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Radiological Investigation I: X-ray and CT

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

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Deep learning for chest X-ray analysis: A survey.

Erdi Çallı¹, Ecem Sogancioglu¹, Bram van Ginneken¹

¹Radboud University Medical Center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, the Netherlands.

Medical Image Analysis

|June 25, 2021

Summary

This summary is machine-generated.

Deep learning shows promise in analyzing chest X-rays for various medical tasks. This review covers studies using deep learning on chest radiographs before March 2021, detailing datasets and applications.

Keywords:

Chest X-ray analysis Chest radiograph Deep learning Survey

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Area of Science:

Medical imaging analysis
Artificial intelligence in radiology
Deep learning applications

Background:

Chest radiographs are the most common radiological exam.
Deep learning has advanced medical image analysis.
Publicly available chest X-ray datasets have spurred research.

Purpose of the Study:

To review deep learning studies on chest radiographs published before March 2021.
To categorize research by task (prediction, segmentation, localization, generation, adaptation).
To describe publicly available datasets and commercial systems.

Main Methods:

Systematic literature review of studies using deep learning on chest radiographs.
Categorization of studies based on specific medical imaging tasks.
Inclusion of descriptions for all publicly available datasets.

Main Results:

Deep learning has been applied to image-level prediction, segmentation, localization, image generation, and domain adaptation on chest radiographs.
Numerous public datasets are available, facilitating research.
Commercial systems are emerging in this domain.

Conclusions:

Deep learning is a rapidly advancing field in chest radiograph analysis.
Understanding current applications, datasets, and limitations is crucial.
Further research is needed to address clinical utility and literature gaps.