Cureus, Год журнала: 2024, Номер unknown
Опубликована: Дек. 10, 2024
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance differentiation cystic lesions in sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) craniopharyngiomas (CP), through use advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, ensemble methods, can overcome limitations traditional diagnostic approaches, providing more accurate early these lesions. review incorporates findings from critical studies, using Open Access Series Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting significance statistical rigor automated segmentation developing reliable AI models. By drawing on insights addressing challenges posed by small, single-institutional datasets, aims demonstrate applications improve precision, clinical decision-making, ultimately lead better patient outcomes managing region
Язык: Английский