Deep learning (DL) is a powerful machine learning technique that has increasingly been
used to predict surgical outcomes. However, the large quantity of data required and lack of …

An inherent problem in software engineering is that competing prediction systems have
been found to produce conflicting results. Yet accurate prediction is crucial because the …

Introduction Machine learning algorithms have received increased attention in neurosurgical
literature for improved accuracy over traditional predictive methods. In this review, the …

Background: Primary headaches are widespread and costly public health problems.
However, there are insufficient medical resources for their treatment in Japan due to two …

Background: Reliable prediction models of intracerebral hemorrhage (ICH) outcomes are
needed for decision-making of the treatment. Statistically making such prediction models …

Aim Accurate preoperative diagnosis of lateral lymph node metastasis (LLNM) from lower
rectal cancer is important to identify patients who require lateral lymph node dissection …

Background: Previous studies found that Asians seemed to have higher risk of HT after
thrombolysis than Caucasians due to its race differences in genetic polymorphism. Whether …

Methods We used an open dataset of 298 SAH patients, who were with non-severe
neurological grade and treated by coiling. Modified Rankin Scale 0-3 at six months was …

BACKGROUND: Interest in machine learning (ML)–based predictive modeling has led to the
development of models predicting outcomes after aneurysmal subarachnoid hemorrhage …

Abstract Introduction The diagnosis of Alzheimer's disease (AD) is sometimes difficult for
nonspecialists, resulting in misdiagnosis. A missed diagnosis can lead to improper …