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

Musculoskeletal: Deep Learning Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Musculoskeletal ❯ Deep Learning

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  • AI and MR of the Knee
  • Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard.
    Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
    Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Summary
    * There was no statistically significant difference between the anterior cruciate ligament (ACL) tear detection system and clinical radiologists with varying levels of experience for determining the presence or absence of a full-thickness ACL tear using sagittal proton density–weighted and fat-suppressed T2-weighted fast spin-echo MR images.
    Key Points
    * There was no significant difference between the diagnostic performance of a fully automated deep learning–based diagnosis system and clinical radiologists for detecting a full-thickness anterior cruciate ligament (ACL) tear at MRI.
    * Sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively; the sensitivity of the clinical radiologists ranged between 0.96 and 0.98 and specificity ranged between 0.90 and 0.98.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • Key Points
    * There was no significant difference between the diagnostic performance of a fully automated deep learning–based diagnosis system and clinical radiologists for detecting a full-thickness anterior cru- ciate ligament (ACL) tear at MRI.
    * Sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively; the sensitivity of the clinical radiologists ranged between 0.96 and 0.98 and specificity ranged between 0.90 and 0.98.
    Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091

  • Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning
    Fang Liu et al.
    Radiology: Artificial Intelligence 2019; 1(3):e180091 • https://doi.org/10.1148/ryai.2019180091
  • PURPOSE: This study is designed to test the authors’ hypothesis that radiologists’ reports from multiple imaging centers performing a lumbar MRI examination on the same patient over a short period of time will have (1) marked variability in interpretive findings and (2) a broad range of interpretive errors.
    STUDY DESIGN: This is a prospective observational study comparing the interpretive findings reported for one patient scanned at 10 different MRI centers over a period of 3 weeks to each other and to reference MRI examinations performed immediately preceding and following the 10 MRI examinations.
    PATIENT SAMPLE: The sample is a 63-year-old woman with a history of low back pain and right L5 radicular symptoms.
  • RESULTS: Across all 10 study examinations, there were 49 distinct findings reported related to the presence of a distinct pathology at a specific motion segment. Zero interpretive findings were reported in all 10 study examinations and only one finding was reported in nine out of 10 study examinations. Of the interpretive findings, 32.7% appeared only once across all 10 of the study examinations’ reports.
  • CONCLUSIONS: This study found marked variability in the reported interpretive findings and a high prevalence of interpretive errors in radiologists’ reports of an MRI examination of the lumbar spine performed on the same patient at 10 different MRI centers over a short time period. As a result, the authors conclude that where a patient obtains his or her MRI examination and which radiologist in- terprets the examination may have a direct impact on radiological diagnosis, subsequent choice of treatment, and clinical outcome.
  • “With a sufficient supply of expertly labeled examples, an appropriately designed model can learn to emulate the judgments of those expert clinicians who provided the labels. In this work, we hypothesized that a deep learning model trained on a large dataset of high-quality labels would produce an automated fracture detector capable of emulating the diagnostic acumen of a team of experienced orthopedic surgeons.”
    Deep neural network improves fracture detection by clinicians
    Lindsey R et al.
    PNAS | November 6, 2018 | vol. 115 | no. 45 | 11591–11596
  • “The dataset consisted of 135,845 radiographs of a variety of body parts. Of these, 34,990 radiographs were posterior–anterior or lateral wrist views. The remaining 100,855 radiographs belonged to 11 other body parts: foot, elbow, shoulder, knee, spine, femur, ankle, humerus, pelvis, hip, and tibia. The nonwrist body part with the maximum number of radiographs was shoulder with 26,042 images, and spine had the least number of radiographs with only 885 images.
    Deep neural network improves fracture detection by clinicians
    Lindsey R et al.
    PNAS | November 6, 2018 | vol. 115 | no. 45 | 11591–11596

  • Deep neural network improves fracture detection by clinicians
    Lindsey R et al.
    PNAS | November 6, 2018 | vol. 115 | no. 45 | 11591–11596
  • “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
  • FDA Statement
    The OsteoDetect software is a computer-aided detection and diagnostic software that uses an artificial intelligence algorithm to analyze two-dimensional X-ray images for signs of distal radius fracture, a common type of wrist fracture. The software marks the location of the fracture on the image to aid the provider in detection and diagnosis.
  • FDA Statement
    OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight regions of distal radius fracture during the review of posterior-anterior (front and back) and medial-lateral (sides) X-ray images of adult wrists. OsteoDetect is intended to be used by clinicians in various settings, including primary care, emergency medicine, urgent care and specialty care, such as orthopedics. It is an adjunct tool and is not intended to replace a clinician’s review of the radiograph or his or her clinical judgment.
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.