Statistical Analysis of Seizure Data to Support Clinical Proceedings
Vajiram Jayanthi, Sivakumar Shanmugasundaram, Syed Khasim · Apple Academic Press eBooks · 2024
Radiographic imaging is highly valued and widely utilized in the field of oncology. It plays a crucial role in assisting clinicians with diagnostic, treatment planning, and procedural decision-making tasks. Artificial intelligence (AI) technology is employed to analyze radiographic images by quantifying various characteristics through predetermined algorithms. Strategies for managing clinical imaging data involve normalization techniques, robust modelling approaches, and statistical analyses. These methods aim to improve the quality and accuracy of medical image analysis, thereby enhancing the precision of clinical outcomes. Statistical models provide a customized framework for addressing specific surgical concerns by training on a dataset comprising cases with corresponding outcomes. Such models are particularly useful for image recovery tasks, assessing intensity, graph curves, and global shapes to guide clinical recommendations toward the most likely outcome. 2 Visual interpretation by the medical team is currently the prevailing method for analyzing clinical images, heavily reliant on subjective interpretations. Therefore, automated analysis of these images using computer algorithms offers a more reliable option to ensure a highly precise analysis. This chapter focuses on the statistical analysis of seizure data to support clinical decision-making processes.