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This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models. By drawing on these insights and addressing the challenges posed by small, single-institutional datasets, the paper aims to demonstrate how AI applications can improve diagnostic precision, enhance clinical decision-making, and ultimately lead to better patient outcomes in managing sellar region cystic lesions. 

Background:Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning toexpedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical imagedata to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatmentof patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional NeuralNetwork (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. Methods:In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal colorfundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) therelative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model toserve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse diseasemanifestations. Results:We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted insignificantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract,glaucoma, diabetic retinopathy. Conclusion:The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.