A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images.

Journal: Computers in biology and medicine
PMID: 34968861

Abstract

OBJECTIVE: Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients. Cartilage damage is nowadays assessed by clinicians, which manually measure cartilage thickness in ultrasound (US) imaging. This poses issues relevant to intra-and inter-observer variability. Relying on the acquisition of metacarpal-head US images from 38 subjects, this work addresses the problem of automatic cartilage-thickness measurement by designing a new deep-learning (DL) framework.

Authors

  • Maria Chiara Fiorentino
    Department of Information Engineering, Università Politecnica delle Marche, Italy. Electronic address: m.c.fiorentino@pm.univpm.it.
  • Edoardo Cipolletta
    Department of Rheumatology, Università Politecnica delle Marche, Italy.
  • Emilio Filippucci
    Department of Rheumatology, Università Politecnica delle Marche, Italy.
  • Walter Grassi
    Department of Rheumatology, Università Politecnica delle Marche, Italy.
  • Emanuele Frontoni
  • Sara Moccia
    Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy. Electronic address: sara.moccia@iit.it.

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