Artificial Intelligence-Coupled Self-Calibrating SERS Spectroscopy for Robust Clinical Diagnosis of Diabetes and...

Diabetes mellitus (DM), a prevalent metabolic disorder, poses significant diagnostic and therapeutic challenges, especially, in the early stage diagnosis of diabetes related complications. Accurate early stage diagnosis of diabetes and its complications is essential for preventing chronic health problems, improving treatment outcomes, and reducing healthcare costs by enabling timely medical interventions and personalized ailment management strategies. However, conventional diagnostic techniques often face challenges to offer the required sensitivity and accuracy that are essential for early stage detection and classification of diabetes and its complications. In this context, we developed an advanced diagnostic model that utilized gold nanoparticles (AuNPs) functionalized with 4-mercaptophenylboronic acid (AuNPs@4MPBA) to enable both specific and nonspecific SERS detection of diabetes biomarkers in serum samples. In this study, the artificial intelligence (AI)-assisted self-calibration method was smartly integrated within the SERS-based diagnostic system to enable efficient early stage detection of diabetes and its associated complications. This combined diagnostic approach, demonstrating high diagnostic accuracy required only minimal sample volumes for the implementation unlike conventional diagnostic methods. Essentially, a ResNet-LSTM multihead self-attention neural network, integrated with the self-calibrating SERS technique facilitated the precise classification as well as detection of diabetes and related complications. Unlike the conventional diagnostic methods with limited scope of tracking the postmedication complications, the present self-calibrating SERS-AI combined diagnostic method provided accurate reliable diagnosis of the diabetic patients even with the premedication history. Furthermore, the incorporation of cosine similarity and Pearson's correlation methods ascertained the generalization and improved accuracy of the diagnostic model, apart from limiting the scope of clinical misdiagnosis.