OC-065 PEEKABOO WITH MESHES AND MACHINES: DEEP LEARNING TO IDENTIFY TACKS AND MESHES ON CT SCANS

Abstract

Abstract Aim To identify the presence of tacks and meshes in CT scans of patients who have previously undergone hernia repairs Materials & Methods We annotated data from >100 anonymised Hernia CT scans of patients who had undergone previous ventral hernia repairs and used machine learning(ML)/ deep learning(DL) techniques to identify the presence of meshes and tacks.Annotation of CT scans was performed using computer vision tools. A combination of image processing, feature extraction and artificial intelligence(AI) techniques were used to create models that could identify tacks and meshes on CT scans. Results We were able to identify the presence of meshes and tacks with >55% accuracy. The AI model under construction is continuing to improve performance. Conclusions Identification of the mesh is a challenge for surgeons and radiologists alike. The presence of machine learning techniques has revolutionised radiology and made the identification of obscure structures possible. Meshes present a unique challenge as they constitute a foreign tissue which integrates into native tissue. We believe that identification of hernia meshes and tacks can help surgeons identify the right planes for planning re-surgeries while operating in patients with previous hernia repairs or while tackling recurrences.