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Yang C, Zhou C. Observation on the changes of visual field and optic nerve fiber layer thickness in patients with early diabetic retinopathy. Photodiagnosis Photodyn Ther 2024; 47:104197. [PMID: 38723758 DOI: 10.1016/j.pdpdt.2024.104197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/27/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM) and is a leading cause of vision loss. Early detection of DR-related neurodegenerative changes is crucial for effective management and prevention of vision loss in diabetic patients. METHODS In this study, we employed spectral-domain polarization-sensitive optical coherence tomography (SD PS-OCT) to assess retinal nerve fiber layer (RNFL) changes in 120 eyes from 60 types 1 DM patients without clinical DR and 60 age-matched healthy controls. Visual field testing was performed to evaluate mean sensitivity (MS) and mean defect (MD) as indicators of visual function. RESULTS SD PS-OCT measurements revealed significant reductions in RNFL birefringence, retardation, and thickness in type 1 DM patients compared to healthy controls. Visual field testing showed decreased MS and increased MD in DM patients, indicating functional impairment correlated with RNFL alterations. CONCLUSION Our findings demonstrate early neurodegenerative changes in the RNFL of type 1 DM patients without clinical DR, highlighting the potential of SD PS-OCT as a sensitive tool for early detection of subclinical DR-related neurodegeneration. These results underscore the importance of regular ophthalmic screenings in diabetic patients to enable timely intervention and prevent vision-threatening complications.
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Affiliation(s)
- Chen Yang
- In Eye Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
| | - Chunyang Zhou
- In Eye Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China.
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2
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Xiong C, Ren Z, Liu T. Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks. BIOMEDICAL OPTICS EXPRESS 2024; 15:2719-2740. [PMID: 38855672 PMCID: PMC11161381 DOI: 10.1364/boe.521059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 06/11/2024]
Abstract
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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Affiliation(s)
- Chengxin Xiong
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Zhong Ren
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Tao Liu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang 330038, China
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3
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Prasad V PNSBSV, Syed AH, Himansh M, Jana B, Mandal P, Sanki PK. Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression. Sci Rep 2024; 14:8352. [PMID: 38594267 PMCID: PMC11269628 DOI: 10.1038/s41598-024-53691-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/03/2024] [Indexed: 04/11/2024] Open
Abstract
Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.
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Affiliation(s)
- P N S B S V Prasad V
- Department of Electronics and Communication Engineering, SRM University -AP, Neerukonda, 522240, India
| | - Ali Hussain Syed
- Department of Electronics and Communication Engineering, SRM University -AP, Neerukonda, 522240, India
| | - Mudigonda Himansh
- Department of Computer Science and Engineering, SRM University -AP, Neerukonda, 522240, India
| | - Biswabandhu Jana
- Department of Electrical and Electronics Engineering, ABV-IIITM Gwalior, Gwalior, MP, 474015, India
| | - Pranab Mandal
- Department of Physics, SRM University -AP, Neerukonda, 522240, India
| | - Pradyut Kumar Sanki
- Department of Electronics and Communication Engineering, SRM University -AP, Neerukonda, 522240, India.
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Pérez-Pacheco A, Ramírez-Chavarría RG, Quispe-Siccha RM, Colín-García MP. Dynamic modeling of photoacoustic sensor data to classify human blood samples. Med Biol Eng Comput 2024; 62:389-403. [PMID: 37880558 PMCID: PMC10794472 DOI: 10.1007/s11517-023-02939-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/14/2023] [Indexed: 10/27/2023]
Abstract
The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios.
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Affiliation(s)
- Argelia Pérez-Pacheco
- Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México "Dr. Eduardo Liceaga", Dr. Balmis 148, 06720, Cuauhtémoc, Doctores, Ciudad de México, México.
| | - Roberto G Ramírez-Chavarría
- Instituto de Ingeniería, Universidad Nacional Autónoma de México, Av. Universidad 3000, 04510, Ciudad Universitaria, Coyoacán, Ciudad de México, México.
| | - Rosa M Quispe-Siccha
- Unidad de Investigación y Desarrollo Tecnológico (UIDT), Hospital General de México "Dr. Eduardo Liceaga", Dr. Balmis 148, 06720, Cuauhtémoc, Doctores, Ciudad de México, México
| | - Marco P Colín-García
- Programa de Maestría y Doctorado en Ingeniería, Universidad Nacional Autónoma de México, Av. Universidad 3000, 04510, Ciudad Universitaria, Coyoacán, Ciudad de México, México
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Liu T, Ren Z, Xiong C, Peng W, Wu J, Huang S, Liang G, Sun B. Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks. Heliyon 2023; 9:e20796. [PMID: 37842612 PMCID: PMC10569993 DOI: 10.1016/j.heliyon.2023.e20796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 09/11/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak-peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.
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Affiliation(s)
- Tao Liu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Zhong Ren
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
- Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Chengxin Xiong
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Wenping Peng
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Junli Wu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Shuanggen Huang
- Agricultural Equipment Key Laboratory of Jiangxi Provincial, Jiangxi Agriculture University, 330045 Nanchang, Jiangxi, China
| | - Gaoqiang Liang
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Bingheng Sun
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
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Naveena S, Bharathi A. Weighted entropy deep features on hybrid RNN with LSTM for glucose level and diabetes prediction. Comput Methods Biomech Biomed Engin 2022; 26:1-25. [PMID: 36448678 DOI: 10.1080/10255842.2022.2149263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 11/15/2022] [Indexed: 12/05/2022]
Abstract
Glucose level regulation with essential advice regarding diabetes must be provided to the patients to maintain their diet for diabetes treatment. Therefore, the academic community has focused on implementing novel glucose prediction techniques for decision support systems. Recent computational techniques for diagnosing diabetes have certain limitations, and also they are not evaluated under various datasets obtained from the different people of various countries. This generates inefficiency in the prediction systems to apply it in real-time applications. This paper plans to suggest a hybrid deep learning model for diabetes prediction and glucose level classification. Two benchmark datasets are used in the data collection process for experimenting. Initially, the deep selected features were extracted by the Convolutional Neural Network (CNN). Further, weighted entropy deep features are extracted, where the tuning of weight is taken place by the Modified Escaping Energy-based Harris Hawks Optimization. These features are processed in the glucose level classification using the modified Fuzzy classifier for classifying the high-level and low-level glucose. Further, glucose prediction is done by the Hybrid Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) termed R-LSTM with parameter optimization. From the experimental result, In the dataset 2 analyses on SMAPE, the MEE-HHO-R-LSTM is 12.5%, 87.5%, 50%, 12.5%, and 2.5% better than SVM, LSTM, DNN, RNN, and RNN-LSTM, at the learning percentage of 75%. The analytical results enforce that the suggested methods attain enhanced prediction performance concerning the evaluation metrics compared to conventional prediction models.
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Affiliation(s)
- Somasundaram Naveena
- Assistant Professor Senior Grade, Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India
| | - Ayyasamy Bharathi
- Professor, Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India
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Biswas D, Roy S, Vasudevan S. Biomedical Application of Photoacoustics: A Plethora of Opportunities. MICROMACHINES 2022; 13:1900. [PMID: 36363921 PMCID: PMC9692656 DOI: 10.3390/mi13111900] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/19/2022] [Accepted: 10/29/2022] [Indexed: 06/16/2023]
Abstract
The photoacoustic (PA) technique is a non-invasive, non-ionizing hybrid technique that exploits laser irradiation for sample excitation and acquires an ultrasound signal generated due to thermoelastic expansion of the sample. Being a hybrid technique, PA possesses the inherent advantages of conventional optical (high resolution) and ultrasonic (high depth of penetration in biological tissue) techniques and eliminates some of the major limitations of these conventional techniques. Hence, PA has been employed for different biomedical applications. In this review, we first discuss the basic physics of PA. Then, we discuss different aspects of PA techniques, which includes PA imaging and also PA frequency spectral analysis. The theory of PA signal generation, detection and analysis is also detailed in this work. Later, we also discuss the major biomedical application area of PA technique.
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Affiliation(s)
- Deblina Biswas
- School of Bioengineering and Food Technology, Shoolini University, Solan 173229, HP, India
| | - Swarup Roy
- School of Bioengineering and Food Technology, Shoolini University, Solan 173229, HP, India
| | - Srivathsan Vasudevan
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol 453552, MP, India
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Li G, Wang K, Wang D, Lin L. Noninvasive blood glucose detection system based on dynamic spectrum and “M+N″ theory. Anal Chim Acta 2022; 1201:339635. [DOI: 10.1016/j.aca.2022.339635] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/24/2022] [Accepted: 02/17/2022] [Indexed: 11/15/2022]
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Local Structure Awareness-Based Retinal Microaneurysm Detection with Multi-Feature Combination. Biomedicines 2022; 10:biomedicines10010124. [PMID: 35052803 PMCID: PMC8773350 DOI: 10.3390/biomedicines10010124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/31/2021] [Accepted: 01/03/2022] [Indexed: 01/02/2023] Open
Abstract
Retinal microaneurysm (MA) is the initial symptom of diabetic retinopathy (DR). The automatic detection of MA is helpful to assist doctors in diagnosis and treatment. Previous algorithms focused on the features of the target itself; however, the local structural features of the target and background are also worth exploring. To achieve MA detection, an efficient local structure awareness-based retinal MA detection with the multi-feature combination (LSAMFC) is proposed in this paper. We propose a novel local structure feature called a ring gradient descriptor (RGD) to describe the structural differences between an object and its surrounding area. Then, a combination of RGD with the salience and texture features is used by a Gradient Boosting Decision Tree (GBDT) for candidate classification. We evaluate our algorithm on two public datasets, i.e., the e-ophtha MA dataset and retinopathy online challenge (ROC) dataset. The experimental results show that the performance of the trained model significantly improved after combining traditional features with RGD, and the area under the receiver operating characteristic curve (AUC) values in the test results of the datasets e-ophtha MA and ROC increased from 0.9615 to 0.9751 and from 0.9066 to 0.9409, respectively.
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