Vol 10, No 2 (2021)
Review article
Published online: 2021-02-12

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Use of artificial intelligence for management and identification of complications in diabetes

Ashish Behera1
Clin Diabetol 2021;10(2):221-225.

Abstract

Artificial intelligence (AI) can play an important role is in early diagnosis of complications, adherence to a healthy lifestyle and medication, real-time monitoring for optimal glycemic status and predictive prognostic model for the diabetic status of a patient. The early recognition and management of the complications (acute as well as chronic) in diabetes predict the quality of life  (QoL) of a patient. The promising results of AI in the early diagnosis of diabetic retinopathy have opened the frontiers for management of other complications as well. Although flash glucose monitoring (FGMs) and continuous glucose monitoring(CGMs) are yet to be used in routine clinical practice but these modalities do hold promise in future for management of diabetes. Automated diagnosis of diabetic retinopathy (DR) and cardiovascular risk factor monitoring are now possible based on the large retinal fundus imag-ing datasets with improved sensitivity and specificity. Smart-phones and smart devices do have the potential to bring the monitoring and early diagnosis of complications of diabetes into the patient’s domain with the use of applications on their smart devices which will make future management of diabetes as an “e-disease management”. AI applications offer greater accuracy, efficiency, ease of use and satisfaction and can complement the management and early identifi-cation of complication of diabetes in long run. 

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References

  1. Merriam-Webster. (n.d.). Intelligence. In Merriam-Webster.com dictionary. Retrieved November 11, 2020, from: www.merriam-webster.com/dictionary/intelligence. [Last accessed at: 11.11.2020].
  2. Boden MA. Artificial intelligence and natural man. Hassocks, Harvester Press 1977.
  3. Ramesh AN, Kambhampati C, Monson JRT, et al. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004; 86(5): 334–338.
  4. Ellahham S. Artificial intelligence: the future for diabetes care. Am J Med. 2020; 133(8): 895–900.
  5. Dankwa-Mullan I, Rivo M, Sepulveda M, et al. Transforming diabetes care through artificial intelligence: the future is here. Popul Health Manag. 2019; 22(3): 229–242.
  6. Thomas RL, Halim S, Gurudas S, et al. IDF diabetes atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019; 157: 107840.
  7. Karagiannis T, Andreadis P, Manolopoulos A, et al. Decision aids for people with Type 2 diabetes mellitus: an effectiveness rapid review and meta-analysis. Diabet Med. 2019; 36(5): 557–568.
  8. Fagherazzi G, Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes Metab. 2019; 45(4): 322–329.
  9. McConnell MV, Shcherbina A, Pavlovic A, et al. Feasibility of obtaining measures of lifestyle from a smartphone app: the myheart counts cardiovascular health study. JAMA Cardiol. 2017; 2(1): 67–76.
  10. Zhang J, Xu J, Hu X, et al. Diagnostic method of diabetes based on support vector machine and tongue images. Biomed Res Int. 2017; 2017: 7961494.
  11. Marling C, Wiley M, Bunescu R, et al. Emerging applications for intelligent diabetes management. AI Magazine. 2012; 33(2): 67.
  12. Schmidt R, Montani S, Bellazzi R, et al. Cased-Based reasoning for medical knowledge-based systems. Int J Med Inform. 2001; 64(2-3): 355–367.
  13. Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabet Med. 2018; 35(4): 495–497.
  14. Odedra D, Samanta S, Vidyarthi AS. Computational intelligence in early diabetes diagnosis: a review. Rev Diabet Stud. 2010; 7(4): 252–262.
  15. Feig DS, Donovan LE, Corcoy R, et al. Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial. Lancet. 2017; 390(10110): 2347–2359.
  16. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016; 316(22): 2402–2410.
  17. Ting DS, Cheung CYL, Lim G, et al. 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): 2211–2223.
  18. Sosale B, Aravind SR, Murthy H, et al. Simple, mobile-based artificial intelligence algoithm in the detection of diabetic retinopathy (SMART) study. BMJ Open Diabetes Res Care. 2020; 8(1).
  19. Nagaraj SB, Sidorenkov G, van Boven J, et al. 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): 2704–2711.
  20. Han W, Ye Y. A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data. . Pac Symp Biocomput. 2019; 24: 236–247.
  21. Rahmani Katigari M, Ayatollahi H, Malek M, et al. Fuzzy expert system for diagnosing diabetic neuropathy. World J Diabetes. 2017; 8(2): 80–88.
  22. Wang L, Pedersen PC, Strong DM, et al. An automatic assessment system of diabetic foot ulcers based on wound area determination, color segmentation, and healing score evaluation. J Diabetes Sci Technol. 2015; 10(2): 421–428.