Gomes Rahul, Kamrowski Connor, Mohan Pavithra Devy, Senor Cameron, Langlois Jordan, Wildenberg Joseph
Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA.
Interventional Radiology, Mayo Clinic Health System, Eau Claire, WI 54703, USA.
Diagnostics (Basel). 2022 Oct 13;12(10):2475. doi: 10.3390/diagnostics12102475.
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.
下腔静脉滤器（IVCF）在患有静脉血栓的特定患者中发挥着重要作用。然而，它们通常是临时性的，移除的显著延迟可能会给患者带来负面的健康后果。目前，所有介入放射学（IR）实践都负责追踪放置了IVCF的患者。由于其体积小且位于腹部深处，患者常常会忘记自己体内有IVCF。因此，新的医疗服务提供者要意识到滤器的存在会有显著延迟。患者可能因多种原因进行腹盆腔CT扫描，幸运的是，IVCF在这些扫描中清晰可见。在本研究中，开发了一种能够从轴向平面的CT扫描切片中分割出IVCF的深度学习模型。该模型在对372个CT扫描切片进行训练时，Dice评分为0.82。然后将分割模型与能够标记整个CT扫描是否存在IVCF的预测算法相结合。利用分割模型的预测算法在扫描中检测IVCF的准确率达到了92.22%。
