Developmental dysplasia of the hip (DDH) is a common congenital problem affecting up to 3% of the the population. If untreated, DDH may lead to hip dislocation and premature osteoarthritis. Current DDH diagnosis is highly operator-dependent as it relies on 2D ultrasound. 3D ultrasound offers more complete, and potentially more reliable, imaging of infant hip geometry. However, it is unclear whether 3D ultrasound images, noisy with artifacts and reconstructed by proprietary algorithms from non-parallel beams, give accurate 3D shape information. We sought to validate the fidelity of acetabular surface models obtained by 3D ultrasound by comparison with those obtained concurrently from MRI. Read More

Meeting name: IPR 2016 Conjoint Meeting & Exhibition , 2016

Authors: Diederichs Chad, Heath Alana, Mabee Myles, Hareendranathan Abhilash, Zonoobi Dornoosh, Thompson Adrienne, Jaremko Jacob

Keywords: DDH, 3D ultrasound, MRI, pediatric radiology, hip dysplasia

Wrist trauma is common in children and generally requires radiography for diagnosis. Many children who receive radiographs do not have fractures and are thus subjected to unnecessary radiation exposure along with increased wait times in the emergency department (ED). Ultrasound (US) is safe, cost-effective, portable and sensitive in visualizing cortical disruption, potentially making it a valuable tool for bedside assessment of fractures. This study aims to determine the feasibility of using US to detect distal radius fractures (DRF) in children and to contrast the accuracy of hand-held device compatible 2D transducers to 3D transducers that can only be used with traditional US machines. In order to address difficulties in US image interpretation by inexperienced users, we investigated the utility of an artificial intelligence (AI) network. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Knight Jessica, Jaremko Jacob, Zhou Yuyue, Keen Christopher, Rakkundedeth Abhilash, Ghasseminia Siyavash, Wichuk Stephanie, Brilz Alan, Alves Pereira Fatima, Kirschner David

Keywords: Artificial Intelligence, Ultrasound, Musculoskeletal