APPLICATION OF ARTIFICIAL INTELLIGENCE CALIBRATION FRAMEWORK TO EVALUATE BRAIN TISSUE CHARACTERIZATION STUDY

The application of artificial intelligence (AI) in medical research has revolutionized the field of brain tissue characterization. Accurate classification and characterization of brain tissue types are crucial for diagnosing and understanding neurological disorders. AI models used in brain tissue characterization studies require proper calibration to ensure reliable and consistent results. In AI calibration framework specifically designed for brain tissue characterization. The framework incorporates various techniques, including data pre-processing, model optimization, and performance evaluation, to enhance the accuracy and reliability of AI models in classifying brain tissue types. pre-processing the brain tissue data to remove noise and artifacts, ensuring the input data is clean and representative. Next, a suitable AI model architecture is selected, and a training process is conducted using labelled brain tissue images. During the training phase, optimization techniques such as transfer learning or fine-tuning are employed to improve the model's performance. After the AI model is trained, the framework evaluates its performance using a comprehensive set of metrics. These metrics include accuracy, precision, recall, and F1-score, which provide a holistic understanding of the model's classification capabilities framework assesses the model's robustness by conducting cross-validation and sensitivity analyses. The proposed AI calibration framework was applied to a brain tissue characterization study using a dataset of magnetic resonance imaging (MRI) scans. The results demonstrated that the framework improved the accuracy and reliability of the AI model in classifying different brain tissue types. The interpretability analysis provided valuable insights into the model's decision-making process, facilitating the identification of potential limitations and areas for improvement. In the application of the AI calibration framework presented in this study offers a robust and reliable approach for brain tissue characterization. The framework enhances the accuracy and interpretability of AI models, enabling better understanding and diagnosis of neurological disorders. Future research can focus on expanding the framework to include multi-modal imaging data and validating its effectiveness across diverse patient populations.