Artificial Intelligence With Neural Network Algorithms in Pediatric Astrocytoma Diagnosis: A Systematic Review - Floresya K. Farmawati; Della W.A. Nurwakhid; Tifani A. Pradhea; Rayyan Fitriasa; Hutami H. Arrahmi; Muhana F. Ilyas; Fadhilah T. Nur (Инновационная медицина Кубани)
Background: Astrocytoma is a common pediatric brain tumor that poses a significant health burden. Recent advancements in artificial intelligence (AI), particularly neural network algorithms, have been studied for their precision and efficiency in medical diagnostics via effectively analyzing imaging data to identify patterns and anomalies.Objective: To systematically review AI-based diagnostic tools with neural network algorithms’ methodologies, sensitivities, specificities, and potential clinical integration for pediatric astrocytoma, providing a consolidated perspective on their overall performance and impact on clinical decision-making.Methods: As per PRISMA 2020 guidelines, we conducted a comprehensive search in PubMed, Scopus, and ScienceDirect on February 5, 2024. The search strategy was guided by a PECO question focusing on pediatric astrocytoma diagnosis using AI algorithms vs computed tomography or magnetic resonance imaging (MRI). Keywords were terms related to AI and neural network algorithms. We included studies analyzing the diagnostic accuracy of AI-based methods in cases of pediatric astrocytoma (World Health Organization grades 1-3), with no restrictions on a publication year or country. We excluded papers written in languages other than English or Bahasa Indonesia and nonhuman studies. Data was assessed using the Effective Public Health Practice Project tool.Results: Of 454 articles screened, 6 met inclusion criteria. These studies varied in design, location, and sample size, ranging from 10 to 135 subjects. The AI methods showed high sensitivity and specificity, often surpassing traditional radiological techniques. Notably, neural network algorithms using 3-dimensional MRI demonstrated improved accuracy compared with 2-dimensional MRI (96% vs 77%). The AI models exhibited performance levels comparable to or exceeding that of expert radiologists, with metrics such as tumor classification accuracy of 92% and high values of the area under the receiver operating characteristic curve.Conclusions: AI with neural network algorithms shows significant promise in enhancing accuracy of pediatric astrocytoma diagnosis. The studies reviewed indicate that these advanced methods can achieve superior sensitivity and specificity compared with conventional diagnostic techniques. Integrating AI into clinical practice could substantially improve diagnostic precision and patient outcomes.