Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis.

Introduction: Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists. Research question: This review investigates the utilization of ML and DL in TLIs causing VFs. Materials and methods: Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis. Results: Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137). Conclusion: The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.

Augmented Reality Integration in Skull Base Neurosurgery: A Systematic Review.

Background and Objectives: To investigate the role of augmented reality (AR) in skull base (SB) neurosurgery. Materials and Methods: Utilizing PRISMA methodology, PubMed and Scopus databases were explored to extract data related to AR integration in SB surgery. Results: The majority of 19 included studies (42.1%) were conducted in the United States, with a focus on the last five years (77.8%). Categorization included phantom skull models (31.2%, n = 6), human cadavers (15.8%, n = 3), or human patients (52.6%, n = 10). Microscopic surgery was the predominant modality in 10 studies (52.6%). Of the 19 studies, surgical modality was specified in 18, with microscopic surgery being predominant (52.6%). Most studies used only CT as the data source (n = 9; 47.4%), and optical tracking was the prevalent tracking modality (n = 9; 47.3%). The Target Registration Error (TRE) spanned from 0.55 to 10.62 mm. Conclusion: Despite variations in Target Registration Error (TRE) values, the studies highlighted successful outcomes and minimal complications. Challenges, such as device practicality and data security, were acknowledged, but the application of low-cost AR devices suggests broader feasibility.