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Review
. 2022 Jan 18;22(3):718.
doi: 10.3390/s22030718.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

Georgina Faura  1   2 , Gerard Boix-Lemonche  1 , Anne Kristin Holmeide  3 , Rasa Verkauskiene  4 , Vallo Volke  5   6 , Jelizaveta Sokolovska  7 , Goran Petrovski  1   8
Affiliations
Review

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

Georgina Faura et al. Sensors (Basel). .

Abstract

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

Keywords: diabetes mellitus; diabetic retinopathy; early detection and diagnosis; glucose sensing; machine learning; point-of-care; screening; sensor arrays.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 4
Figure 4
Schematic structure of the multi-layer sensor designed by Calabria et al. [113] (redrawn). PAH: poly(allylamine hydrochloride); PSS: poly(styrene sulfonate); LOx: L-lactate oxidase; HRP: horse radish peroxidase; TMB: 3,3′,5,5′-tetramethylbenzidine.
Figure 5
Figure 5
Scheme of the folding Schirmer strip sensor described in Kang et al. [130] (redrawn).
Figure 1
Figure 1
Schematic representation of classic enzymatic colorimetric methods for the detection of glucose. Indicators: potassium iodide (KI) [70,71,72,73,74,75,76,77], 2,4,6-tribromo-3-hydroxy benzoic acid (TBHBA) + 4-aminoantipyrine (4-AAP) [78,79], N-ethyl-N(3-sulfopropyl)-3-methyl-aniline sodium salt (TOPS) + 4-AAP [80], 3,5-dichloro-2-hydroxybenzenesulfonic acid (DHBS) + 4-AAP [81], 3-aminopropyltriethoxysilane (APTMS) + 4-AAP [82], pH indicator [83], 3,3′-diaminobenzidine (DAB) [84], 3,3′,5,5′-tetramethyl-benzidine (TMB) [81,85].
Figure 2
Figure 2
Scheme of a generic lateral-flow immunoassay paper-based sensor. Redrawn from Hainsworth et al. [19].
Figure 3
Figure 3
On the left, schematic representation of the paper-based sensor described in Hiraoka et al. [92] for the clinical assessment of albumin index (redrawn). On the right, three possible results for the test are shown. The green line represents a hand-drawn straight line that passes through the top of the two color-changed zones. Results (albuminuria index) are interpreted depending on which zone of the results chart (signaled as 1 or 2 in the scheme on the left) the drawn straight line crosses. For a better interpretation of the color references in this figure, the reader is referred to the web version of the article.
Figure 6
Figure 6
Schematic representation of the glucose sensor described by Wei et al. [84]. (redrawn).
Figure 7
Figure 7
Simplified comparison of the sense of taste and a sensor array + ML technology.
See this image and copyright information in PMC

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