. Machine learning and smart devices for diabetes management: Systematic review. Sensors (Basel) 2022; 22(5): 1843.
[http://dx.doi.org/10.3390/s22051843] [PMID: 35270989]
[52]
Chaki J, Thillai Ganesh S, Cidham SK, Ananda Theertan S. Machine learning and artificial intelligence-based Diabetes Mellitus detection and self-management: A systematic review. J King Saud Univ - Comput. Inf Sci 2022; 34 (6, Part B): 3204-25.
[53]
Cobelli C, Renard E, Kovatchev B. Artificial pancreas: Past, present, future. Diabetes 2011; 60(11): 2672-82.
[http://dx.doi.org/10.2337/db11-0654] [PMID: 22025773]
[54]
Kamusheva M, Tachkov K, Dimitrova M, et al. a systematic review of collective evidences investigating the effect of diabetes monitoring systems and their application in health care. Front Endocrinol (Lausanne) 2021; 12: 636959.
[http://dx.doi.org/10.3389/fendo.2021.636959] [PMID: 33796074]
[55]
Sriram RD, Reddy SSK. Artificial intelligence and digital tools. Clin Geriatr Med 2020; 36(3): 513-25.
[http://dx.doi.org/10.1016/j.cger.2020.04.009] [PMID: 32586478]
[56]
Holzer R, Bloch W, Brinkmann C. Continuous glucose monitoring in healthy adults-possible applications in health care, wellness, and sports. Sensors (Basel) 2022; 22(5): 2030.
[http://dx.doi.org/10.3390/s22052030] [PMID: 35271177]
[57]
Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care 2019; 42(8): 1593-603.
[http://dx.doi.org/10.2337/dci19-0028] [PMID: 31177185]
[58]
Rghioui A, Lloret J, Harane M, Oumnad A. A smart glucose monitoring system for diabetic patient. Electronics (Basel) 2020; 9(4): 678.
[http://dx.doi.org/10.3390/electronics9040678]
[59]
Deiss D, Szadkowska A, Gordon D, et al. Clinical practice recommendations on the routine use of eversense, the first long-term implantable continuous glucose monitoring system. Diabetes Technol Ther 2019; 21(5): 254-64.
[http://dx.doi.org/10.1089/dia.2018.0397] [PMID: 31021180]
[60]
Reddy N, Verma N, Dungan K. Monitoring technologies- continuous glucose monitoring, mobile technology, biomarkers of glycemic control. In: Feingold KR, Anawalt B, Boyce A, et al., Eds. Endotext. South Dartmouth (MA): MDText.com, Inc. 2000.
[61]
Campbell FM, Murphy NP, Stewart C, Biester T, Kordonouri O. Outcomes of using flash glucose monitoring technology by children and young people with type 1 diabetes in a single arm study. Pediatr Diabetes 2018; 19(7): 1294-301.
[http://dx.doi.org/10.1111/pedi.12735] [PMID: 30054967]
[62]
Mancini G, Berioli M, Santi E, et al. Flash glucose monitoring: A review of the literature with a special focus on type 1 diabetes. Nutrients 2018; 10(8): 992.
[http://dx.doi.org/10.3390/nu10080992] [PMID: 30060632]
[63]
Blum A. Freestyle libre glucose monitoring system. Clin Diabetes 2018; 36(2): 203-4.
[http://dx.doi.org/10.2337/cd17-0130] [PMID: 29686463]
[64]
Doyle-Delgado K, Chamberlain JJ. Use of diabetes-related applications and digital health tools by people with diabetes and their health care providers. Clin Diabetes 2020; 38(5): 449-61.
[http://dx.doi.org/10.2337/cd20-0046] [PMID: 33384470]
[65]
Montero AR, Toro-Tobon D, Gann K, Nassar CM, Youssef GA, Magee MF. Implications of remote monitoring Technology in Optimizing Traditional Self-Monitoring of blood glucose in adults with T2DM in primary care. BMC Endocr Disord 2021; 21(1): 222.
[http://dx.doi.org/10.1186/s12902-021-00884-6] [PMID: 34758807]
[66]
Trief PM, Cibula D, Rodriguez E, Akel B, Weinstock RS. Incorrect insulin administration: A problem that warrants attention. Clin Diabetes 2016; 34(1): 25-33.
[http://dx.doi.org/10.2337/diaclin.34.1.25] [PMID: 26807006]
[67]
Cefalu WT, Kaul S, Gerstein HC, et al. Cardiovascular outcomes trials in type 2 diabetes: Where do we go from here? Reflections from a Diabetes Care Editors’ expert forum. Diabetes Care 2018; 41(1): 14-31.
[http://dx.doi.org/10.2337/dci17-0057] [PMID: 29263194]
[68]
Basal insulin and cardiovascular and other outcomes in dysglycemia. N Engl J Med 2012; 367(4): 319-28.
[http://dx.doi.org/10.1056/NEJMoa1203858] [PMID: 22686416]
[69]
Rao Kondapally Seshasai S, Kaptoge S, Thompson A, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 2011; 364(9): 829-41.
[http://dx.doi.org/10.1056/NEJMoa1008862] [PMID: 21366474]
[70]
Berget C, Lange S, Messer L, Forlenza GP. A clinical review of the t:slim X2 insulin pump. Expert Opin Drug Deliv 2020; 17(12): 1675-87.
[http://dx.doi.org/10.1080/17425247.2020.1814734] [PMID: 32842794]
[71]
Fanzola V, Riboni S, Cannalire G, et al. The impact of new Continuous Glucose Monitoring (CGM) devices versus Self-Management of Blood Glucose (SMBG) on the daily life of parents and children affected by type 1 diabetes mellitus. J Pediatr Neonatal Individ Med 2022; 11(1): e110111.
[72]
Fujihara K, Matsubayashi Y, Harada Yamada M, et al. Machine learning approach to decision making for insulin initiation in Japanese patients with type 2 diabetes (JDDM 58): Model development and validation study. JMIR Med Inform 2021; 9(1): e22148.
[http://dx.doi.org/10.2196/22148] [PMID: 33502325]
[73]
Kerr D. Review: Continuous subcutaneous insulin infusion therapy and children with type 1 diabetes mellitus: The 2008 updated NICE guidelines. Br J Diabetes Vasc Dis 2008; 8 (Suppl. 1): S2-5.
[http://dx.doi.org/10.1177/1474651408097727]
[74]
Kesavadev J, Das AK, Unnikrishnan R, et al. Use of insulin pumps in India: Suggested guidelines based on experience and cultural differences. Diabetes Technol Ther 2010; 12(10): 823-31.
[http://dx.doi.org/10.1089/dia.2010.0027] [PMID: 20807118]
[75]
Cobelli C, Dalla Man C, Sparacino G, Magni L, De Nicolao G, Kovatchev BP. Diabetes: Models, signals, and control. IEEE Rev Biomed Eng 2009; 2: 54-96.
[http://dx.doi.org/10.1109/RBME.2009.2036073] [PMID: 20936056]
[76]
Pinsker JE, Müller L, Constantin A, et al. Real-world patient-reported outcomes and glycemic results with initiation of control-IQ technology. Diabetes Technol Ther 2021; 23(2): 120-7.
[http://dx.doi.org/10.1089/dia.2020.0388] [PMID: 32846114]
[77]
Trevitt S, Simpson S, Wood A. Artificial pancreas device systems for the closed-loop control of type 1 diabetes: What systems are in development? J Diabetes Sci Technol 2015; 10.
[PMID: 26589628]
[78]
Jafri RZ, Balliro CA, Sherwood J, et al. 77-OR: First human study testing the iLet, a purpose-built bionic pancreas platform. Diabetes 2019; 68 (Supplement_1): 77. [-OR.
[79]
Lee S, Kim J, Park SW, Jin SM, Park SM. Toward a fully automated artificial pancreas system using a bioinspired reinforcement learning design: In silico validation. IEEE J Biomed Health Inform 2021; 25(2): 536-46.
[http://dx.doi.org/10.1109/JBHI.2020.3002022] [PMID: 32750935]
[80]
Moon SJ, Jung I, Park CY. Current advances of artificial pancreas systems: A comprehensive review of the clinical evidence. Diabetes Metab J 2021; 45(6): 813-39.
[http://dx.doi.org/10.4093/dmj.2021.0177] [PMID: 34847641]
[81]
Gildon BW. In pen smart insulin pen system: Product review and user experience. Diabetes Spectr 2018; 31(4): 354-8.
[http://dx.doi.org/10.2337/ds18-0011] [PMID: 30510392]
[82]
Ignaut DA, Venekamp WJRR. HumaPen® Memoir™: A novel insulin-injecting pen with a dose-memory feature. Expert Rev Med Devices 2007; 4(6): 793-802.
[http://dx.doi.org/10.1586/17434440.4.6.793] [PMID: 18035945]
[83]
Wolfensberger TJ. The historical discovery of macular edema. Doc Ophthalmol 1999; 97(3/4): 207-16.
[http://dx.doi.org/10.1023/A:1002176428000] [PMID: 10896334]
[84]
Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 2017; 124(7): 962-9.
[http://dx.doi.org/10.1016/j.ophtha.2017.02.008] [PMID: 28359545]
[85]
Oren O, Gersh BJ, Bhatt DL. Artificial intelligence in medical imaging: Switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health 2020; 2(9): e486-8.
[http://dx.doi.org/10.1016/S2589-7500(20)30160-6] [PMID: 33328116]
[86]
Doi K. Diagnostic imaging over the last 50 years: Research and development in medical imaging science and technology. Phys Med Biol 2006; 51(13): R5-R27.
[http://dx.doi.org/10.1088/0031-9155/51/13/R02] [PMID: 16790920]
[87]
Kumar A, Padhy SK, Takkar B, Chawla R. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol 2019; 67(7): 1004-9.
[http://dx.doi.org/10.4103/ijo.IJO_1989_18] [PMID: 31238395]
[88]
Huang GM, Huang KY, Lee TY, Weng J. An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients. BMC Bioinformatics 2015; 16 (Suppl. 1): S5.
[http://dx.doi.org/10.1186/1471-2105-16-S1-S5]
[89]
Al-Rubeaan K, Youssef AM, Subhani SN, et al. Diabetic nephropathy and its risk factors in a society with a type 2 diabetes epidemic: A Saudi National Diabetes Registry-based study. PLoS One 2014; 9(2): e88956.
[http://dx.doi.org/10.1371/journal.pone.0088956] [PMID: 24586457]
[90]
Thomas MC, Brownlee M, Susztak K, et al. Diabetic kidney disease. Nat Rev Dis Primers 2015; 1(1): 15018.
[http://dx.doi.org/10.1038/nrdp.2015.18] [PMID: 27188921]
[91]
Sharifiaghdas F, Kashi AH, Eshratkhah R. Evaluating percutaneous nephrolithotomy-induced kidney damage by measuring urinary concentrations of β2-microglobulin. Urol J 2011; 8(4): 277-82.
[PMID: 22090045]
[92]
Feldman EL, Callaghan BC, Pop-Busui R, et al. Diabetic neuropathy. Nat Rev Dis Primers 2019; 5(1): 41.
[http://dx.doi.org/10.1038/s41572-019-0092-1] [PMID: 31197153]
[93]
Pop-Busui R, Boulton AJM, Feldman EL, et al. Diabetic neuropathy: A position statement by the american diabetes association. Diabetes Care 2017; 40(1): 136-54.
[http://dx.doi.org/10.2337/dc16-2042] [PMID: 27999003]
[94]
Abbott CA, Malik RA, van Ross ERE, Kulkarni J, Boulton AJM. Prevalence and characteristics of painful diabetic neuropathy in a large community-based diabetic population in the U.K. Diabetes Care 2011; 34(10): 2220-4.
[http://dx.doi.org/10.2337/dc11-1108] [PMID: 21852677]
[95]
Bostani A, Homayounfar H. The relationship between NCS findings and Toronto clinical scoring system of neuropathy in diabetic polyneuropathy. Majallah-i Danishgah-i Ulum-i Pizishki-i Kirmanshah 2006; 10(3): e81805.
[96]
Rahman M, Griffin SJ, Rathmann W, Wareham NJ. How should peripheral neuropathy be assessed in people with diabetes in primary care? A population-based comparison of four measures. Diabet Med 2003; 20(5): 368-74.
[http://dx.doi.org/10.1046/j.1464-5491.2003.00931.x] [PMID: 12752485]
[97]
Wei HY, Lu CS, Lin TH. Exploring the P2 and P3 ligand binding features for Hepatitis C virus NS3 protease using some 3D QSAR techniques. J Mol Graph Model 2008; 26(7): 1131-44.
[http://dx.doi.org/10.1016/j.jmgm.2007.10.005] [PMID: 18024210]
[98]
Riazi H, Larijani B, Langarizadeh M, Shahmoradi L. Managing diabetes mellitus using information technology: A systematic review. J Diabetes Metab Disord 2015; 14(1): 49.
[http://dx.doi.org/10.1186/s40200-015-0174-x] [PMID: 26075190]
[99]
Rahmani Katigari M, Ayatollahi H, Malek M, Kamkar Haghighi M. Fuzzy expert system for diagnosing diabetic neuropathy. World J Diabetes 2017; 8(2): 80-8.
[http://dx.doi.org/10.4239/wjd.v8.i2.80] [PMID: 28265346]
[100]
Armstrong DG, Lavery LA. Diabetic foot ulcers: Prevention, diagnosis and classification. Am Fam Physician 1998; 57(6): 1325-1332, 1337-1338.
[PMID: 9531915]
[101]
Lavery LA, Armstrong DG, Wunderlich RP, Mohler MJ, Wendel CS, Lipsky BA. Risk factors for foot infections in individuals with diabetes. Diabetes Care 2006; 29(6): 1288-93.
[http://dx.doi.org/10.2337/dc05-2425] [PMID: 16732010]
[102]
Brem H, Tomic-Canic M. Cellular and molecular basis of wound healing in diabetes. J Clin Invest 2007; 117(5): 1219-22.
[http://dx.doi.org/10.1172/JCI32169] [PMID: 17476353]
[103]
Spampinato SF, Caruso GI, De Pasquale R, Sortino MA, Merlo S. The treatment of impaired wound healing in diabetes: Looking among old drugs. Pharmaceuticals (Basel) 2020; 13(4): 60.
[http://dx.doi.org/10.3390/ph13040060] [PMID: 32244718]
[104]
Kaabouch N, Hu WC, Chen Y, Anderson JW, Ames F, Paulson R. Predicting neuropathic ulceration: Analysis of static temperature distributions in thermal images. J Biomed Opt 2010; 15(6): 061715.
[http://dx.doi.org/10.1117/1.3524233] [PMID: 21198163]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy