Search
CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning Ask the Fish

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 --

  • “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)
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.