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Atlas of Spinal Imaging Phenotypes (ePub)

Phenotypes, Measurements and Classification Systems (Sprache: Englisch)

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Atlas of Spinal Imaging Phenotypes
 
Spine-related pain is the world's leading disabling condition, affecting every population and a frequent reason for seeking medical consultation and obtaining imaging studies. Numerous spinal phenotypes (observations/traits) and their respective...
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Spine-related pain is the world's leading disabling condition, affecting every population and a frequent reason for seeking medical consultation and obtaining imaging studies. Numerous spinal phenotypes (observations/traits) and their respective measurements performed on various spine imaging have been shown to directly correlate and predict clinical outcomes. Atlas of Spinal Imaging Phenotypes: Classifications and Radiographic Measurements is a comprehensive visual resource that highlights various spinal phenotypes on imaging, describes their clinical and pathophysiological relevance, and discusses and illustrates their respective measurement techniques and classifications.

  • Helps readers better understanding spinal phenotypes and their imaging, and how today's knowledge will facilitate new targeted drug discovery, novel diagnostics and biomarker discovery, and outcome predictions.  

  • Features step-by-step instructions on performing the radiographic measurements with examples of normal and pathologic images to demonstrate the various presentations. 

  • Presents clinical correlation of the phenotypes as well as the radiographic measurements with landmark references. 

  • Includes validated classification systems that complement the phenotypes and radiographic measurements. 

  • Complies the knowledge and expertise of Dr. Dino Samartzis, the preeminent global authority on spinal phenotypes who has discovered and proposed new phenotypes and classification schemes; Dr. Howard S. An, a leading expert in patient management and at the forefront of 3D imaging of various spinal phenotypes; and Dr. Philip Louie, a prolific surgeon who is involved in one of the largest machine learning initiatives of spinal phenotyping. 

Bibliographische Angaben
  • 2021, 282 Seiten, Englisch
  • Herausgegeben: Philip K. Louie, Howard S. An, Dino Samartzis
  • Verlag: Elsevier HealthScience EN
  • ISBN-10: 0323761127
  • ISBN-13: 9780323761123
  • Erscheinungsdatum: 23.03.2021

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eBook Informationen
  • Dateiformat: ePub
  • Grösse: 67 MB
  • Mit Kopierschutz
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Sprache:
Englisch
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