Diabetes Management in the Era of Artificial Intelligence

Overview

Journal Arch Med Sci Atheroscler Dis

Date 2024 Aug 1

PMID 39086621

Authors

Athanasia K Papazafiropoulou

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence is growing quickly, and its application in the global diabetes pandemic has the potential to completely change the way this chronic illness is identified and treated. Machine learning methods have been used to construct algorithms supporting predictive models for the risk of getting diabetes or its complications. Social media and Internet forums also increase patient participation in diabetes care. Diabetes resource usage optimisation has benefited from technological improvements. As a lifestyle therapy intervention, digital therapies have made a name for themselves in the treatment of diabetes. Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.

References

Patel M, Asch D, Volpp K . Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2015; 313(5):459-60. DOI: 10.1001/jama.2014.14781. View

Zueger T, Schallmoser S, Kraus M, Saar-Tsechansky M, Feuerriegel S, Stettler C . Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes. Diabetes Technol Ther. 2022; 24(11):842-847. DOI: 10.1089/dia.2022.0210. View

Zee B, Lee J, Lai M, Chee P, Rafferty J, Thomas R . Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis. BMJ Open Diabetes Res Care. 2022; 10(6). PMC: 9809219. DOI: 10.1136/bmjdrc-2022-002914. View

Bellemo V, Lim Z, Lim G, Nguyen Q, Xie Y, Yip M . Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digit Health. 2020; 1(1):e35-e44. DOI: 10.1016/S2589-7500(19)30004-4. View

Baig M, GholamHosseini H, Gutierrez J, Ullah E, Linden M . Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications. Appl Clin Inform. 2021; 12(1):1-9. PMC: 7787711. DOI: 10.1055/s-0040-1719043. View

Abramoff M, Folk J, Han D, Walker J, Williams D, Russell S . Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013; 131(3):351-7. DOI: 10.1001/jamaophthalmol.2013.1743. View

Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J . An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 2018; 41(12):2509-2516. DOI: 10.2337/dc18-0147. View

Yang Y, Pan J, Yuan M, Lai K, Xie H, Ma L . Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study. Ann Transl Med. 2022; 10(20):1088. PMC: 9652560. DOI: 10.21037/atm-22-350. View

Ashrafzadeh S, Hamdy O . Patient-Driven Diabetes Care of the Future in the Technology Era. Cell Metab. 2018; 29(3):564-575. DOI: 10.1016/j.cmet.2018.09.005. View

10.

Zhou W, Sailani M, Contrepois K, Zhou Y, Ahadi S, Leopold S . Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019; 569(7758):663-671. PMC: 6666404. DOI: 10.1038/s41586-019-1236-x. View

11.

Heydon P, Egan C, Bolter L, Chambers R, Anderson J, Aldington S . Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. 2020; 105(5):723-728. PMC: 8077216. DOI: 10.1136/bjophthalmol-2020-316594. View

12.

Ting D, Yim-Lui Cheung C, Lim G, Wei Tan G, Quang N, 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):2211-2223. PMC: 5820739. DOI: 10.1001/jama.2017.18152. View

13.

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(22):2402-2410. DOI: 10.1001/jama.2016.17216. View

14.

Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A . Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022; 4(4):e235-e244. DOI: 10.1016/S2589-7500(22)00017-6. View

15.

Berman M, Guthrie N, Edwards K, Appelbaum K, Njike V, Eisenberg D . Change in Glycemic Control With Use of a Digital Therapeutic in Adults With Type 2 Diabetes: Cohort Study. JMIR Diabetes. 2018; 3(1):e4. PMC: 6238888. DOI: 10.2196/diabetes.9591. View

16.

Zequera M, Stephan S, Paul J . Effectiveness of moulded insoles in reducing plantar pressure in diabetic patients. Annu Int Conf IEEE Eng Med Biol Soc. 2007; 2007:4671-4. DOI: 10.1109/IEMBS.2007.4353382. View

17.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

18.

Khandakar A, Chowdhury M, Reaz M, Hamid Md Ali S, Kiranyaz S, Rahman T . A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images. Sensors (Basel). 2022; 22(11). PMC: 9185274. DOI: 10.3390/s22114249. View

19.

Deo R . Machine Learning in Medicine. Circulation. 2015; 132(20):1920-30. PMC: 5831252. DOI: 10.1161/CIRCULATIONAHA.115.001593. View

20.

Singh K, Singh V, Agrawal N, Gupta S, Singh K . Association of Toll-like receptor 4 polymorphisms with diabetic foot ulcers and application of artificial neural network in DFU risk assessment in type 2 diabetes patients. Biomed Res Int. 2013; 2013:318686. PMC: 3725976. DOI: 10.1155/2013/318686. View