Wildenberg, Joseph C.

Abstract Foramen of Winslow hernia is a rare subtype of internal hernias, accounting for ⁓8% of all internal hernia cases. Its nonspecific clinical presentation often delays diagnosis, with ˂10% identified preoperatively. With advances in minimally invasive techniques, robotic-assisted surgery has emerged as a safe and precise method for managing complex intra-abdominal pathology. A 70-year-old male presented with an acute abdomen. He was hemodynamically stable, and computed tomography imaging suggested an internal hernia. Robotic-assisted abdominal exploration revealed a segment of colon herniated through the foramen of Winslow. The colon was carefully reduced without complications. A piece of omentum was placed into the defect, and the retracted gall bladder naturally positioned itself within the foramen, potentially minimizing the risk of recurrence. The patient recovered uneventfully and discharged on postoperative day one. To our knowledge, this is the first documented case of foramen of Winslow hernia repair using a robotic-assisted approach.

Abstract Bilateral ureteroinguinal hernia is a rare presentation with only five cases previously presented in the literature. We describe a 60-year-old male who was diagnosed with large bilateral hernias extending into the scrotum containing redundant ureters. Both hernias were repaired in staged fashion with retrograde temporary ureteral stent placement and modified Lichtenstein technique. Follow-up computerized tomography imaging with >5 years follow-up revealed intact repairs with no evidence of urinary obstruction.

IVC filters (IVCF) perform an important function in select patients that have venous blood clots. However, they are usually intended to be temporary, and significant delay in removal can have negative health consequences for the patient. Currently, all Interventional Radiology (IR) practices are tasked with tracking patients in whom IVCF are placed. Due to their small size and location deep within the abdomen it is common for patients to forget that they have an IVCF. Therefore, there is a significant delay for a new healthcare provider to become aware of the presence of a filter. Patients may have an abdominopelvic CT scan for many reasons and, fortunately, IVCF are clearly visible on these scans. In this research a deep learning model capable of segmenting IVCF from CT scan slices along the axial plane is developed. The model achieved a Dice score of 0.82 for training over 372 CT scan slices. The segmentation model is then integrated with a prediction algorithm capable of flagging an entire CT scan as having IVCF. The prediction algorithm utilizing the segmentation model achieved a 92.22% accuracy at detecting IVCF in the scans.