Full-Endoscopic Transforaminal Approach for Lumbar Discectomy.

With technical advancements, the full-endoscopic transforaminal approach for lumbar discectomy (ETALD) is gaining popularity. This technique utilizes various tools and instruments, including a dilator, a beveled working sleeve, and an endoscope with a 20-degree angle and 177 mm length, equipped with a 9.3-diameter oval shaft and a 5.6 mm diameter working channel. Additionally, the procedure involves using a Kerrison punch (5.5 mm), rongeur (3-4 mm), punch (5.4 mm), tip control radioablator applying a radiofrequency current of 4 MHz, fluid control irrigation and suction pump device, 5.5 mm oval burr with lateral protection, burr round, and the diamond round. During the surgery, it is essential to identify significant landmarks, including the caudal pedicle, ascending facet, annulus fibrosis, posterior longitudinal ligament, and the exiting nerve root. The steps of the technique are relatively easy to follow, especially when utilizing the appropriate instruments and having a good understanding of the anatomy. Research studies have demonstrated comparable outcomes to open microdiscectomy techniques. ETALD presents itself as a safe option for lumbar discectomy, as it minimizes tissue disruption, results in low postoperative surgical site pain, and allows for early mobilization.

An Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Setting.

AIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (EAHs) in an emergency trauma setting. MATERIAL AND METHODS: A total of 150 sets of cranial computed tomography (CT) scans were used in this study with a total of 11,025 images. Of the CTs, 75 were labeled as EAH, the remaining 75 were normal. A random forest algorithm was utilized for the detection of EAHs. The CTs were randomized into two groups: 100 samples for training of the algorithm (split evenly between EAH and normal cases), and 50 samples for testing. In the training phase, the algorithm scanned every CT slice separately for image features such as entropy, moment, and variance. If the algorithm determined an EAH on two or more images in a CT set, then the workflow produced an alert in the form of an email. RESULTS: Data from 50 patients (25 EAH and 25 controls) were used for testing the EWS. For all CTs with an EAH, an alert was produced, with a 0% false-negative rate. For 16% of the cases, the practitioner received an email from the EWS that the patient might have an EAH despite having a normal CT scan. Positive and negative predictive values were 86% and 100%, respectively. CONCLUSION: An EWS based on a machine learning algorithm is an efficient and inexpensive way of facilitating the work of emergency practitioners such as emergency physicians, neuroradiologists, and neurosurgeons.