Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine learning (ML) offers a novel approach, leveraging advanced algorithms to analyze complex imaging data with high precision. A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Google Scholar identified eligible studies. The sensitivity, specificity, accuracy, precision, F1 score, and the (area under the curve) AUC items were extracted from the included studies. After screening 1077 records, seven studies met the eligibility criteria for the systematic review, of which five were included in the meta-analysis. ML algorithm, particularly Support Vector Machines (SVM), demonstrated promising performance. A meta-analysis of five studies revealed a pooled sensitivity of 0.95 (95% CI: 0.84-0.99) and specificity of 0.80 (95% CI: 0.69-0.88). Additionally, the positive diagnostic likelihood ratio (DLR) was 4.75 (95% CI: 2.91-7.76), the negative DLR was 0.06 (95% CI: 0.02-0.21), and the diagnostic odds ratio was 80.97 (95% CI: 17.5-374.61). The diagnostic score was calculated as 4.39 (95% CI: 2.86-5.93), and the AUC was 0.86 (95% CI: 0.83-0.89). Subgroup analyses showed SVM-based models with higher sensitivity (0.98 vs. 0.87) and specificity (0.82 vs. 0.77) than non-SVM (p = 0.13). Comparing glioblastoma and Grade 3 tumors, sensitivities were 94 % vs. 97 %, and specificities were 79 % vs. 83 %, with no significant heterogeneity. These findings suggest that ML models, particularly SVM, offer promising diagnostic performance in distinguishing true tumor recurrence from treatment-related changes.

Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools. Artificial intelligence (AI) holds promise in enhancing outcome predictions for congenital cardiac surgery. This study aims to systematically review the utilization of AI in predicting post-operative outcomes in this population. Following PRISMA guidelines, a comprehensive search of Pubmed, Scopus, and Web of Science databases was conducted. Two independent reviewers screened articles based on predefined criteria. Included studies focused on AI models predicting various post-operative outcomes in congenital heart surgery. The review included 35 articles, primarily published within the last four years, indicating growing interest in AI applications. Models predominantly targeted mortality and survival (n = 16), prolonged length of hospital or ICU stay (n = 7), postoperative complications (n = 6), prolonged mechanical ventilatory support time (n = 4), with additional focus on specific outcomes such as peri-ventricular leucomalacia (n = 2) and malnutrition (n = 1). Performance metrics, such as area under the curve (AUC), ranged from 0.52 to 0.997. Notably, these AI models consistently outperformed traditional risk stratification categories. For instance, in assessing the risk of morbidity and mortality, the AI models demonstrated superior performance compared to conventional methods. AI-driven prediction models show significant promise in improving outcome predictions for congenital heart surgery. They surpass traditional risk prediction tools not only in immediate postoperative risks but also in long-term outcomes such as 1-year survival and malnutrition. Further studies with robust external validation are necessary to assess the practical applicability of these models in clinical settings. The protocol of this review was prospectively registered on PROSPERO (CRD42024550942).