Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks

Liu, Tao; Ren, Zhong; Xiong, Chengxin; Peng, Wenping; Wu, Junli; Huang, Shuanggen; Liang, Gaoqiang; Sun, Bingheng

doi:10.1016/j.heliyon.2023.e20796

Cited by 3 publications

(3 citation statements)

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“…Observing and analyzing vast amounts of data, these AI algorithms are remarkably efficient at identifying patterns [ 7 ]. AI is being applied extensively in diabetes care in four key areas: screening for retinal disorders automatically, clinical advice and decision support, predicting risk for particular groups, and creating tools that allow patients to take care of their conditions on their own [ 8 , 9 ]. Based on this, the purpose of this study is to evaluate the usage of AI in diabetes management as well as the level of knowledge regarding its possible advantages.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis

Alhalafi,

Alqahtani,

Alqarni

et al. 2024

Cureus

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Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I 2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I 2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I 2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.

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Section: Introductionmentioning

confidence: 99%

Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis

Alhalafi,

Alqahtani,

Alqarni

et al. 2024

Cureus

View full text Add to dashboard Cite

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“…To the best of the authors' knowledge, few studies have been conducted about the application of hybrid and advanced optimization algorithms in dealing with irregular structures. On the other hand, the hybridization of PSO with ANN improves performance and yields promising results for solving complex processes and engineering problems [ 60 , 61 ]. In addition, in some studies, compared to the GA algorithm, PSO has fast convergence and a lower number of computational formulations [ 62 , 63 ].…”

Section: Introductionmentioning

confidence: 99%

Design optimization of irregularity RC structure based on ANN-PSO

Zhang

2024

Heliyon

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“…Under the optimal parameters of improved QPSO-WNN algorithm, the MSE of BGC reached 0.3088 mmol/L. In addition, a QPSO-optimized WNN algorithm was proposed to ensure the high accuracy classification of diabetes [ 21 ]. For continuous blood glucose prediction, Mehrad [ 22 ] presented a DNN model, i.e., conventional neural network combined with long-short term memory (CNN-LSTM), to predict blood glucose levels for type 1 diabetes patients, and assessed the model's accuracy and clinical acceptability in different time.…”

Section: Introductionmentioning

confidence: 99%

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

Xiong,

Ren,

Liu

2024

Biomed. Opt. Express

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In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

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Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks

Cited by 3 publications

References 50 publications

Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis

Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis

Design optimization of irregularity RC structure based on ANN-PSO

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

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