google ads
Search

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

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

-- OR --

  • “The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.”
    Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions
    Gyftopoulos S et al.
    AJR 2019; 213:1–8
  • “Several studies have shown promising results of using ML to determine bone age. Using datasets from two separate chil- dren’s hospitals, Larson et al. found that their deep CNN was able to estimate skeletal maturity with accuracy comparable to that of an expert radiologist as well as to that of existing automated bone age software. Tajmir et al. showed that AI-assisted radiologist interpretation performed better than AI alone, a radiologist alone, or a pooled cohort of experts, by increasing accuracy and decreasing variability and the root-mean-square error. Their findings suggest that the most optimal use of AI for determination of bone age may be in combination with a radiologist’s interpretation.”
    Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions
    Gyftopoulos S et al.
    AJR 2019; 213:1–8
  • “The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriate- ness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that MSK imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.”
    Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions
    Gyftopoulos S et al.
    AJR 2019; 213:1–8
  • “Radiomics is an emerging field in medicine that is based on the extraction of diverse quantitative characteristics from images and the use of these characteristics for data mining and pattern identification. These data can then be used with other patient information to better characterize and predict disease processes. ML techniques have led to a rapid expansion of the potential of radiomics to impact clinical care. For instance, the description of a sarcoma diagnosed on MRI will typically include estimates of tumor size, shape, and enhancement pattern. ML-driven algorithms can also identify and collect other characteristics that are not easily appreciated on images (e.g., texture analysis, image intensity histograms, and image voxel relationships) and can lead to more precise treatment.”
    Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions
    Gyftopoulos S et al.
    AJR 2019; 213:1–8

  • 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 perfor mance 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
  • “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
  • “We have shown that radiological scores can be predicted to an excellent standard using only the disc-specific assessments as a reference set. The proposed method is quite general, and although we have implemented it here for sagittal T2 scans, it could easily be applied to T1 scans or axial scans, and for radiological features not studied here or indeed to any medical task where label/grading might be available only for a small region or a specific anatomy of an image. One benefit of automated reading is to produce a numerical signal score that would provide a scale of degeneration and so avoid an arbitrary categorization into artificial grades.”
    Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist
    Jamaludin A et al.
    Eur Spine J 2018; DOI 10.1007/s00586-017-4956-3
  • “Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts.”
    Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist
    Jamaludin A et al.
    Eur Spine J 2018; DOI 10.1007/s00586-017-4956-3
  • The process in a flow chart
  • One of the biggest potential bottlenecks that could inhibit or derail AI development and adoption in health care is the availability of sufficient quantities of high-quality data in standardized formats. As noted earlier, information today is highly fragmented and spread across the industry, residing in diverse, mostly uncoordinated repositories like electronic medical records, laboratory and imaging systems, physician notes, and health-insurance claims. Merging this information into large, integrated databases, which is required to empower AI to develop the deep understanding of diseases and their cures, is difficult.
    Artificial Intelligence- The Next Digital Frontier
    McKinsey Global Institute(2017)
  • 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.
  • FDA Approval Statement (AIDOC)
  • "Deep learning–based approaches have the potential to maximize diagnostic performance for detecting cartilage degeneration and acute cartilage injury within the knee joint while reducing subjectivity, variability, and errors due to distraction and fatigue associated with human interpretation."
    Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection
    FangLiu et al.
    Radiology 2018 (in press)
  • Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders. In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance.

    
Deep learning for automated skeletal bone age assessment in X-ray images.
Spampinato C  et al.
Med Image Anal. 2017 Feb;36:41-51

  • “In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance. Furthermore, this is the first automated skeletal bone age assessment work tested on a public dataset and for all age ranges, races and genders, for which the source code is available, thus representing an exhaustive baseline for future research in the field. Beside the specific application scenario, this paper aims at providing answers to more general questions about deep learning on medical images: from the comparison between deep-learned features and manually-crafted ones, to the usage of deep-learning methods trained on general imagery for medical problems, to how to train a CNN with few images.”

    
Deep learning for automated skeletal bone age assessment in X-ray images.
  • “An automated machine learning computer system was created to detect, anatomically localize, and categorize vertebral compression fractures at high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images.”
Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
    “Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis.”


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
  • “This system performed with 95.7% sensitivity in fracture detection and lo- calization to the correct vertebral level, with a low false-positive rate. There was a high level of overall agreement (95%) for compression morphology and 68% overall agreement for severity categorization relative to radiologist classification.”


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
  • *A fully automated machine learning software system with which to detect, localize, and classify compression fractures and determine the bone density of thoracic and lumbar vertebral bodies on CT images was developed and validated. 
* The computer system has a sensitivity of 95.7% in the detection of compression fractures and in the localization of these fractures to the correct vertebrae, with a false-positive rate of 0.29 per patient. 
* The accuracy of this computer system in fracture classification by Genant type was 95% (weighted k = 0.90). 


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
  • “An automated machine learning computer system was created to detect, anatomically localize, and categorize vertebral compression fractures at high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images.”


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
  • “Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis.”


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
  • “This system performed with 95.7% sensitivity in fracture detection and lo- calization to the correct vertebral level, with a low false-positive rate. There was a high level of overall agreement (95%) for compression morphology and 68% overall agreement for severity categorization relative to radiologist classification. .”


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
  • * A fully automated machine learning software system with which to detect, localize, and classify compression fractures and determine the bone density of thoracic and lumbar vertebral bodies on CT images was developed and validated. 

    * The computer system has a sensitivity of 95.7% in the detection of compression fractures and in the localization of these fractures to the correct vertebrae, with a false-positive rate of 0.29 per patient.
    
* The accuracy of this computer system in fracture classification by Genant type was 95% (weighted k = 0.90). 


    Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images 
Burns JE et al.
Radiology (in press)
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.