Radiology: Artificial Intelligence

Editorial

Will Artificial Intelligence Replace Radiologists?   Artificial intelligence algorithms will not replace radiologists but will modify the work of radiologists.    Curtis P. Langlotz

Original Research

Detection and Classification of Myocardial Delayed Enhancement Patterns on MR Images with Deep Neural Networks: A Feasibility Study   Deep learning with deep neural networks can detect the presence of myocardial delayed enhancement (MDE) on cardiovascular MR images and may obtain the ability to classify MDE by training with a similar number of images to that required for physicians who perform or interpret cardiovascular MRI examinations as part of their practice.     Yasutoshi Ohta, Hiroto Yunaga... Toshihide Ogawa

Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance   A deep learning model showed improved overall sensitivity compared with manual identification of pulmonary nodules and was insensitive to radiation dose, patient age, or CT manufacturer; the model also enhanced manual review by increasing sensitivity and reducing reading time.     Kai Liu, Qiong Li... Shiyuan Liu

Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning   There was no statistically significant difference between the anterior cruciate ligament (ACL) tear detection system and clinical radiologists with varying levels of experience for determining the presence or absence of a full-thickness ACL tear using sagittal proton density–weighted and fat-suppressed T2-weighted fast spin-echo MR images.    Fang Liu, Bochen Guan... Richard Kijowski

Opinion

The Rebirth of CAD: How Is Modern AI Different from the CAD We Know?   The ability of deep learning to succeed across a range of perceptual tasks distinguishes the technology from traditional CAD.     Luke Oakden-Rayner

Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos
Crete.Greece.72100
2841026182

6948891480

alsfakia@gmail.com