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

Faura, Georgina; Boix‐Lemonche, Gerard; Holmeide, Anne Kristin; Verkauskienė, Rasa; Volke, Vallo; Sokolovska, Jeļizaveta; Petrovski, Goran

doi:10.3390/s22030718

Cited by 10 publications

(9 citation statements)

References 150 publications

(220 reference statements)

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“…Similar to sweat, the physicochemical properties and enrichment of tears can be detected non‐invasively with colorimetric biosensors to determine the health status of the living body 95–97 . Contact lenses combined with colorimetric sensors have been widely used for in situ monitoring of tears.…”

Section: Colorimetric and Fluorescence Biosensingmentioning

confidence: 99%

“…Similar to sweat, the physicochemical properties and enrichment of tears can be detected non-invasively with colorimetric biosensors to determine the health status of the living body. [95][96][97] Contact lenses combined with colorimetric sensors have been widely used for in situ monitoring of tears. Riaz et al employed Anthocyanin pigments to functionalize the polymeric matrices of contact lenses to continuously monitor the ocular pH values.…”

Section: Tears Detectionmentioning

confidence: 99%

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Optical biosensing systems for a biological living body

Yan

Guo

Wang

et al. 2023

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With the development of technology, biosensors are increasingly used in the biomedical field. Due to the high sensitivity of optical signals, good stability, high signal‐to‐noise ratio, and different spectral characteristics of different analytes, optical biosensors can achieve direct and real‐time detection of analytes with high specificity compared with traditional analytical techniques. In view of the rapid development of optical biosensors for in vivo applications and their promising future, we have attempted to summarize the working principles and recent advances in application in humans, with the aim of helping readers to gain an in‐depth and detailed understanding of optical biosensors developed in recent years for applications in the biological living body. In this review, we focus on some of the current widely used and promising optical biosensors, including colorimetric biosensors, fluorescence biosensors, and surface‐enhanced‐Raman‐scattering‐based biosensors. The working principles for each type of optical biosensor are concisely described and the application of the biosensor in the living body is summarized by category. We conclude by looking at the prospects of in vivo applications of optical biosensors.

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Section: Colorimetric and Fluorescence Biosensingmentioning

confidence: 99%

Section: Tears Detectionmentioning

confidence: 99%

Optical biosensing systems for a biological living body

Yan

Guo

Wang

et al. 2023

VIEW

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“…2. In this example, the kernel k[n] = [1], [2], [3], [4] [8], [8], [9], [14], [19], [20]. The provided example demonstrates that the correlation will reach its maximum value when the input signal most closely resembles the values of the kernel.…”

Section: A Proposed Correlation Operationmentioning

confidence: 99%

“…According to medical studies, small amounts of glucose are found in saliva, tears, sweat and urine [3]. These biological samples offer the potential for non-invasive glucose level prediction in the body.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Diabetes From Exhaled Human Breath Using Deep Hybrid Architecture

et al. 2023

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In this paper, we have proposed an automated medical system for detecting type 2 diabetes from exhaled breath. Human breath can be used as a diagnostic sample for detecting many diseases as it contains many gases that are dissolved in the blood. Breath-based analysis stands out among the different non-invasive ways of detection as it provides more accurate predictions and offers many advantages. In this work, the concentration of acetone in the exhaled breath is analysed to detect type 2 diabetes. A new sensing module consisting of an array of sensors is implemented for monitoring the acetone concentration to detect the disease. Deep learning algorithms like Convolutional Neural Networks (CNN) are normally used to automatically analyse medical data to make predictions. Even though the CNN performs well, a few modifications to the network layout can further improve the classification accuracy of the learning model. To analyse the sensor signals to generate predictions, a new deep hybrid Correlational Neural Network (CORNN) is designed and implemented in this research. The proposed detection approach and deep learning algorithm offer improved accuracy when compared to other non-invasive techniques.

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“…Some outstanding research has been successfully applied in breast cancer 6 , hepatocellular carcinoma 7 , lung cancer 8 , and other cancer types automatic recognition and survival prediction tasks 6 – 8 . Similar to cancer research, many machine learning methods have been proposed for the effective diagnosis, prognosis, management, and treatment of diabetes 9 – 11 in the past few years. For the diagnosis and treatment of diseases, the methods used by researchers are divided into rule-based methods 12 – 15 , such as C4.5, CART, and random forest (RF), and nonrule-based methods 16 – 19 , such as support vector machine (SVM), Bayes, k-nearest neighbor (k-NN), and neural network (NN).…”

Section: Introductionmentioning

confidence: 99%

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes

Hao

Luo

Pan

2022

Sci Rep

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Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method.

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

Cited by 10 publications

References 150 publications

Optical biosensing systems for a biological living body

Optical biosensing systems for a biological living body

Automated Detection of Diabetes From Exhaled Human Breath Using Deep Hybrid Architecture

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes

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