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.

Figure 1.

Conceptual map of AI and representative methods. Figure courtesy of Juan C. Espinoza. Abbreviations: AI, artificial intelligence; DL, deep leaning; ML, machine learning.

Figure 2.

Diagram of data sources and opportunities to apply AI methods to the continuum of care for persons with diabetes. Figure courtesy of Juan C. Espinoza. Abbreviation: AI, artificial intelligence.

Figure 3.

Variable importance plot of top 30 predictor variables of hypoglycemia. Source: Reproduced from Mathioudakis et al. Abbreviations: BG, blood glucose; BMI, body mass index; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; WBC, white blood cell count.

Figure 4.

Examples of the outputs of the proposed computer-aided diagnosis system. (a) An original fundus image from the Messidor database (filename: 20051020 57566 0100 PP.tif), kindly provided by the Messidor program partners (https://www.adcis.net/en/third-party/messidor/). The quality-verification module automatically assigned a probability of 0.98 that the image would have good quality. (b) Output of the automatic vessel segmentation module. The image shows the obtained pixel probability map indicating the likelihood of the pixel to belong to a vessel. White: higher probability. Source: Reproduced with permission from Sánchez et al.

Figure 5.

The receiver operating characteristic (ROC) curve for the six-variable model of predicting DFU is shown in blue. The area under the curve (AUC) is shown at the bottom right. Source: Reproduced from Stephanopoulos et al. Abbreviations: AUC, area under the curve; DFU, diabetic foot ulcer; ROC, receiver operating characteristic.

Figure 6.

(a-b) Example CCM images from healthy individuals. (c-d) Example CCM images from individuals with diabetic neuropathy. (e) An example of a CCM image. (f) Manual annotation of the previous image in 5e with red lines representing manual tracing of the nerve. (g) Manual annotation of 5e indicating branch and terminal points with green triangles denoting tail points and blue squares denoting branching points. Source: Reproduced from Williams et al under a Creative Commons license: http://creativecommons.org/licenses/by/4.0/. Abbreviation: CCM, corneal confocal microscopy.

Figure 7.

Current chronic kidney disease (CKD) risk factors used by KDIGO: CKD is defined as abnormalities of kidney structure or function, present for at least 3 months. CKD prognosis is currently classified into risk categories by a combination of clinical features, such as persistent albuminuria category (A1-A3) and glomerular filtration rate (GFR) category (G1-G5). Green = low or no risk; Yellow = moderately increased risk; Orange = high risk; Red = very high risk. Source: Reproduced with permission from de Boer et al. Abbreviations: CKD, chronic kidney disease; GFR, glomerular filtration rate; KDIGO, Kidney Disease: Improving Global Outcomes risk matrix.