Hybrid Quantum-Inspired Transformer Network for Kidney Stone Prediction Using Urine Analysis and CT Scans

Kidney stones, in particular those from crystallising calcium oxalate, are a major healthcare issue. They must be accurately and promptly located to ensure effective treatment. Traditional diagnostic modalities including imaging studies and biochemical analysis can be expensive, invasive, and ineffective in prognosticating disease course. In this study, the authors propose HQIT-Net (Hybrid Quantum-Inspired Transformer Network), a new deep learning system for accurately and non-invasively predicting kidney stones by using biochemical indicators in urine and CT image. The novelty of HQIT-Net is that QCNNs and ViT are employed in combination to handle CT images, as well as the application of Bi-Directional LSTM with Attention in order to capture sequential patterns from numerical urine data. The unique incorporation of these modes is the key innovation in this framework, which provides a holistic view of kidney stone formation—structurally and biochemically. The model is trained on two datasets: (1) the dataset of “Physical Characteristics of Urines with and Without Crystals” consisting of scarce urine samples categorized as per six vital attributes (specific gravity, pH, osmolarity, conductivity, urea concentration and calcium concentration); and (2) the PACS CT Kidney dataset(Dhaka), to detect crystallization at kidneys and kidney without stone. To ensure data privacy and improve generalization performance, HQIT-Net adopts a Federated Self-Supervised Learning (FSSL) architecture that supports decentralized training over hospitals without sharing raw data. Experimental results show that HQIT-Net outperforms the state-of-the-art models, such as Swin Transformer, ConvNeXt, EfficientNetV2, DeepLabV3 + and GNNs with an accuracy of 99.1%, sensitivity of 98.6%, specificity of 99.3% and AUC-ROC of 0.998 respectively. HQIT-Net will establish a new benchmark for real-time privacy-preserving detection of kidney stone, which integrates quantum-inspired AI, attention-based learning and multimodal data fusion.