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

Musculoskeletal: Artificial Intelligence (ai) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Musculoskeletal ❯ Artificial Intelligence (AI)

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  • “Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.”
    Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence
    Natalia Gorelik, Jaron Chong, Dana J. Lin
    Semin Musculoskelel Radiol 2020;24:38–49.
  • “Arttificial intelligence (AI) has the potential to affect every step of the radiology workflow from ordering with clinical decision support, examination scheduling and protocoling, image acquisition and reconstruction, radiation dose estimation and reduction, quality control, optimization of automatic image display with hanging protocols, worklist management with prioritization of urgent or abnormal studies, integration of radiologic data with clinical data, quantitative image analy- sis, structured reporting, delivery of results to the referring location. selected and input to a ML classifier, like support vector physician, to billing and coding.”
    Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence
    Natalia Gorelik, Jaron Chong, Dana J. Lin
    Semin Musculoskelel Radiol 2020;24:38–49.
  • “The proficiency of AI applications in pattern recognition holds great promise for improving patient care through achieving higher diagnostic accuracy, better predicting individual out- comes, and increasing radiologists’ efficiency, which is essential tial in light of the ever-increasing imaging volumes in both absolute number of examinations as well as the amount of data per study. In this article we reviewed how pattern recognition in MSK imaging using AI could facilitate the diagnosis of bone tumors, detection of bone metastases, evaluation of pediatric bone age, identification of fractures, labeling of images, and assessment of OA. Future research will no doubt further expand on the variety of MSK pathologies that can be addressed with AI-based solutions. As this field continues to evolve, radiology researchers, societies, and industry will collaborate to tackle the challenges ahead to improve radiololgy, technology, and patient care.”
    Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence
    Natalia Gorelik, Jaron Chong, Dana J. Lin
    Semin Musculoskelel Radiol 2020;24:38–49.
  • “The advent of AI in radiology lends a quantitative lens to the imaging practice to create more value for the patient and the referring physicians. We anticipate that integration of AI tools with BI&A will continue to rise at a rapid pace, particularly as demands for quality and efficiency grow, and our imaging informatics infrastructure grows increasingly complex. Specifically, the most salient growth will depend on the guidance of national professional societies such as the ACR to align AI development along appropriate standards, and the fastest business AI development is likely to arise from the pressure points along various regulatory drivers such as merit-based incentive payments and APMs.”
    From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value
    Teresa Martin-Carreras, Po-Hao Chen
    Semin Musculoskelet Radiol 2020;24:65–73.

  • From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value
    Teresa Martin-Carreras, Po-Hao Chen
    Semin Musculoskelet Radiol 2020;24:65–73.
  • “Computed tomography with multiple detectors and the advancement of processors improved rendered images and three-dimensional reconstructions in clinical practice. Traditional axial slices form non-intuitive images because they are seen in only one plane. The three-dimensional reconstructions can show structures details and diseases with complex anatomy in different perspectives. Cinematic rendering is a newly three-dimensional reconstruction technique, already approved for clinical use, which can produce realistic images from traditional computed tomography data.”
    Cinematic rendering for three-dimensional reconstructions of the chest wall: a new reality
    Altair da Silva Costa Jr., Norman Gellada
    DOI: 10.31744/einstein_journal/2020MD5223
  • “The possibility of simultaneous visualization of different anatomical structures of a larger body region in colors introduced by volume rendering has changed imaging assessment standards. This different depiction of image data may be useful for anatomically complex structures and diseases and provides a more user-friendly illustration of imaging findings for medical training and treatment planning.”
    Cinematic rendering for three-dimensional reconstructions of the chest wall: a new reality
    Altair da Silva Costa Jr., Norman Gellada
    DOI: 10.31744/einstein_journal/2020MD5223
  • “As light ray passes through the volume, volume contributions are accumulated and calculated at specific points along the ray using the aforementioned sampling process. Each sample point is classified and assigned a color and an opacity value via transfer functions. This step allows the construction of a color representation from gray scale sections. Composition is the process by which color and opacity values from each line of sample points are accumulated using a mathematical formula to generate.”
    Cinematic rendering for three-dimensional reconstructions of the chest wall: a new reality
    Altair da Silva Costa Jr., Norman Gellada
    DOI: 10.31744/einstein_journal/2020MD5223
  • "Useful as they may be for complex anatomy assessment, volume rendering techniques are not perfect and have some downsides, such as potential masking of anatomical information and pathological changes. Therefore, images must be interpreted by trained, experienced professionals. Visualization tools (i.e., filtering and subtraction) increase diagnostic accuracy. The fact that nothing is added to what is actually present in original images acquired from patients must be emphasized. In dubious cases, reconstructions must be correlated and compared with corresponding original multiplane images.”
    Cinematic rendering for three-dimensional reconstructions of the chest wall: a new reality
    Altair da Silva Costa Jr., Norman Gellada
    DOI: 10.31744/einstein_journal/2020MD5223
  • “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
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