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Artificial intelligence for point of care radiograph quality assessment
Author(s): Satyananda Kashyap; Mehdi Moradi; Alexandros Karargyris; Joy T. Wu; Michael Morris; Babak Saboury; Eliot Siegel; Tanveer Syeda-Mahmood
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Paper Abstract

Chest X-rays are among the most common modalities in medical imaging. Technical flaws of these images, such as over- or under-exposure or wrong positioning of the patients can result in a decision to reject and repeat the scan. We propose an automatic method to detect images that are not suitable for diagnostic study. If deployed at the point of image acquisition, such a system can warn the technician, so the repeat image is acquired without the need to bring the patient back to the scanner. We use a deep neural network trained on a dataset of 3487 images labeled by two experienced radiologists to classify the images as diagnostic or non-diagnostic. The DenseNet121 architecture is used for this classification task. The trained network has an area under the receiver operator curve (AUC) of 0.93. By removing the X-rays with diagnostic quality issues, this technology could potentially provide significant cost savings for hospitals.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503K (13 March 2019); doi: 10.1117/12.2513092
Show Author Affiliations
Satyananda Kashyap, IBM Research - Almaden (United States)
Mehdi Moradi, IBM Research - Almaden (United States)
Alexandros Karargyris, IBM Research - Almaden (United States)
Joy T. Wu, IBM Research - Almaden (United States)
Michael Morris, IBM Research - Almaden (United States)
Babak Saboury, IBM Research - Almaden (United States)
Eliot Siegel, Univ. of Maryland (United States)
Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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