OBJECTIVE. Although imaging technology has advanced significantly since the work of
Garland in 1949, interpretive error rates remain unchanged. In addition to patient harm …

Artificial intelligence (AI) applications for chest radiography and chest CT are among the
most developed applications in radiology. More than 40 certified AI products are available …

Purpose To develop and validate a deep learning–based automatic detection algorithm
(DLAD) for malignant pulmonary nodules on chest radiographs and to compare its …

Importance Interpretation of chest radiographs is a challenging task prone to errors,
requiring expert readers. An automated system that can accurately classify chest …

The study aimed to determine if computer vision techniques rooted in deep learning can use
a small set of radiographs to perform clinically relevant image classification with high fidelity …

Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early
diagnosis and treatment of lung nodules can significantly improve the quality of patients' …

Objective To investigate the feasibility of a deep learning–based detection (DLD) system for
multiclass lesions on chest radiograph, in comparison with observers. Methods A total of …

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic
radiology, are under active investigation with deep learning technology, which has shown …

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its
well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR …

In the present article, we provide an overview on the basics of deep learning in terms of
technical aspects and steps required to launch a deep learning research. Deep learning is a …