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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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Related Experiment Video

Updated: Mar 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

Alvin Rajkomar1,2, Sneha Lingam3, Andrew G Taylor4

  • 1Department of Medicine, Division of Hospital Medicine, University of California, San Francisco, 533 Parnassus Ave., Suite 127a, San Francisco, CA, 94143-0131, USA. Alvin.rajkomar@ucsf.edu.

Journal of Digital Imaging
|October 13, 2016
PubMed
Summary

Deep learning computer vision accurately classifies chest radiograph views using augmented data. This rapid, high-fidelity method aids in high-throughput annotation of medical images.

Keywords:
Artificial neural networksChest radiographsComputer visionConvolutional neural networkDeep learningMachine learningRadiography

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Area of Science:

  • Radiology
  • Computer Vision
  • Deep Learning

Background:

  • Accurate classification of chest radiograph views is crucial for diagnosis.
  • Manual annotation can be time-consuming and prone to error.

Purpose of the Study:

  • To evaluate the efficacy of deep learning-based computer vision for classifying chest radiograph views.
  • To determine if a model pre-trained on non-radiology images can achieve high-fidelity classification on radiographs.

Main Methods:

  • A deep convolutional neural network (GoogLeNet) was pre-trained on ImageNet images and fine-tuned on augmented chest radiographs.
  • 1,885 chest radiographs were annotated and divided into training, validation, and test sets.
  • The Youden Index was used to assess classification accuracy for frontal or lateral views.

Main Results:

  • The fine-tuned model achieved 100% accuracy (95% CI 99.73-100%) in classifying chest radiograph views on both institutional and public test sets.
  • The classification process was rapid, processing 38 images per second.
  • The model demonstrated high fidelity in clinically relevant image classification.

Conclusions:

  • Deep learning computer vision models, pre-trained on non-radiology data and fine-tuned on augmented radiographs, are effective for accurate chest radiograph view classification.
  • This approach offers a feasible and rapid method for high-throughput medical image annotation.
  • The study highlights the potential of AI in improving radiograph analysis efficiency and accuracy.