[Lumbar Facet Joint Injection: A Review of Efficacy and Safety]. / 요추 í›„ê´€ì ˆ 주사: ìž„ìƒì  ìœ ìš©ì„±ê³¼ ì•ˆì „ì„±ì— 대한 ê³ ì°°.

Facet joint arthrosis is a progressive degenerative disease that is frequently associated with other spinal degenerative disorders such as degenerative disc disease or spinal stenosis. Lumbar facet joint arthrosis can induce pain in the proximal lower extremities. However, symptoms and imaging findings of "facet joint syndrome" are not specific as they mimic the pain from herniated discs or nerve root compression. Currently, evidence for therapeutic intra-articular lumbar facet joint injections is still considered low, with a weak recommendation strength. Nevertheless, some studies have reported therapeutic effectiveness of facet joint injections. Moreover, the use of therapeutic facet joint injections in clinical practice has increased. This review article includes opinions based on the authors' experience with facet joint injections. This review primarily aimed to investigate the efficacy of lumbar facet joint injections and consider their associated safety aspects.

Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.

With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is an increased chance that patients with implanted cardiac devices require MRI examination during their lifetime. Though MRI is generally contraindicated in patients who have undergone pacemaker implantation with electronic circuits, the recent introduction of MR Conditional pacemaker allows physicians to take advantage of MRI to assess these patients during diagnosis and treatment. When MRI examinations of patients with pacemaker are requested, physicians must confirm whether the device is a conventional pacemaker or an MR Conditional pacemaker by reviewing chest radiographs or the electronic medical records (EMRs). The purpose of this study was to evaluate the utility of a deep convolutional neural network (DCNN) trained to detect pacemakers on chest radiographs and to determine the device's subclassification. The DCNN perfectly detected pacemakers on chest radiographs and the accuracy of the subclassification of pacemakers using the internal and external test datasets were 100.0% (n = 106/106) and 90.1% (n = 279/308). The DCNN can be applied to the radiologic workflow for double-checking purposes, thereby improving patient safety during MRI and preventing busy physicians from making errors.