Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

被引：24

作者：

Huang, Jingtong ^{[1
]}

Yeung, Andrea M. M. ^{[1
]}

Armstrong, David G. G. ^{[2
]}

Battarbee, Ashley N. N. ^{[3
]}

Cuadros, Jorge ^{[4
]}

Espinoza, Juan C. C. ^{[5
]}

Kleinberg, Samantha ^{[6
]}

Mathioudakis, Nestoras ^{[7
]}

Swerdlow, Mark A. A. ^{[2
]}

Klonoff, David C. C. ^{[1
,8
]}

机构：

[1] Diabet Technol Soc, Burlingame, CA USA

[2] Univ Southern Calif, Keck Sch Med, Los Angeles, CA USA

[3] Univ Alabama Birmingham, Ctr Womens Reprod Hlth, Birmingham, AL USA

[4] Univ Calif Berkeley, Meredith Morgan Optometr Eye Ctr, Berkeley, CA USA

[5] Univ Southern Calif, Childrens Hosp Los Angeles, Los Angeles, CA USA

[6] Stevens Inst Technol, Hoboken, NJ USA

[7] Johns Hopkins Univ, Baltimore, MD USA

[8] Diabet Res Inst, Mills Peninsula Med Ctr, 100 South San Mateo Dr,Room 5147, San Mateo, CA 94401 USA

来源：

JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY | 2023年 / 17卷 / 01期

关键词：

diabetes; complications; artificial intelligence; machine learning algorithm; risk factors; prediction; KIDNEY-DISEASE; INPATIENT HYPOGLYCEMIA; PERIPHERAL NEUROPATHY; VALIDATION; RISK; MODEL; NEPHROPATHY;

D O I：

10.1177/19322968221124583

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

引用

收藏

页码：224 / 238

页数：15

相关论文

共 99 条

[91] Deep learning in diabetic foot ulcers detection: A comprehensive evaluation [J].

Yap, Moi Hoon ;

Hachiuma, Ryo ;

Alavi, Azadeh ;

Brungel, Raphael ;

Cassidy, Bill ;

Goyal, Manu ;

Zhu, Hongtao ;

Ruckert, Johannes ;

Olshansky, Moshe ;

Huang, Xiao ;

Saito, Hideo ;

Hassanpour, Saeed ;

Friedrich, Christoph M. ;

Ascher, David ;

Song, Anping ;

Kajita, Hiroki ;

Gillespie, David ;

Reeves, Neil D. ;

Pappachan, Joseph ;

O'Shea, Claire ;

Frank, Eibe .

COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 135

[92]

Yap Moi Hoon, 2018, J Diabetes Sci Technol, V12, P169, DOI 10.1177/1932296817713761

[93] Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study [J].

Ye, Yunzhen ;

Xiong, Yu ;

Zhou, Qiongjie ;

Wu, Jiangnan ;

Li, Xiaotian ;

Xiao, Xirong .

JOURNAL OF DIABETES RESEARCH, 2020, 2020

[94] Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry [J].

Yudovsky, Dmitry ;

Nouvong, Aksone ;

Schomacker, Kevin ;

Pilon, Laurent .

JOURNAL OF BIOMEDICAL OPTICS, 2011, 16 (02)

[95] Machine Learning Models for Inpatient Glucose Prediction [J].

Zale, Andrew ;

Mathioudakis, Nestoras .

CURRENT DIABETES REPORTS, 2022, 22 (08) :353-364

[96] Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients [J].

Zale, Andrew D. ;

Abusamaan, Mohammed S. ;

McGready, John ;

Mathioudakis, Nestoras .

ECLINICALMEDICINE, 2022, 44

[97] Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis [J].

Zhang, Zheqing ;

Yang, Luqian ;

Han, Wentao ;

Wu, Yaoyu ;

Zhang, Linhui ;

Gao, Chun ;

Jiang, Kui ;

Liu, Yun ;

Wu, Huiqun .

JOURNAL OF MEDICAL INTERNET RESEARCH, 2022, 24 (03)

[98] A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women [J].

Zheng, Tao ;

Ye, Weiping ;

Wang, Xipeng ;

Li, Xiaoyong ;

Zhang, Jun ;

Little, Julian ;

Zhou, Lixia ;

Zhang, Lin .

BMC PREGNANCY AND CHILDBIRTH, 2019, 19 (1)

[99] Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease [J].

Zou, Yutong ;

Zhao, Lijun ;

Zhang, Junlin ;

Wang, Yiting ;

Wu, Yucheng ;

Ren, Honghong ;

Wang, Tingli ;

Zhang, Rui ;

Wang, Jiali ;

Zhao, Yuancheng ;

Qin, Chunmei ;

Xu, Huan ;

Li, Lin ;

Chai, Zhonglin ;

Cooper, Mark E. ;

Tong, Nanwei ;

Liu, Fang .

RENAL FAILURE, 2022, 44 (01) :562-570

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