ArticleBiomedical optics express2018
Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 118 citations in OpenAlex.
- Establishing a competence model in discriminatingBiomedical reports · 2026Article
- Noninvasive Blood Glucose Monitoring with Machine Learning Enhanced Transmittance Spectroscopy.ACS omega · 2026Article
- Developing an Integrated Toolbox for Raman Spectral Analysis with Both Artificial Neural Networks and Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2026Article
- Raman spectroscopy supported by machine learning reveals changes in balance of macromolecules in diabetic rat serum.Analytical and bioanalytical chemistry · 2025Article
- Unsupervised spectra information extraction using physics-informed neural networks in the presence of non-linearities and multi-agent problems.Scientific reports · 2025Article
- Diagnosis of pregnancy disorder in the first-trimester patient plasma with Raman spectroscopy and protein analysis.Bioengineering & translational medicine · 2024Article
- Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes.Chemical & biomedical imaging · 2024Article
- Noninvasive Glucose Measurements Through Transcutaneous Raman Spectroscopy: A Review.Journal of diabetes science and technology · 2024Review
- Classification of glucose-level in deionized water using machine learning models and data pre-processing technique.PloS one · 2024Article
- Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease.Scientific reports · 2023Article
- Quantitative Assessment of Low-Dose Photodynamic Therapy Effects on Diabetic Wound Healing Using Raman Spectroscopy.Pharmaceutics · 2023Article
- The emerging applications and advancements of Raman spectroscopy in pediatric cancers.Frontiers in oncology · 2023Review
- Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks.Lasers in medical science · 2022Article
- Comment on "Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks".Lasers in medical science · 2022Article
- Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects.British journal of cancer · 2022Review
- Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency.Applied spectroscopy · 2022Article
- Clinical assessment of W-band spectroscopy for non-invasive detection and monitoring of sustained hyperglycemia.Biomedical optics express · 2021Article
- Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications.IEEE access : practical innovations, open solutions · 2021Article
- Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies.Molecules (Basel, Switzerland) · 2020Review
- Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools: comment.Biomedical optics express · 2019Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9-90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0-82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.