Artificial Intelligence Techniques for Dual-discriminator Spiking Generative Adversarial Network Based Classification and Segmentation for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes: Trends and Challenges

Abstract

Diabetic foot ulcers (DFUs) represent a severe complication of diabetes mellitus, often leading to infection, amputation, and increased mortality. Early detection and accurate prediction of ulcer pathogenesis are critical for effective clinical intervention. Recent advancements in artificial intelligence have introduced innovative approaches that combine deep learning architectures with biologically inspired computing paradigms. This paper explores the integration of dual-discriminator spiking generative adversarial networks (S-GANs) for classification and segmentation tasks in DFU prediction. The proposed framework leverages spiking neural dynamics to mimic neuronal firing patterns, enhancing temporal feature representation while maintaining computational efficiency. The dual-discriminator mechanism improves both data realism and structural consistency, addressing limitations in traditional GAN-based medical imaging systems. Furthermore, multimodal data fusion, including imaging and clinical metadata, is incorporated to enhance predictive accuracy. This study provides a comprehensive overview of emerging trends, challenges, and opportunities in this domain, emphasizing robustness, interpretability, and scalability. The findings highlight the potential of combining spiking neural networks with adversarial learning to advance precision medicine in diabetic care. Challenges such as data scarcity, model generalization, and clinical deployment barriers are also discussed, offering insights for future research directions in intelligent healthcare systems.