Background Approximately 1 in 33 newborns is affected by congenital anomalies worldwide.
We aimed to develop a practical model for identifying infants with a high risk of congenital …

This paper is based on an improved three-dimensional U-net convolutional neural network
deep learning algorithm for heart coronary artery segmentation for disease risk prediction …

Purpose: The purpose of this study was to establish a deep learning model for automated
sub-basal corneal nerve fiber (CNF) segmentation and evaluation with in vivo confocal …

Purpose Management of vestibular schwannoma (VS) is based on tumour size as observed
on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the …

The use of postmortem computed tomography in forensic medicine, in addition to
conventional autopsy, is now a standard procedure in several countries. However, the large …

Advances in deep learning have led to the development of neural network algorithms which
today rival human performance in vision tasks, such as image classification or segmentation …

Fueled by new techniques, computational tools, and broader availability of imaging data,
artificial intelligence has the potential to transform the practice of neuroradiology. The recent …

In intracranial hemorrhage treatment patient mortality depends on prompt diagnosis based
on a radiologist's assessment of CT scans. In this paper, we investigate the intracranial …

Interpretability has become an essential topic as deep learning is widely applied in
professional fields (eg, medical image processing) where high level of accountability is …

Objectives: To develop an accurate machine learning (ML) predictive model incorporating
patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) …