1879-LB: Predicting Diabetic Peripheral Neuropathy in Type 2 Diabetes Using a Multimodal Model Integrating Foot Radiograph and Electronic Medical Records

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Introduction and Objective: The global rise in diabetes has led to increased chronic complications, with diabetic peripheral neuropathy (DPN) being the most common. Undiagnosed DPN can progress to diabetic foot ulcers, making early screening at type 2 diabetes (T2D) diagnosis crucial. However, an optimal and cost-effective diagnostic strategy has yet to be established. This study aims to develop a multimodal deep learning model integrating foot radiographs and electronic medical records (EMRs) to improve DPN prediction. Methods: We utilized a small dataset consisting of 133 patients with 607 foot radiograph images and 133 EMRs from an internal dataset, and 29 patients with 111 foot radiograph images and 29 EMRs from an external dataset. To augment the data, we applied cropping, and utilized fine-tuning and multimodal learning to enhance performance. Our model was trained on foot radiograph images using ResNet50, while EMR data were concatenated with the image feature map at the classifier to build the multimodal model. Model performance was evaluated based on AUC, specificity, and accuracy. Results: Among the training cohort, 72 patients were classified as DPN (-) and 61 as DPN (+). The mean age was significantly lower in the DPN (+) group than in the DPN (-) group (59 ± 14 vs. 65 ± 12 years, P = 0.023), whereas HbA1c levels were comparable between the two groups (8.1 ± 2.2% vs. 7.8 ± 1.9%, P = 0.521). The proposed multimodal deep learning model achieved an AUC of 0.894 and an accuracy of 0.841 on the internal dataset, and an AUC of 0.723 and an accuracy of 0.730 on the external dataset. Notably, the multimodal approach outperformed single-input models, which exhibited lower AUC and accuracy in both internal and external testing. Conclusion: Despite a small dataset, our model showed strong predictive performance for DPN. In light of the lack of cost-effective diagnostic tools, this approach could serve as a valuable screening aid. Further studies with larger datasets are needed for broader clinical application. Disclosure C. Chung: None. Y. Jang: None. M. Kwon: None. J. Moon: None. K. Kim: None. G. Lee: None. J. Kim: None. Funding Korean Diabetes Association (2023F-7)

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