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Everything you need to know about Computed Tomography (CT) & CT Scanning

Trauma: Deep Learning and Ai Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Trauma ❯ Deep Learning and AI

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  • “This study showed that a deep learning model can be trained to detect wrist fractures in radiographs with diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. Additionally, this study showed that, when emergency medicine clinicians are provided with the assistance of the trained model, their ability to detect wrist fractures can be significantly improved, thus diminishing diagnostic errors and also improving the clinicians’ efficiency."
    Deep neural network improves fracture detection by clinicians
    Lindsey R et al.
    PNAS | November 6, 2018 | vol. 115 | no. 45 | 11591–11596
  • “The approach of this investigation is to apply machine learning algorithms trained by experts in the field to less experienced clinicians (who are at particular risk for diagnostic errors yet responsible for primary patient care and triage) to improve both their performance and efficiency. The learning model presented in this study mitigates these factors.”
    Deep neural network improves fracture detection by clinicians
    Lindsey R et al.
    PNAS | November 6, 2018 | vol. 115 | no. 45 | 11591–11596
  • “This study shows that deep learning models offer potential for subspecialized clinicians (without machine learning experience) to teach computers how to emulate their diagnostic expertise and thereby help patients on a global scale. Although teaching the model is a laborious process requiring collecting thousands of radiographs and carefully labeling them, making a prediction using the trained model takes less than a second on a modern computer.”
    Deep neural network improves fracture detection by clinicians
    Lindsey R et al.
    PNAS | November 6, 2018 | vol. 115 | no. 45 | 11591–11596
  • “ 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 appli- cation to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applica- tions. 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.”
    Deep neural network improves fracture detection by clinicians
    Robert Lindsey et al.
    Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596
  • In this work, we developed a deep neural network to detect and localize fractures in radiographs. We trained it to accurately emulate the expertise of 18 senior sub- specialized orthopedic surgeons by having them annotate 135,409 radiographs. We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model. The average clinician’s sensitivity was 80.8% (95% CI, 76.7–84.1%) unaided and 91.5% (95% CI, 89.3–92.9%) aided, and specificity was 87.5% (95 CI, 85.3–89.5%) unaided and 93.9% (95% CI, 92.9–94.9%) aided. The average clinician experienced a relative reduction in misinterpretation rate of 47.0% (95% CI, 37.4– 53.9%).
    Deep neural network improves fracture detection by clinicians
    Robert Lindsey et al.
    Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596
  • “The significant improvements in diagnostic accuracy that we observed in this study show that deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care.”
    Deep neural network improves fracture detection by clinicians
    Robert Lindsey et al.
    Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596
  • Misinterpretation of radiographs may have grave consequences, resulting in complications including malunion with restricted range of motion, posttraumatic osteoarthritis, and joint collapse, the latter of which may require joint replacement. Misdiagnoses are also the primary cause of malpractice claims or litigation. There are multiple factors that can contribute to radiographic misinterpretations of fractures by clinicians, including physician fatigue, lack of subspecialized expertise, and inconsistency among reading physicians.
    Deep neural network improves fracture detection by clinicians
    Robert Lindsey et al.
    Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596
  • “The approach of this investigation is to apply machine learning algorithms trained by experts in the field to less experienced clinicians (who are at particular risk for diagnostic errors yet responsible for primary patient care and triage) to improve both their performance and efficiency. The learning model presented in this study mitigates these factors. It does not become fatigued, it always provides a consistent read, and it gains subspecialized expertise by being provided with labeled radiographs from human experts.”
    Deep neural network improves fracture detection by clinicians
    Robert Lindsey et al.
    Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596
  • Thus, we speculate that, someday, technology may permit any patient whose clinician has computer access to receive the same high-quality radiographic interpretations as those received by the patients of senior subspecialized experts.
    Deep neural network improves fracture detection by clinicians
    Robert Lindsey et al.
    Proc Natl Acad Sci U S A. 2018 Nov 6;115(45):11591-11596
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