Objective Accurate measurement of surgical outcomes is highly desirable to optimize
surgical decision-making. An important element of surgical decision making is identification …

Abstract Machine learning (ML) involves algorithms learning patterns in large, complex
datasets to predict and classify. Algorithms include neural networks (NN), logistic regression …

Radiation outcomes prediction (ROP) plays an important role in personalized prescription
and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation …

Purpose To validate a machine learning approach to Virtual intensity‐modulated radiation
therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using …

Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in
progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must …

Introduction Machine learning has been increasingly used to develop predictive models to
diagnose different disease conditions. The heterogeneity of the kidney transplant population …

Background Recent technological advances have led to the development and
implementation of machine learning (ML) in various disciplines, including neurosurgery. Our …

Stroke is a leading cause of long-term disability, and outcome is directly related to timely
intervention. Not all patients benefit from rapid intervention, however. Thus a significant …

Purpose The purpose of this study was to compare the performance of Deep Neural
Networks against a technique designed by domain experts in the prediction of gamma …

Freezing of gait is a devastating symptom of Parkinson's disease and other forms of
parkinsonism. It poses a major burden on both patients and their families, as freezing often …