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

Background Machine learning (ML) has garnered increasing attention as a means to
quantitatively analyze the growing and complex medical data to improve individualized …

Background Machine learning (ML) is a branch of artificial intelligence that allows computers
to learn from large complex datasets without being explicitly programmed. Although ML is …

BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows
computer algorithms to learn from experience without being explicitly programmed …

In recent years, there has been a significant surge in discussions surrounding artificial
intelligence (AI), along with a corresponding increase in its practical applications in various …

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

Current practice of neurosurgery depends on clinical practice guidelines and evidence-
based research publications that derive results using statistical methods. However, statistical …

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

BACKGROUND Current outcomes prediction tools are largely based on and limited by
regression methods. Utilization of machine learning (ML) methods that can handle multiple …

Introduction Being able to predict functional outcomes after a stroke is highly desirable for
clinicians. This allows clinicians to set reasonable goals with patients and relatives, and to …