Objective To provide insights into the learning curve of unilateral biportal endoscopic (UBE) spine surgery by synthesizing available evidence on critical points and associated clinical outcomes. Methods A comprehensive literature search was conducted across multiple databases, yielding a pool of relevant studies. Inclusion criteria encompassed studies reporting on UBE learning curves and quantitative data related to clinical outcomes (operative time, hospital stay, and complications). Results A total of five studies were included in the analysis, providing six datasets to elucidate the UBE learning curve. Three of the five studies analyzed learning curves using the Cumulative Sum method and identified cutoff points. One study plotted learning curves and determined cutoff points based on surgical time analysis, while the remaining one study (providing two datasets) plotted learning curves using the phased analysis method. The mean value of the cutoff point in terms of the number of cases required to reach proficiency in time to surgery was calculated at 37.5 cases, with a range spanning from 14 to 58 cases. Notably, there was a statistically significant difference in time to surgery between the late group and the early group, with the late group demonstrating a significantly reduced time to surgery ( P &amp;lt; 0.0001). Additionally, the determined cutoff points exhibited significant variations when applied to patient outcome parameters, including postoperative hospitalization, postoperative drainage, and surgical complications ( P &amp;lt; 0.05). Conclusion While the analysis indicates that UBE surgery's learning curve is associated with surgical time, the limited focus on this metric and potential discrepancies in cutoff point determination highlight the need for a more comprehensive understanding. 

Unilateral biportal endoscopic spine surgery: a meta-analysis unveiling the learning curve and clinical benefits

Endoscopic Retrieval of a Penetrating Ethmoid Sinus Foreign Body with a Successful Minimally Invasive Approach: A Case Report

Accurate classification of glioma grades is crucial for effective treatment planning and patient prognosis. In this extended study, we propose a comprehensive approach combining radiomics features and machine learning techniques to classify glioma grades. We explore the effectiveness of different feature selection methods, including Recursive Feature Elimination (RFE), Minimum Redundancy - Maximum Relevance (MRMR), and k-best, in identifying relevant features from segmented glioma for accurate classification. To achieve this, a deep learning approach that combines Convolutional Neural Networks (CNN) based on the hybridization of U-Net and SegNet is investigated in this study. The evaluation of the proposed approach involves training and testing machine learning models, including Linear Regression, Random Forest and XGBoost, using the selected features from each feature selection technique. The obtained results show that XGBoost with k-best feature selection achieves the highest accuracy and Area Under the Curve (AUC) for distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). This indicates the effectiveness of the k-best feature selection method in capturing the most discriminative information for glioma grade classification. To the best of our knowledge, this is the first study to incorporate machine learning with RFE, MRMR, and k-best feature selection methods for predicting glioma grade. The proposed approach demonstrates improved accuracy compared to existing methods, highlighting the potential of radiomics and machine learning in glioma classification. 

Segmented Glioma Classification Using Radiomics-Based Machine Learning: A Comparative Analysis of Feature Selection Techniques

: Low-Grade Gliomas (LGG) are the most common malignant brain tumors that greatly deﬁne the rate of survival of patients. LGG segmentation across Magnetic Resonance Imaging (MRI) is common and necessary for diagnosis and treatment planning. To achieve this challenging clinical need, a deep learning approach that combines Convolutional Neural Networks (CNN) based on the hybridization of U-Net and SegNet is developed in this study. In fact, an adopted SegNet model was established in order to compare it with the most used model U-Net. The segmentation uses FLuid Attenuated Inversion Recovery (FLAIR) of 110 patients of LGG for training and evaluations. The highest mean and median Dice Coefﬁcient (DC) achieved by the hybrid model is 83% and 85 . 7%, respectively. The obtained results of this work lead to the potential of using deep learning in MRI images in order to provide a non-invasive tool for automated LGG segmentation for many relevant clinical applications.

Brain Tumor Segmentation of Lower-Grade Glioma Across MRI Images Using Hybrid Convolutional Neural Networks

Biportal spinal endoscopy is a safe and cost-effective methodology for the management of lumbar pathology in rural underserved hospitals that have standard orthopaedic arthroscopic equipment, but lack access to microscopy. 

Global utilization of biportal spinal endoscopy: Case series on management of lumbar pathology in Soddo, Ethiopia

Abstract Endoscopic spine surgery (ESS) is an innovative technique allowing for minimally invasive, direct visualization of spinal abnormalities. The growth of ESS in the United States has been stunted by high start-up costs, low reimbursement rates, and the steep learning curve associated with mastering endoscopic techniques. Hergrae, we describe the current state and future direction of ESS and provide key action items for ESS program implementation.

Development of an Endoscopic Spine Surgery Program

Gliomas, which originate from glial cells, are considered the most aggressive type of brain tumors. Currently, glioma research efforts are focused mainly on high-grade gliomas. This project aims to analyze lower-grade gliomas (LGG) in MRI and extend the understanding of LGGs. LGG segmentation, which outlines tumors in MRIs, is crucial to developing effective treatment plans. However, segmentation performed manually by radiologists is tedious, time-consuming, and often leads to inter-observer variability. Another unexplored area of LGG research is genomic subtypes. These subtypes can play a large factor in how LGGs can be treated, however there is currently no noninvasive method of identifying these subtypes. Recent studies suggest that LGG shape features have a correlation with genomic subtypes and should be investigated as a viable factor in LGG treatment options. This presents a need for additional research as most LGGs eventually develop into high-grade gliomas. The specific aims of this project include analyzing LGGs through deep learning-based segmentation, shape feature extraction, and statistical analysis to identify correlation between selected shape features and genomic subtypes. To realize these goals, programs were written and run using a publicly available LGG dataset. In terms of automatic segmentation, two models were created using different convolutional neural networks (CNN). The highest performing model used U-Net with a ResNeXt-50 backbone and yielded a 91.4% accuracy in terms of mean intersection over union (IoU) after testing. Shape feature extraction included three geometric features and 4 radiomic features which quantified tumor shape in 2D and 3D. Angular Standard Deviation (ASD), Margin Fluctuation (MF), Bounding Ellipsoid Volume Ratio (BEVR), Elongation, Major and Minor Axis Length, and Volume were tested for correlation with genomic subtype data using 49 Pearson’s chi-squared tests. P-values less than or equal to 0.05 suggested correlation. Statistical analysis found 16 statistically significant associations. The strongest associations were between MF and the RNASeq cluster (p < 0.00003), ASD and the RNASeq Cluster (p < 0.0005), and volume and the RPPA cluster (p < 0.0035).

Analysis of Lower-Grade Gliomas in MRI Through Segmentation and Genomic Cluster-Shape Feature Correlation

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Development of an Endoscopic Spine Surgery Program

Analysis of Lower-Grade Gliomas in MRI Through Segmentation and Genomic Cluster-Shape Feature Correlation