@inproceedings{0ff81797110646548caf0bcbd00cd96a,
title = "Automation of clinical measurements on radiographs of children's hips",
abstract = "Developmental dysplasia of the hip (DDH) and cerebral palsy (CP) related hip migration are two of the most common orthopaedic diseases in children, each affecting around 1-2 in 1000 children. For both of these conditions, early detection is a key factor in long term outcomes for patients. However, early signs of the disease are often missed and manual monitoring of routinely collected radiographs is time-consuming and susceptible to inconsistent measurement. We propose an automatic system for calculating acetabular index (AcI) and Reimer's migration percentage (RMP) from paediatric hip radiographs. The system applies Random Forest regression-voting to fully automatically locate the landmark points necessary for the calculation of the clinical metrics. We show that the fully automatically obtained AcI and RMP measurements are in agreement with manual measurements obtained by clinical experts, and have replicated these findings in a clinical dataset. Such a system allows for the reliable and consistent monitoring of DDH and CP patients, aiming to improve patient outcomes through hip surveillance programmes.",
keywords = "Acetabular index, Automated radiographic measurement, Clinical decision support system, Reimer{\textquoteright}s migration percentage",
author = "Peter Thompson and {Medical Annotation Collaborative} and Perry, {Daniel C} and Timothy Cootes and Claudia Lindner",
note = "Funding Information: Acknowledgements. Peter Thompson was supported by the Wellcome Trust University of Manchester TPA [209741/Z/17/Z] and an Artificial Intelligence in Health and Care Award (AI AWARD02268). Claudia Lindner was funded by the Medical Research Council, UK (MR/S00405X/1) as well as a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (223267/Z/ 21/Z). Daniel C. Perry is funded by the National Institute for Health Research (NIHR) through a NIHR Research Professorship. Manual measurements were provided by the Medical Annotation Collaborative (Grace Airey, Mohammed Ali, Bishoy Bessada, Benjamin Gompels, Tom Hughes, Mustafa Javaid, Zarmina Kakakhel, Mohammed Khattak, James Redfern, David Samy, Myles Simmons, Lucy Stead). This report is independent research funded by the National Institute for Health Research (Artificial Intelligence, An Automated System for Measuring Hip Dysplasia in Children with Cerebral Palsy, AI AWARD02268). The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
doi = "10.1007/978-3-031-16437-8_40",
language = "English",
isbn = "9783031164361",
volume = "13433",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "419--428",
editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",
booktitle = "Springer Lecture Notes in Computer Science",
address = "United States",
}