Predicting pancreatic diseases from fundus images using deep learning

Pancreatic cancer (PC) is an extremely deadly cancer, with mortality rates closely tied to its frequency of occurrence. By the time of diagnosis, pancreatic cancer often presents at an advanced stage, and has often spread to other parts of the body. Due to the poor survival outcomes, PDAC is the fifth leading cause of global cancer death. The 5-year relative survival rate of pancreatic cancer was about 6% and the lowest level in all cancers. Currently, there are no established guidance for screening individuals at high risk for pancreatic cancer, including those with a family history of the pancreatic disease or chronic pancreatitis (CP). With the development of medicine, fundus maps can now predict many systemic diseases. Subsequently, the association between ocular changes and a few pancreatic diseases was also discovered. Therefore, our objective is to construct a deep learning model aimed at identifying correlations between ocular features and significant pancreatic ailments. The utilization of AI and fundus images has extended beyond the investigation of ocular disorders. Hence, in order to solve the tasks of PC and CP classification, we propose a brand new deep learning model (PANet) that integrates pre-trained CNN network, multi-scale feature modules, attention mechanisms, and an FC classifier. PANet adopts a ResNet34 backbone and selectively integrates attention modules to construct its fundamental architecture. To enhance feature extraction capability, PANet combines multi-scale feature modules before the attention module. Our model is trained and evaluated using a dataset comprising 1300 fundus images. The experimental outcomes illustrate the successful realization of our objectives, with the model achieving an accuracy of 91.50% and an area under the receiver operating characteristic curve (AUC) of 96.00% in PC classification, and an accuracy of 95.60% and an AUC of 99.20% in CP classification. Our study establishes a characterizing link between ocular features and major pancreatic diseases, providing a non-invasive, convenient, and complementary method for screening and detection of pancreatic diseases.