Differentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective Review of the Novel Diagnostic Modalities DOI Open Access
Kaivan Patel,

Harshal Sanghvi,

Gurnoor S Gill

и другие.

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

Differentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective Review of the Novel Diagnostic Modalities DOI Open Access
Kaivan Patel,

Harshal Sanghvi,

Gurnoor S Gill

и другие.

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

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