SERS-based deep learning approach for early detection of gestational diabetes mellitus

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 93.7% diagnostic accuracy

• Primary Outcome: Diagnostic accuracy for early GDM detection

• Nnt Or Sensitivity: Sensitivity: 0.95, Specificity: 0.93

• Confidence Interval: Not reported

Clinical Application

Potentially feasible for point-of-care testing given rapid turnaround time and minimal sample requirements, but requires specialized SERS equipment Could complement current Australian GDM screening protocols if validated, but would require TGA approval and cost-effectiveness analysis for PBS consideration Pregnant women requiring early GDM screening, though specific gestational age and risk factors unclear

Abstract

Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of adverse pregnancy events. However, current clinical screening methods for GDM still exhibit limitations in detection speed, sensitivity and convenience, making it difficult to meet the clinical demand for rapid early-pregnancy screening. To address this, we propose a novel strategy for early GDM diagnosis based on surface-enhanced Raman spectroscopy (SERS) combined with deep learning, aiming to achieve rapid and accurate early screening. Characteristic SERS spectra of serum were obtained using a substrate based on silver nanoparticles (Ag NPs). A fused PCA-CNN model integrating principal component analysis (PCA) for dimensionality reduction and a one-dimensional convolutional neural network (1D-CNN) for feature learning was developed. The PCA-CNN model effectively extracts potential biomarker features from serum SERS spectra, achieving a diagnostic accuracy of 93.7%, with sensitivity and specificity of 0.95 and 0.93, respectively. Moreover, the entire detection process can be completed within 30 min, requires about 2.5 μL of serum per sample, and involves minimal preprocessing, highlighting both efficiency and practicality. This study provides a novel method for early GDM screening that combines high diagnostic performance with clinical applicability, offering promising technical support for early intervention and clinical management of GDM.