Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Jingtong Huang; Andrea M. Yeung; David G. Armstrong; Ashley N. Battarbee; Jorge Cuadros; Juan C. Espinoza; Samantha Kleinberg; Nestoras Mathioudakis; Mark A. Swerdlow; David C. Klonoff

doi:10.1177/19322968221124583

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Jingtong Huang, Andrea M. Yeung, David G. Armstrong, Ashley N. Battarbee, Jorge Cuadros, Juan C. Espinoza, Samantha Kleinberg, Nestoras Mathioudakis, Mark A. Swerdlow, David C. Klonoff

School of Medicine

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

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.

Original language	English (US)
Pages (from-to)	224-238
Number of pages	15
Journal	Journal of Diabetes Science and Technology
Volume	17
Issue number	1
DOIs	https://doi.org/10.1177/19322968221124583
State	Published - Jan 2023

Keywords

artificial intelligence
complications
diabetes
machine learning algorithm
prediction
risk factors

ASJC Scopus subject areas

Bioengineering
Internal Medicine
Biomedical Engineering
Endocrinology, Diabetes and Metabolism

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title = "Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes",

abstract = "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.",

keywords = "artificial intelligence, complications, diabetes, machine learning algorithm, prediction, risk factors",

author = "Jingtong Huang and Yeung, {Andrea M.} and Armstrong, {David G.} and Battarbee, {Ashley N.} and Jorge Cuadros and Espinoza, {Juan C.} and Samantha Kleinberg and Nestoras Mathioudakis and Swerdlow, {Mark A.} and Klonoff, {David C.}",

note = "Funding Information: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.C. is the CEO of EyePACS, Inc. J.C.E.{\textquoteright}s time is supported in part by the Food and Drug Administration under award number P50FD006425 for The West Coast Consortium for Technology & Innovation in Pediatrics (PI: Espinoza). D.C.K. is a consultant to EOFlow, Fractyl Health, Integrity, Lifecare, Rockley Photonics, and Thirdwayv. The remaining authors have nothing to disclose. Publisher Copyright: {\textcopyright} 2022 Diabetes Technology Society.",

year = "2023",

month = jan,

doi = "10.1177/19322968221124583",

language = "English (US)",

volume = "17",

pages = "224--238",

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AU - Huang, Jingtong

AU - Yeung, Andrea M.

AU - Armstrong, David G.

AU - Battarbee, Ashley N.

AU - Cuadros, Jorge

AU - Espinoza, Juan C.

AU - Kleinberg, Samantha

AU - Mathioudakis, Nestoras

AU - Swerdlow, Mark A.

AU - Klonoff, David C.

N1 - Funding Information: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.C. is the CEO of EyePACS, Inc. J.C.E.’s time is supported in part by the Food and Drug Administration under award number P50FD006425 for The West Coast Consortium for Technology & Innovation in Pediatrics (PI: Espinoza). D.C.K. is a consultant to EOFlow, Fractyl Health, Integrity, Lifecare, Rockley Photonics, and Thirdwayv. The remaining authors have nothing to disclose. Publisher Copyright: © 2022 Diabetes Technology Society.

PY - 2023/1

Y1 - 2023/1

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - complications

KW - diabetes

KW - machine learning algorithm

KW - prediction

KW - risk factors

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Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

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Keywords

ASJC Scopus subject areas

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