Application of artificial intelligence in diabetes research and clinic performance

doi:10.16352/j.issn.1001-6325.2022.11.1650

Abstract

Abstract: Artificial intelligence technology is the outcome of economic globalization. With the advent of the digital age, artificial intelligence plays an increasingly important role in various fields including the constantly updated and iterative healthcare and clinical service. Diabetes is a chronic non-communicable disease characterized by hyperglycemia. Many scholars are committed to researching diabetes prediction, intelligent diagnosis, early screening of complication and health management from an innovative perspective, so as to promote the health state of people. This paper mainly summarizes the application of artificial intelligence in diabetes research and clinical practice in recent years and puts forward future prospects for the intelligence and automation of diabetes diagnosis and treatment.

Key words: artificial intelligence, diabetes, complications, healthcare

CLC Number:

R587.1

WANG Yan-lei, DONG Wen-li, ZHANG Qiu. Application of artificial intelligence in diabetes research and clinic performance[J]. Basic & Clinical Medicine, 2022, 42(11): 1650-1655.

References 35

[1]	Rigla M, Garcia-Saez G, Pons B, et al. Artificial intelligence methodologies and their application to diabetes[J]. J Diabetes Sci Technol, 2010, 12: 303-310. doi:10.1177/1932296817710475
[2]	Li Y, Teng D, Shi X, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study[J]. BMJ, 2020, 369, m997. doi:10.1136/bmj.m997
[3]	Choi SB, Kim WJ, Yoo TK, et al. Screening for prediabetes using machine learning models[J]. Comput Math Methods Med, 2014, 618976. doi:10.1155/2014/618976
[4]	肖薇. 机器学习算法在糖尿病预测中的应用[J]. 数字技术与应用, 2021, 39: 104-106. doi:10.19695/j.cnki.cn12-1369.2021.04.35.
[5]	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[J]. JAMA, 2016, 316: 2402-2410. doi:10.1001/jama.2016.17216
[6]	Sosale B, Sosale AR, Murthy H, et al. Medios-an offline smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy[J]. Indian J Ophthal-mol, 2020, 68: 391-395. doi:10.4103/ijo.IJO_1203_19
[7]	Van Der Heijden Aa, Abramoff MD, Verbraak F, et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System[J]. Acta Ophthalmol, 2018, 96: 63-68. doi:10.1111/aos.13613
[8]	李萌, 王耿媛, 夏鸿慧, 等. 眼底阅片人工智能系统在糖尿病视网膜病变筛查中的临床价值评价[J].中华实验眼科杂志, 2019, 37: 663-668. doi:10.3760/cma.j.issn.2095-0160.2019.08.015.
[9]	李治玺, 张健, Fong Nellie, 等. 人工智能初筛分流在大规模糖尿病视网膜病变筛查中的应用[J].中华医学杂志, 2020, 100: 3835-3840. doi:10.3760/cma.j.cn112137-20200901-02526.
[10]	郑武, 阮坤炜, 吴天添, 等. 人工智能糖尿病视网膜病变筛查系统与眼科医师诊断结果的一致性分析[J]. 眼科新进展, 2020, 40: 1170-1173.
[11]	吴丰玉, 栗夏莲. 糖尿病患者眼底照相人工与人工智能分析结果比较[J]. 中华眼底病杂志, 2021, 37: 27-31. doi:10.3760/cma.j.cn511434-20200915-00452.
[12]	Yan A, Issar T, Tummanapalli SS, et al. Relationship between corneal confocal microscopy and markers of peripheral nerve structure and function in Type 2 diabetes[J]. Diabet Med, 2020, 37: 326-334. doi:10.1111/dme.13952
[13]	Wei S, Shi F, Wang Y, et al. A deep learning model for automated sub-basal corneal nerve segmentation and evaluation using in vivo confocal microscopy[J]. Transl Vis Sci Technol, 2020, 9: 32. doi:10.1167/tvst.9.2.32
[14]	Williams BM, Borroni D, Liu R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study[J]. Diabetologia, 2020, 63: 419-430. doi:10.1007/s00125-019-05023-4
[15]	Yildiz E, Arslan AT, Yildiz TA, et al. Generative adversarial network based automatic segmentation of corneal subbasal nerves on in vivo confocal microscopy images[J]. Transl Vis Sci Technol, 2021, 10: 33. doi:10.1167/tvst.10.6.33
[16]	Lopez-De-Andres A, Hernandez-Barrera V, Lopez R, et al. Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks[J]. BMC Med Res Methodol, 2016, 16: 160. doi:10.1186/s12874-016-0265-5
[17]	Dagliati A, Marini S, Sacchi L, et al. Machine learn-ing methods to predict diabetes complications[J]. J Diabetes Sci Technol, 2018, 12: 295-302. doi:10.1177/1932296817706375
[18]	Norouzi S, Kamel GA, Sistani S, et al. A mobile application for managing diabetic patients' nutrition: a food recommender system[J]. Arch Iran Med, 2018, 21: 466-472
[19]	Fang S, Shao Z, Kerr DA, et al. An end-to-end image-based automatic food energy estimation technique based on learned energy distribution images: protocol and methodology[J]. Nutrients, 2019, 11. doi:10.3390/nu11040877
[20]	Omisore OM, Ojokoh BA, Babalola AE, et al. An affective learning-based system for diagnosis and personalized management of diabetes mellitus[J]. Future Gener Comp Sy, 2021, 117: 273-290. doi:10.1016/j. future.2020.10.035
[21]	Jacobs PG, Resalat N, El YJ, et al. Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate[J]. J Diabetes Sci Technol, 2015, 9: 1175-1184. doi:10.1177/1932296815609371
[22]	Sevil M, Rashid M, Hajizadeh I, et al. Physical activity and psychological stress detection and assessment of their effects on glucose concentration predictions in diabetes management[J]. IEEE Trans Biomed Eng, 2021, 68: 2251-2260. doi:10.1109/tbme.2020.3049109
[23]	Cobelli C, Renard E, Kovatchev B. Artificial pancreas: past, present, future[J]. Diabetes, 2011, 60: 2672-2682. doi:10.2337/db11-0654
[24]	Topol EJ. High-performance medicine: the convergence of human and artificial intelligence[J]. Nat Med, 2019, 25: 44-56. doi:10.1038/s41591-018-0300-7
[25]	Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, et al. Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment[J]. Artif Intell Med, 2018, 87: 20-33. doi:10.1016/j.artmed.2018.03.00
[26]	Noaro G, Cappon G, Sparacino G, et al. Nonlinear machine learning models for insulin bolus estimation in type 1 diabetes therapy[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 5502-5505. doi:10.1109/embc44109.2020.9176021
[27]	Tyler NS, Mosquera-Lopez CM, Wilson LM, et al. An artificial intelligence decision support system for the management of type 1 diabetes[J]. Nat Metab, 2020, 2: 612-619. doi:10.1038/s42255-020-0212-y
[28]	Zhu T, Li K, Kuang L, et al. An insulin bolus advisor for type 1 diabetes using deep reinforcement learning[J]. Sensors, 2020, 20: 5058. doi:10.3390/s20185058
[29]	Nimri R, Battelino T, Laffel LM, et al. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes[J]. Nat Med, 2020, 26: 1380-1384. doi:10.1038/s41591-020-1045-7
[30]	Noaro G, Cappon G, Vettoretti M, et al. Machine-learning based model to improve insulin bolus calculation in type 1 diabetes therapy[J]. IEEE Trans Biomed Eng, 2021, 68: 247-255. doi:10.1109/tbme.2020.3004031
[31]	Nimri R, Oron T, Muller I, et al. Adjustment of insulin pump settings in type 1 diabetes management: advisor pro device compared to physicians' recommendations[J]. J Diabetes Sci Technol, 2022, 16: 364-372. doi:10.1177/1932296820965561
[32]	Zhu T, Li K, Herrero P, et al. Basal glucose control in type 1 diabetes using deep reinforcement learning: an in silico validation[J]. IEEE J Biomed Health Inform, 2021, 25: 1223-1232. doi:10.1109/jbhi.2020.3014556
[33]	Bertachi A, Vinals C,Biagi L, et al. Prediction of nocturnal hypoglycemia in adults with type 1 diabetes under multiple daily injections using continuous glucose monitor-ing and physical activity monitor[J]. Sensors, 2020, 20. doi:10.3390/s20061705
[34]	计成, 代晶, 李林通, 等. 基于人工智能系统的健康教育对2型糖尿病患者控制指标的影响[J]. 药学服务与研究, 2021, 21:1-5. doi:10.5428/pcar20210101.
[35]	杨昕, 姚姝妍, 杨宏杰, 等. 智能化系统在2型糖尿病患者院外管理中的应用[J]. 临床荟萃, 2021, 36: 927-932. doi:10.3969/j.issn.1004-583X.2021.10.012.