Non-invasive diagnosis of pancreatic steatosis with ultrasound images using deep learning network.

Objective: This study aimed to verify whether pancreatic steatosis (PS) is an independent risk factor for type 2 diabetes mellitus (T2DM). We also developed and validated a deep learning model for the diagnosis of PS using ultrasonography (US) images based on histological classifications. Methods: In this retrospective study, we analysed data from 139 patients who underwent US imaging of the pancreas followed by pancreatic resection at our medical institution. Logistic regression analysis was employed to ascertain the independent predictors of T2DM. The diagnostic efficacy of the deep learning model for PS was assessed using receiver operating characteristic curve analysis and compared with traditional visual assessment methodology in US imaging. Results: The incidence rate of PS in the study cohort was 64.7 %. Logistic regression analysis revealed that age (P = 0.003) and the presence of PS (P = 0.048) were independent factors associated with T2DM. The deep learning model demonstrated robust diagnostic capabilities for PS, with areas under the curve of 0.901 and 0.837, sensitivities of 0.895 and 0.920, specificities of 0.700 and 0.765, accuracies of 0.814 and 0.857, and F1-scores of 0.850 and 0.885 for the training and validation cohorts, respectively. These metrics significantly outperformed those of conventional US imaging (P < 0.001 and P = 0.045, respectively). Conclusion: The deep learning model significantly enhanced the diagnostic accuracy of conventional ultrasound for PS detection. Its high sensitivity could facilitate widespread screening for PS in large populations, aiding in the early identification of individuals at an elevated risk for T2DM in routine clinical practice.