Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …

Background and Aims Deep learning is an innovative algorithm based on neural networks.
Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small …

This paper aims to provide a review of the basis for application of AI in radiology, to discuss
the immediate ethical and professional impact in radiology, and to consider possible future …

Radiation therapy (RT) continues to be one of the mainstays of cancer treatment.
Considerable efforts have been recently devoted to integrating MRI into clinical RT planning …

Objectives The aim of the current study was to evaluate the detection and diagnosis of three
types of odontogenic cystic lesions (OCLs)—odontogenic keratocysts, dentigerous cysts …

The purpose of the paper was the assessment of the success of an artificial intelligence (AI)
algorithm formed on a deep-convolutional neural network (D-CNN) model for the …

Radiology historically has been a leader of digital transformation in healthcare. The
introduction of digital imaging systems, picture archiving and communication systems …

Artificial Neural Networks (ANN) forms the basis of most deep learning methods. ANNs are
loosely based on the hypothesis of how biological neural networks operate: data enters the …

Human cancers represent complex structures, which display substantial inter-and intratumor
heterogeneity in their genetic expression and phenotypic features. However, cancers …

Imaging and image-guided procedures play an imperative role in the screening, diagnosis,
and surveillance of cancer. Although emerging imaging techniques now enable more …