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| http://dx.doi.org/10.1097/JS9.0000000000001962 | DOI Listing |
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Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders.
Rev Neurosci
January 2025
557765 Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN ), Tehran, Iran.
The recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring of neurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss the potential of AI techniques, including deep learning and computer vision, to accurately interpret and categorize subtle changes in facial expressions associated with these pathological conditions.
View Article and Find Full Text PDFUsing Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.
Korean J Neurotrauma
December 2024
Department of Neurosurgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea.
Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management.
View Article and Find Full Text PDFTowards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges.
Int J Med Inform
December 2024
Neurosurgery Department, Hamad General Hospital, Qatar; Department of Clinical Academic Sciences, College of Medicine, Qatar University, Doha, Qatar; Department of Neurological Sciences, Weill Cornell Medicine, Doha, Qatar.
Introduction: Artificial Intelligence is in the phase of health care, with transformative innovations in diagnostics, personalized treatment, and operational efficiency. While having potential, critical challenges are apparent in areas of safety, trust, security, and ethical governance. The development of these challenges is important for promoting the responsible adoption of AI technologies into healthcare systems.
View Article and Find Full Text PDFAutomated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.
Neurooncol Adv
December 2024
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Background: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.
View Article and Find Full Text PDFArtificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.
Adv Exp Med Biol
November 2024
Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures.
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