Development of an artificial intelligence-based convolutional neural network for sellar barrier classification using magnetic resonance imaging.

Background: This study aims to develop an artificial intelligence (AI) model using convolutional neural networks and transfer learning to classify sellar barriers as strong, mixed, or weak based on magnetic resonance imaging (MRI). Accurate classification is essential for surgical planning in endoscopic endonasal approaches for pituitary adenomas, as variations in the sellar barrier can lead to complications such as cerebrospinal fluid leaks. Methods: The dataset consisted of 600 MRI images of sellar barriers obtained from coronal sections and evenly distributed among the three classes. The EfficientNetB0 architecture was employed, leveraging transfer learning to optimize performance despite the small dataset. The model was implemented and trained on Google Colab using TensorFlow, with techniques such as dropout and batch normalization to improve generalization and reduce overfitting. Performance metrics included accuracy, recall, and F1-score. Results: The AI model achieved a classification accuracy of 96.33%, with individual class accuracies of 98% for strong barriers, 95% for mixed barriers, and 96% for weak barriers. These results demonstrate the model's high capacity to accurately classify sellar barriers and identify their specific characteristics, ensuring reliable preoperative assessment. Conclusion: The proposed AI model significantly enhances the preoperative classification of sellar barriers, contributing to improving surgical planning and reducing complications. While the "black box" nature of AI poses challenges, integrating explainability modules and expanding datasets can further increase clinical trust and applicability. This study underscores the transformative potential of AI in neurosurgical practice, paving the way for precise and reliable diagnostics in managing pituitary lesions.