Corneal Confocal Microscopy (CCM) is a sensitive tool for assessing small-fiber damage in Diabetic Peripheral Neuropathy (DPN), yet the development of robust, automated deep learning-based diagnostic models is limited by scarce labelled data and fine-grained variability in corneal nerve morphology. Although Artificial Intelligence (AI)-driven foundation generative models excel at natural image synthesis, they often struggle in medical imaging due to limited domain-specific training, compromising the anatomical fidelity required for clinical analysis. To overcome these limitations, we propose a Weight-Decomposed Low-Rank Adaptation (WDLoRA)-based multimodal generative framework for clinically guided CCM image synthesis. WDLoRA is a parameter-efficient fine-tuning (PEFT) mechanism that decouples magnitude and directional weight updates, enabling foundation generative models to independently learn the orientation (nerve topology) and intensity (stromal contrast) required for medical realism. By jointly conditioning on nerve segmentation masks and disease-specific clinical prompts, the model synthesises anatomically coherent images across the DPN spectrum (Control, T1NoDPN, T1DPN). A comprehensive three-pillar evaluation demonstrates that the proposed framework achieves state-of-the-art visual fidelity (Fréchet Inception Distance (FID): 5.18) and structural integrity (Structural Similarity Index Measure (SSIM): 0.630), significantly outperforming GAN and standard diffusion baselines. Crucially, the synthetic images preserve gold-standard clinical biomarkers and are statistically equivalent to real patient data. When used to train automated diagnostic models, the synthetic dataset improves downstream diagnostic accuracy by 2.1% and segmentation performance by 2.2%, validating the framework's potential to alleviate data bottlenecks in medical AI.

翻译：角膜共聚焦显微镜（CCM）是评估糖尿病周围神经病变（DPN）中小纤维损伤的敏感工具，然而，由于标记数据稀缺以及角膜神经形态的细粒度变异，开发鲁棒的、基于深度学习的自动化诊断模型受到限制。尽管人工智能（AI）驱动的基础生成模型在自然图像合成方面表现出色，但由于领域特定训练有限，它们在医学影像中往往表现不佳，从而损害了临床分析所需的解剖学保真度。为克服这些限制，我们提出了一种基于权重分解低秩自适应（WDLoRA）的多模态生成框架，用于临床引导的CCM图像合成。WDLoRA是一种参数高效微调（PEFT）机制，它解耦了权重更新的幅度和方向，使基础生成模型能够独立学习医学真实性所需的方向（神经拓扑结构）和强度（基质对比度）。通过联合以神经分割掩码和疾病特异性临床提示为条件，该模型能够在DPN谱系（对照组、T1NoDPN、T1DPN）中合成解剖学上连贯的图像。全面的三支柱评估表明，所提出的框架实现了最先进的视觉保真度（Fréchet Inception距离（FID）：5.18）和结构完整性（结构相似性指数（SSIM）：0.630），显著优于GAN和标准扩散基线。至关重要的是，合成图像保留了金标准的临床生物标志物，并且在统计上等同于真实患者数据。当用于训练自动化诊断模型时，合成数据集将下游诊断准确率提高了2.1%，分割性能提高了2.2%，验证了该框架在缓解医学AI数据瓶颈方面的潜力。