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X-ray Imaging01:24

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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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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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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method.

Minji Kang1, Tai Joon An2, Deokjae Han3

  • 1School of Industrial and Management Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Korea.

Scientific Reports
|November 9, 2022
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Summary
This summary is machine-generated.

A new multipotent computer-aided diagnosis (CAD) model for chest X-rays was developed using CT scan data. This advanced CAD system can detect multiple lung conditions, improving diagnostic accuracy in primary care settings.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Pulmonology

Background:

  • Computer-aided diagnosis (CAD) for chest X-rays has existed for over 50 years but lacks versatility.
  • Current CAD systems have unmet needs for broad clinical application, especially in primary care.

Purpose of the Study:

  • To develop a versatile, multipotent CAD model for general use, including primary care.
  • To enhance chest X-ray analysis by leveraging computed tomography (CT) scan data for improved lesion detection.

Main Methods:

  • Utilized a one-to-one matched chest X-ray and CT scan dataset, with data preprocessed by pulmonology experts.
  • Employed Faster R-CNN and RetinaNet for multi-object detection to identify twelve diagnostic labels.
  • Compared the performance of Faster R-CNN and RetinaNet for lesion detection.

Main Results:

  • Faster R-CNN demonstrated higher overall sensitivity, while RetinaNet exhibited higher specificity.
  • Achieved excellent sensitivity (100.0%) for detecting cardiomegaly and chemo-port.
  • Identified unique findings for bronchial wall thickening, reticular opacity, and pleural thickening.

Conclusions:

  • This study presents the first object detection model for chest X-rays based on CT-defined chest areas, curated by pulmonology experts.
  • The developed model shows potential as a tool for comprehensive multi-diagnosis from standard chest X-rays.
  • The model's ability to detect multiple conditions from simple X-rays could significantly benefit routine clinical practice.