Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation.

Journal: BMC medical informatics and decision making
PMID: 39026270

Abstract

BACKGROUND: Generalized Joint Hyper-mobility (GJH) can aid in the diagnosis of Ehlers-Danlos Syndrome (EDS), a complex genetic connective tissue disorder with clinical features that can mimic other disease processes. Our study focuses on developing a unique image-based goniometry system, the HybridPoseNet, which utilizes a hybrid deep learning model.

Authors

  • Thirumalesu Kudithi
    School of Technology, The Apollo University, Chittoor, India.
  • J Balajee
    Department of Computer Science and Engineering, Mother Theresa Institute of Engineering and Technology, Palamaner, Chittoor, Andhra Pradesh, 517408, India.
  • R Sivakami
    Department of Computer Science and Engineering, Sona College of Technology, Salem, 636005, India.
  • T R Mahesh
    Department of Computer Science and Engineering, JAIN (Deemed-to-be-University), Bangaluru, Karnataka, India.
  • E Mohan
    Department of ECE, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu, India.
  • Suresh Guluwadi
    Adama Science and Technology University, 302120, Adama, Ethiopia. suresh.guluwadi@astu.edu.et.

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