journal
Journal For International Medical Graduates
Journal For International Medical Graduates
California Institute of Behavioral Neurosciences and Psychology
US
https://www.cibnp.com
2832-9864
92
Articles
Artificial Intelligence Driving Diabetes Care
Aishwarya Sadagopan
Sadagopan
Aishwarya
aishwarya.sadagopan@jimgs.com
01
06
2023
Received: 162023
Copyright (c) 2023 Journal For International Medical Graduates
2023
Journal For International Medical Graduates
Artificial Intelligence
Diabetes
CGMS
Precision Medicine
Glucose Variability
Machine Learning
Diabetes Management
[
Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med. 2020;133(8):895-900. doi:10.1016/j.amjmed.2020.03.033
]
[
2.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. Published 2021 Dec 13. doi:10.1007/s11892-021-01423-2
]
[
3.Contreras I, Vehi J.Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res 2018;20(5):e10775. DOI: 10.2196/10775
]
[
Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism. 2021;124:154872. doi:10.1016/j.metabol.2021.154872
]
[
Dagliati A, Marini S, Sacchi L, et al. Machine Learning Methods to Predict Diabetes Complications. J Diabetes Sci Technol. 2018;12(2):295-302. doi:10.1177/1932296817706375
]
[
Gunasekeran DV, Ting DSW, Tan GSW, Wong TY. Artificial intelligence for diabetic retinopathy screening, prediction and management. Curr Opin Ophthalmol. 2020;31(5):357-365. doi:10.1097/ICU.0000000000000693
]
[
Lemelman, M. B., Letourneau, L., & Greeley, S. A. W. (2018). Neonatal Diabetes Mellitus: An Update on Diagnosis and Management. Clinics in perinatology, 45(1), 41–59.
]
[
Barsanti C, Lenzarini F, Kusmic C. Diagnostic and prognostic utility of non-invasive imaging in diabetes management. World J Diabetes. 2015;6(6):792-806. doi:10.4239/wjd.v6.i6.792
]
[
Subramanian M, Wojtusciszyn A, Favre L, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med. 2020;18(1):472. Published 2020 Dec 9. doi:10.1186/s12967-020-02658-5
]
[
Colclough K, Ellard S, Hattersley A, Patel K. Syndromic Monogenic Diabetes Genes Should Be Tested in Patients With a Clinical Suspicion of Maturity-Onset Diabetes of the Young. Diabetes. 2022;71(3):530-537. doi:10.2337/db21-0517
]
[
Fagherazzi G, Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes Metab. 2019;45(4):322-329. doi:10.1016/j.diabet.2018.08.012
]
[
Lu CQ, Wang YC, Meng XP, et al. Diabetes risk assessment with imaging: a radiomics study of abdominal CT. Eur Radiol. 2019;29(5):2233-2242. doi:10.1007/s00330-018-5865-5
]
[
Gilbeau, J. P., Poncelet, V., Libon, E., Derue, G., & Heller, F. R. (1992). The density, contour, and thickness of the pancreas in diabetics: CT findings in 57 patients. AJR. American journal of roentgenology, 159(3), 527–531.
]
[
Singla, Rajiv; Singla, Ankush1; Gupta, Yashdeep2; Kalra, Sanjay3. Artificial Intelligence/Machine Learning in Diabetes Care. Indian Journal of Endocrinology and Metabolism 23(4):p 495-497, Jul–Aug 2019. | DOI: 10.4103/ijem.IJEM_228_19
]
[
DeVries JH. Glucose variability: where it is important and how to measure it. Diabetes. 2013;62(5):1405-1408. doi:10.2337/db12-1610
]
[
Suh S, Kim JH. Glycemic Variability: How Do We Measure It and Why Is It Important?. Diabetes Metab J. 2015;39(4):273-282.
]
[
Saba L, Sanagala SS, Gupta SK, et al. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med. 2021;9(14):1206. doi:10.21037/atm-20-7676
]
[
Levitt K, Vivas L, Courtney B, Connelly KA. Vascular imaging in diabetes. Curr Atheroscler Rep. 2014;16(4):399. doi:10.1007/s11883-014-0399-z
]
[
Jiang L, Zhao Y. The value of color Doppler ultrasound in the diagnosis of lower extremity vascular disease in type 2 diabetes and an analysis of related factors. Minerva Endocrinol. 2017;42(3):223-227. doi:10.23736/S0391-1977.16.02352-X
]
[
Schaepelynck-Bélicar P, Vague P, Simonin G, Lassmann-Vague V. Improved metabolic control in diabetic adolescents using the continuous glucose monitoring system (CGMS). Diabetes Metab. 2003;29(6):608-612. doi:10.1016/s1262-3636(07)70076-9
]