Abstract: This invention provides systems and methods that can detect incidental OVFs in CT examinations at the level of practicing radiologists. The OVF detection system leverages a deep convolutional neural network (CNN) to extract radiological features from each CT scan slice. These extracted features are processed through a feature aggregation module to make the final diagnosis for the full CT scan. Feature aggregation, can be performed in a variety of ways, including the use of a long short-term memory (LSTM) network. In one example, the CNN can be trained on a predetermined number of CT scans, which are established as reference standards. This result can effectively the performance of practicing radiologists on this test set in real world clinical circumstances. The system and method can be employed to assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists.