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      Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography

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          Abstract

          Objective

          To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT.

          Materials and Methods

          We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455).

          Results

          All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement.

          Conclusion

          We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

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          Most cited references28

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          Deep Learning in Medical Imaging: General Overview

          The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.
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            Deep neural network improves fracture detection by clinicians

            Significance Historically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of deep learning approaches to CAD, there is great excitement about its application to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applications. We trained a deep learning model to detect fractures on radiographs with a diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. We demonstrate that when emergency medicine clinicians are provided with the assistance of the trained model, their ability to accurately detect fractures significantly improves.
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              Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

              To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs.
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                Author and article information

                Journal
                Korean J Radiol
                Korean J Radiol
                KJR
                Korean Journal of Radiology
                The Korean Society of Radiology
                1229-6929
                2005-8330
                July 2020
                05 June 2019
                : 21
                : 7
                : 891-899
                Affiliations
                Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
                Author notes
                Corresponding author: Thomas Weikert, MD, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland. Tel: (4161) 32 86576, Fax: (4161) 265 2020, thomas.weikert@ 123456usb.ch

                *Thomas Weikert and Luca Andre Noordtzij equally contributed to this work as co-first authors.

                Author information
                https://orcid.org/0000-0001-9274-053X
                https://orcid.org/0000-0002-2104-127X
                https://orcid.org/0000-0002-1002-8483
                https://orcid.org/0000-0002-5961-802X
                https://orcid.org/0000-0003-4802-5402
                https://orcid.org/0000-0002-4584-0623
                https://orcid.org/0000-0002-8952-0808
                https://orcid.org/0000-0002-6707-2258
                Article
                10.3348/kjr.2019.0653
                7289702
                32524789
                133d32a9-3128-4cc0-83f5-4b16991d4655
                Copyright © 2020 The Korean Society of Radiology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 September 2019
                : 12 February 2020
                : 19 February 2020
                Categories
                Thoracic Imaging
                Original Article

                Radiology & Imaging
                rib fractures,trauma,deep learning,computer-assisted image interpretation,computed tomography

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