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

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.

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Publication Details

Journal
Surgical Neurology International
Published
2025-05-09
DOI
https://doi.org/10.25259/sni_303_2025
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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article

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

Álvaro Campero, Mickaela Echavarría Demichelis, Matías Baldoncini, Luis Villar
1 citations
Surgical Neurology International
Radiomics and Machine Learning in Medical Imaging
1.36
article

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

Álvaro Campero, Mickaela Echavarría Demichelis, Matías Baldoncini, Luis Villar
article en
1 citations

Abstract

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.

Surgical Neurology InternationalVol. 16
National University of Tucumán (AR), Universidad de Buenos Aires (AR)
Openalex Percentile: Top 20%
Radiomics and Machine Learning in Medical Imaging
1.36
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