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Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.

European journal of clinical nutrition
Currently available anthropometric body composition prediction equations were often developed on small participant samples, included only several measured predictor variables, or were prepared using conventional statistical regression methods. Machin...

Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration.

Acta ophthalmologica
PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.

Keyhole Fenestration for Cerebrospinal Fluid Leaks in the Thoracic Spine: Quantification of Bone Removal and Microsurgical Anatomy.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVE: A safe working trajectory is mandatory for spinal pathologies, especially in the midline, anterior to the spinal cord. For thoracic cerebrospinal fluid (CSF) leaks, we developed a minimally invasive keyhole fenestration. Thi...

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

Biological psychiatry
BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathologic...

Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy ...

AI-powered real-time annotations during urologic surgery: The future of training and quality metrics.

Urologic oncology
INTRODUCTION AND OBJECTIVE: Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for s...

Review of the targeting accuracy of frameless and frame-based robot-assisted deep brain stimulation electrode implantation in pediatric patients using the Neurolocate module.

Journal of neurosurgery. Pediatrics
OBJECTIVE: The Neurolocate module is a 3D frameless patient registration module that is designed for use with the Neuromate stereotactic robot. Long-term electrical stimulation of the globus pallidus internus (GPi) and subthalamic nucleus (STN) via d...

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

Cancer medicine
BACKGROUND: Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the...

A Short-Duration Gonadotropin-Releasing Hormone Stimulation Test for the Diagnosis of Central Precocious Puberty.

Medicina (Kaunas, Lithuania)
: The gonadotropin-releasing hormone (GnRH) stimulation test is the gold standard method for diagnosing central precocious puberty (CPP), although it requires multiple blood samplings over 120 min. This study aimed to evaluate if a shorter test may h...

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

European radiology
OBJECTIVES: To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.