Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

Importance Pulmonary nodules are identified in approximately 1.6 million patients per year
in the US and are detected on approximately 30% of computed tomographic (CT) images of …

Artificial intelligence (AI) has been a very active research topic over the last years and
thoracic imaging has particularly benefited from the development of AI and in particular deep …

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer
screening has led to the increasing detection of pulmonary nodules. However, precisely …

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis
and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly …

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is
crucial for optimizing management in lung cancer screening. Purpose To develop and …

Abstract Introduction Deep Learning has been proposed as promising tool to classify
malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction …

Identification of novel non‐invasive biomarkers is critical for the early diagnosis of lung
adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule …

Artificial Intelligence-supported digital applications (AI applications) are expected to
transform radiology. However, providers need the motivation and incentives to adopt these …