As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with
tremendous potential for advancing research in this field. To provide a comprehensive …

Clinical prediction models for spine surgery applications are on the rise, with an increasing
reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes …

Background Low back pain is a widely prevalent symptom and the foremost cause of
disability on a global scale. Although various degenerative imaging findings observed on …

Purpose A common spine surgery procedure involves decompression of the lumbar spine.
The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well …

The pathogenesis of ankylosing spondylitis (AS) remains unclear, and while recent studies
have implicated necroptosis in various autoimmune diseases, an investigation of its …

Abstract Machine learning algorithms have the potential to significantly improve patient
safety in spine surgeries by providing healthcare professionals with valuable insights and …

Objective Frailty metrics estimate a patient's ability to tolerate physiologic stress and there
are limited frailty data in patients undergoing expanded endonasal approaches (EEA) for …

Neonatal medical data holds critical information within the healthcare industry, and it is
important to analyze this data effectively. Machine learning algorithms offer powerful tools for …

Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the
assessment of various spinal pathologies, including degenerative disc disease, spinal …

Predicting hospital length of stay is critical for efficient hospital management, enabling
proactive resource allocation, the optimization of bed availability, and optimal patient care …