LAUSR

To identify the sac and defect using deep learning algorithms and to compute hernial sac volume in an automated fashion. 70 ventral hernia CT scans were used. Slices with hernia defect and sac were annotated using computer vision tools. 80% of the data was used for training and validation. 20% data was used for testing. We used a combination of 3D reconstruction algorithms and deep learning to train the models. The model output was compared to manual annotation by experts. The testing data showed that our novel algorithm was able tp estimate the defect with an accuracy of 90%. The mean IOU for detection of hernia sac was 88% and volume estimation accuracy was 85%. A combination of supervised deep learning and 3D volumetry will increase the accuracy of volumetric indices and shorten the volume estimation time. In most parts of the world, the hernia sac volume is not calculated by the radiologist and the surgeon is left to estimate it using rudimentary means such as visual estimation. Automated hernia volumetry will play a major role in standardising hernia reports. We believe this is a pivotal step in creating actionable radiology reports that will help the surgeon assess the complexity and plan surgery.