Surgery

Neurosurgery

Latest AI and machine learning research in neurosurgery for healthcare professionals.

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Single-Port Robotic Removal of a Submucosal Foreign Body in the Distal Hypopharynx.

In this report, we present a 55-year-old female with cervical stenosis that underwent C5-C7 anterior...

Surgical Robotics for Intracerebral Hemorrhage Treatment: State of the Art and Future Directions.

Intracerebral hemorrhage (ICH) is a stroke subtype with high mortality and disability, and there are...

Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy.

BACKGROUND: With advances in science and technology, the application of artificial intelligence in m...

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investi...

Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis.

Multi-parametric MR image synthesis is an effective approach for several clinical applications where...

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance.

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enab...

The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying...

Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model.

OBJECTIVE: Interspinous motion (ISM) is a representative method for evaluating the functional fusion...

Neurosurgical skills analysis by machine learning models: systematic review.

Machine learning (ML) models are being actively used in modern medicine, including neurosurgery. Thi...

Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation.

Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurg...

Deployment of an electrocorticography system with a soft robotic actuator.

Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epile...

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

OBJECTIVE: The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains u...

Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution.

CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly ...

Predictors of Remission of Acromegaly following Surgical Treatment in Growth Hormone-Secreting Pituitary Adenoma.

 Surgery is the treatment of choice for growth hormone (GH)-secreting pituitary adenoma. The remiss...

The role of an open artificial intelligence platform in modern neurosurgical education: a preliminary study.

The use of artificial intelligence in neurosurgical education has been growing in recent times. Chat...

Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.

BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable...

Multi-mode information fusion navigation system for robot-assisted vascular interventional surgery.

BACKGROUND: Minimally invasive vascular intervention (MIVI) is a powerful technique for the treatmen...

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