Robotic-assisted minimally invasive surgery (RA-MIS) is becoming increasingly applied to spinal surgery, offering an alternative to conventional minimally invasive spinal surgery (C-MIS). Recently, RA-MIS has been proposed for pedicle screw instrumentation in thoracolumbar fractures. This review aims to evaluate screw accuracy, RA-MIS and C-MIS among the thoracolumbar fracture population. This study followed PRISMA and Cochrane guidelines. Five medical databases were searched. Finally, 10 studies were included in the meta-analysis, with a total of 562 patients. Outcomes on operation time, blood loss, pedicle screw accuracy, complications, and cost-effectiveness were evaluated. RA-MIS showed superiority in terms of accurate (GR A) screw placement (OR: 3.58, 95% CI: 2.30-5.58, p<.001) and satisfactory (GR A+B) screw placement (OR: 3.86, 95% CI: 2.14-6.96, p<.001) compared to C-MIS. RA-MIS led to a significantly lower incidence of severely misplaced (GR D+E) screws (OR: 0.26, 95% CI: 0.13-0.51, p<.001) and lower blood loss (MD: -12.03, 95% CI: -20.85 to -3.21, p<.01). However, we found no statistically significant differences in operation time (MD: -4.92, 95% CI: -12.06 to 2.22, p=.18) or complications (screw loosening, nerve injury, or infection). Costs of RA-MIS were significantly higher (MD: ¥8657.61, 95% CI: 6651.39 to 10663.83, p<.01). Robotic-assisted minimally invasive spinal surgery is superior in terms of higher pedicle screw accuracy and reduced perioperative blood loss, at the cost of significantly higher costs of surgery. Furthermore, we observed no clear advantages regarding complication rate and total operation time. Future trials should provide a less biased methodology to confirm the findings.

Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance. Our study aims to assess the diagnostic accuracy of ML in identifying myocarditis using CMR. A systematic search was performed using PubMed, Embase, Web of Science, Cochrane, and Scopus to identify studies reporting the diagnostic accuracy of ML in the detection of myocarditis using CMR. The included studies evaluated both image-based and report-based assessments using various ML models. Diagnostic accuracy was estimated using a Random-Effects model (R software). We found a total of 141 ML model results from a total of 12 studies, which were included in the systematic review. The best models achieved 0.93 (95% Confidence Interval (CI) 0.88–0.96) sensitivity and 0.95 (95% CI 0.89–0.97) specificity. Pooled area under the curve was 0.97 (95% CI 0.93–0.98). Comparisons with human physicians showed comparable results for diagnostic accuracy of myocarditis. Quality assessment concerns and heterogeneity were present. CMR augmented using ML models with advanced algorithms can provide high diagnostic accuracy for myocarditis, even surpassing novice CMR radiologists. However, high heterogeneity, quality assessment concerns, and lack of information on cost-effectiveness may limit the clinical implementation of ML. Future investigations should explore cost-effectiveness and minimize biases in their methodologies.

• Meta-analysis of 207 patients supports RNS efficacy in refractory MTLE; RNS therapy may reduce seizure frequency in patients with refractory MTLE; Some patients with MTLE achieved seizure freedom within 6 months with RNS.