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Radiological Investigation I: X-ray and CT01:30

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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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Related Experiment Video

Updated: Sep 21, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

Yan Zhuang1,2, Md Fashiar Rahman3, Yuxin Wen4

  • 1Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX, USA.

Journal of X-Ray Science and Technology
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning system accurately diagnoses COVID-19 from chest X-rays, offering a fast, reliable alternative to lab tests. This interpretable AI tool aids early detection and complements clinical evaluation.

Keywords:
COVID-19chest X-rayclinical applicabilitylung detectionmulti-task systemtransfer learning

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Deep Learning for Disease Diagnosis
  • Radiology and Pulmonary Medicine

Background:

  • The emergence of mutating coronavirus variants necessitates rapid and accessible diagnostic tools.
  • Traditional laboratory testing for COVID-19 is time-consuming and requires specialized personnel.
  • Chest X-ray (CXR) imaging presents a viable alternative for timely COVID-19 diagnosis.

Purpose of the Study:

  • To develop an interpretable multi-task deep learning system for automatic lung detection and COVID-19 screening using chest X-rays.
  • To provide a reliable, fast, and accessible diagnostic method correlated with radiological findings.
  • To generate interpretable predictions for enhanced trust in medical AI systems.

Main Methods:

  • Implementation of an unsupervised machine learning algorithm for lung region detection.
  • Utilization of a truncated convolutional neural network (CNN) model based on deep transfer learning (DTL).
  • Application of Grad-CAM technique for generating class-specific heatmap images to ensure interpretability.

Main Results:

  • The system achieved 91.94% accuracy on a test dataset of 2,680 chest X-ray images.
  • Demonstrated high sensitivity (94.48%) for COVID-19 cases and good specificity (88.46%) for normal cases.
  • Produced state-of-the-art results on public and clinical datasets for binary COVID-19 classification.

Conclusions:

  • The automated system's performance in grading lung infections is comparable to radiologist interpretation.
  • The proposed solution can serve as a valuable tool in patient evaluation alongside standard clinical and laboratory tests.
  • This AI-driven approach offers a promising advancement in early COVID-19 detection and management.