Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol
Introduction Paediatric fractures are common but can be easily missed on radiography leading to potentially serious implications including long-term pain, disability and missed opportunities for safeguarding in cases of inflicted injury. Artificial intelligence (AI) tools to assist fracture detectio...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-12-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/12/e084448.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850249751188996096 |
|---|---|
| author | Susan C Shelmerdine Owen J Arthurs Dean Langan Alex Novak Kanthan Theivendran Nick Woznitza Emma Allan David Rosewarne Saira Haque Sarim Ather Cato Pauling Emily Ashworth Ka-Wai Yung Joy Barber |
| author_facet | Susan C Shelmerdine Owen J Arthurs Dean Langan Alex Novak Kanthan Theivendran Nick Woznitza Emma Allan David Rosewarne Saira Haque Sarim Ather Cato Pauling Emily Ashworth Ka-Wai Yung Joy Barber |
| author_sort | Susan C Shelmerdine |
| collection | DOAJ |
| description | Introduction Paediatric fractures are common but can be easily missed on radiography leading to potentially serious implications including long-term pain, disability and missed opportunities for safeguarding in cases of inflicted injury. Artificial intelligence (AI) tools to assist fracture detection in adult patients exist, although their efficacy in children is less well known. This study aims to evaluate whether a commercially available AI tool (certified for paediatric use) improves healthcare professionals (HCPs) detection of fractures, and how this may impact patient care in a retrospective simulated study design.Methods and analysis Using a multicentric dataset of 500 paediatric radiographs across four body parts, the diagnostic performance of HCPs will be evaluated across two stages—first without, followed by with the assistance of an AI tool (BoneView, Gleamer) after an interval 4-week washout period. The dataset will contain a mixture of normal and abnormal cases. HCPs will be recruited across radiology, orthopaedics and emergency medicine. We will aim for 40 readers, with ~14 in each subspecialty, half being experienced consultants. For each radiograph HCPs will evaluate presence of a fracture, their confidence level and a suitable simulated management plan. Diagnostic accuracy will be judged against a consensus interpretation by an expert panel of two paediatric radiologists (ground truth). Multilevel logistic modelling techniques will analyse and report diagnostic accuracy outcome measures for fracture detection. Descriptive statistics will evaluate changes in simulated patient management.Ethics and dissemination This study was granted approval by National Health Service Health Research Authority and Health and Care Research Wales (REC Reference: 22/PR/0334). IRAS Project ID is 274 278. Funding has been provided by the National Institute for Heath and Care Research (NIHR) (Grant ID: NIHR-301322). Findings from this study will be disseminated through peer-reviewed publications, conferences and non-peer-reviewed media and social media outlets.Trial registration number ISRCTN12921105. |
| format | Article |
| id | doaj-art-7b81c2a4f2f44afe817fd2c449ce379e |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-7b81c2a4f2f44afe817fd2c449ce379e2025-08-20T01:58:27ZengBMJ Publishing GroupBMJ Open2044-60552024-12-01141210.1136/bmjopen-2024-084448Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocolSusan C Shelmerdine0Owen J Arthurs1Dean Langan2Alex Novak3Kanthan Theivendran4Nick Woznitza5Emma Allan6David Rosewarne7Saira Haque8Sarim Ather9Cato Pauling10Emily Ashworth11Ka-Wai Yung12Joy Barber13UCL Great Ormond Street Institute of Child Health, London, UKClinical Radiology, Great Ormond Street Hospital for Children, London, UKUCL Great Ormond Street Institute of Child Health, London, UKEmergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UKOrthopaedic Surgery, Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UKSchool of Allied Health Professions, Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, UKClinical Radiology, Great Ormond Street Hospital for Children, London, UKClinical Radiology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UKClinical Radiology, Kings College Hospital NHS Foundation Trust, London, UKOxford University Hospitals NHS Foundation Trust, Oxford, UKUCL Great Ormond Street Institute of Child Health, London, UKClinical Radiology, Great Ormond Street Hospital for Children, London, UKWellcome/ EPSRC Centre for Interventional and Surgical Sciences, London, UKClinical Radiology, St George`s Healthcare NHS Trust, London, UKIntroduction Paediatric fractures are common but can be easily missed on radiography leading to potentially serious implications including long-term pain, disability and missed opportunities for safeguarding in cases of inflicted injury. Artificial intelligence (AI) tools to assist fracture detection in adult patients exist, although their efficacy in children is less well known. This study aims to evaluate whether a commercially available AI tool (certified for paediatric use) improves healthcare professionals (HCPs) detection of fractures, and how this may impact patient care in a retrospective simulated study design.Methods and analysis Using a multicentric dataset of 500 paediatric radiographs across four body parts, the diagnostic performance of HCPs will be evaluated across two stages—first without, followed by with the assistance of an AI tool (BoneView, Gleamer) after an interval 4-week washout period. The dataset will contain a mixture of normal and abnormal cases. HCPs will be recruited across radiology, orthopaedics and emergency medicine. We will aim for 40 readers, with ~14 in each subspecialty, half being experienced consultants. For each radiograph HCPs will evaluate presence of a fracture, their confidence level and a suitable simulated management plan. Diagnostic accuracy will be judged against a consensus interpretation by an expert panel of two paediatric radiologists (ground truth). Multilevel logistic modelling techniques will analyse and report diagnostic accuracy outcome measures for fracture detection. Descriptive statistics will evaluate changes in simulated patient management.Ethics and dissemination This study was granted approval by National Health Service Health Research Authority and Health and Care Research Wales (REC Reference: 22/PR/0334). IRAS Project ID is 274 278. Funding has been provided by the National Institute for Heath and Care Research (NIHR) (Grant ID: NIHR-301322). Findings from this study will be disseminated through peer-reviewed publications, conferences and non-peer-reviewed media and social media outlets.Trial registration number ISRCTN12921105.https://bmjopen.bmj.com/content/14/12/e084448.full |
| spellingShingle | Susan C Shelmerdine Owen J Arthurs Dean Langan Alex Novak Kanthan Theivendran Nick Woznitza Emma Allan David Rosewarne Saira Haque Sarim Ather Cato Pauling Emily Ashworth Ka-Wai Yung Joy Barber Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol BMJ Open |
| title | Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol |
| title_full | Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol |
| title_fullStr | Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol |
| title_full_unstemmed | Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol |
| title_short | Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol |
| title_sort | artificial intelligence ai for paediatric fracture detection a multireader multicase mrmc study protocol |
| url | https://bmjopen.bmj.com/content/14/12/e084448.full |
| work_keys_str_mv | AT susancshelmerdine artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT owenjarthurs artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT deanlangan artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT alexnovak artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT kanthantheivendran artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT nickwoznitza artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT emmaallan artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT davidrosewarne artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT sairahaque artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT sarimather artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT catopauling artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT emilyashworth artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT kawaiyung artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol AT joybarber artificialintelligenceaiforpaediatricfracturedetectionamultireadermulticasemrmcstudyprotocol |