ArticleLasers in medical science2022
Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks.
Article in Lasers in medical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 14 citations in OpenAlex.
- Literature-Grounded Simulation of Non-Invasive Glucose Monitoring Using NIR Wearable Sensor Archetypes: Accuracy Metrics, Candidate Confounders, and an Adaptive Calibration Framework.Sensors (Basel, Switzerland) · 2026Article
- Rapid diagnosis of celiac disease based on serum infrared spectroscopy combined with deep learning.Scientific reports · 2025Article
- Comparison of different Raman spectrometer models in the quantification of blood serum analytes.Lasers in medical science · 2025Article
- Comment on "Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers".Lasers in medical science · 2024Article
- Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms.Frontiers in immunology · 2023Article
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8 authors at 5 institutions in 2 countries.
Funding
Abstract
Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.
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Registered trials
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