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.
Howard S. An & Philip K. Louie
Atlas of Spinal Imaging Phenotypes [EPUB ebook]
Phenotypes, Measurements and Classification Systems
Atlas of Spinal Imaging Phenotypes [EPUB ebook]
Phenotypes, Measurements and Classification Systems
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لغة الإنجليزية ● شكل EPUB ● ISBN 9780323761123 ● محرر Howard S. An & Philip K. Louie ● الناشر Elsevier Health Sciences ● نشرت 2021 ● للتحميل 3 مرات ● دقة EUR ● هوية شخصية 8106496 ● حماية النسخ Adobe DRM
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