Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study
IntroductionMissed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinici...
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| Published in | BMJ open Vol. 14; no. 9; p. e086061 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
British Medical Journal Publishing Group
05.09.2024
BMJ Publishing Group LTD BMJ Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2044-6055 2044-6055 |
| DOI | 10.1136/bmjopen-2024-086061 |
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| Abstract | IntroductionMissed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.Methods and analysisA dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.Ethics and disseminationThe study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.Trial registration numbersThis study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2). |
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| AbstractList | IntroductionMissed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.Methods and analysisA dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.Ethics and disseminationThe study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.Trial registration numbersThis study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2). Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.INTRODUCTIONMissed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.METHODS AND ANALYSISA dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.ETHICS AND DISSEMINATIONThe study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2).TRIAL REGISTRATION NUMBERSThis study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2). Introduction Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.Methods and analysis A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.Trial registration numbers This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2). Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated. A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image. The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal. This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2). |
| Author | Shelmerdine, Susan Espinosa Morgado, Abdala Trinidad Novak, Alex Metcalfe, David Ventre, Jeanne Wilson, Sarah Limphaibool, Nattakarn Hollowday, Max Kiam, Jian Shen Welch, Nick Gleeson, Fergus Woznitza, Nick Oke, Jason Vaz, James Ather, Sarim Mistry, Alpesh Devic, Natasa Costa, Matthew L Jones, Daniel Teh, James Greenhalgh, Lois |
| Author_xml | – sequence: 1 givenname: Alex orcidid: 0000-0002-5880-8235 surname: Novak fullname: Novak, Alex email: Alex.Novak@ouh.nhs.uk organization: Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 2 givenname: Max surname: Hollowday fullname: Hollowday, Max organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 3 givenname: Abdala Trinidad orcidid: 0000-0003-0967-3554 surname: Espinosa Morgado fullname: Espinosa Morgado, Abdala Trinidad organization: Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 4 givenname: Jason surname: Oke fullname: Oke, Jason organization: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK – sequence: 5 givenname: Susan orcidid: 0000-0001-6642-9967 surname: Shelmerdine fullname: Shelmerdine, Susan organization: NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK – sequence: 6 givenname: Nick orcidid: 0000-0001-9598-189X surname: Woznitza fullname: Woznitza, Nick organization: Canterbury Christ Church University, Canterbury Christ Church University, Canterbury, UK – sequence: 7 givenname: David surname: Metcalfe fullname: Metcalfe, David organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 8 givenname: Matthew L surname: Costa fullname: Costa, Matthew L organization: Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Oxford Trauma & Emergency Care (OxTEC), University of Oxford, Oxford, UK – sequence: 9 givenname: Sarah orcidid: 0000-0003-3964-0809 surname: Wilson fullname: Wilson, Sarah organization: Frimley Health NHS Foundation Trust, Frimley, UK – sequence: 10 givenname: Jian Shen surname: Kiam fullname: Kiam, Jian Shen organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 11 givenname: James orcidid: 0000-0002-0513-7220 surname: Vaz fullname: Vaz, James organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 12 givenname: Nattakarn orcidid: 0000-0002-6123-9838 surname: Limphaibool fullname: Limphaibool, Nattakarn organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 13 givenname: Jeanne surname: Ventre fullname: Ventre, Jeanne organization: Gleamer SAS, Paris, France – sequence: 14 givenname: Daniel surname: Jones fullname: Jones, Daniel organization: Gleamer SAS, Paris, France – sequence: 15 givenname: Lois surname: Greenhalgh fullname: Greenhalgh, Lois organization: Patient and Public Involvement Member, Oxford, UK – sequence: 16 givenname: Fergus surname: Gleeson fullname: Gleeson, Fergus organization: Department of Oncology, University of Oxford, Oxford, UK – sequence: 17 givenname: Nick surname: Welch fullname: Welch, Nick organization: Patient and Public Involvement Member, Oxford, UK – sequence: 18 givenname: Alpesh surname: Mistry fullname: Mistry, Alpesh organization: North West MSK Imaging, Liverpool, UK – sequence: 19 givenname: Natasa surname: Devic fullname: Devic, Natasa organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 20 givenname: James surname: Teh fullname: Teh, James organization: Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK – sequence: 21 givenname: Sarim orcidid: 0000-0001-9614-5033 surname: Ather fullname: Ather, Sarim organization: Oxford University Hospitals NHS Foundation Trust, Oxford, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39237277$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1117/1.JMI.5.4.045503 10.1148/radiol.222176 10.1007/s00330-022-08784-6 10.1016/j.acra.2023.06.016 10.1136/emj.18.4.263 10.1016/j.ienj.2013.04.004 10.1148/radiol.211785 10.1136/jech.2006.056622 10.1148/radiol.210937 10.1007/s00256-022-04070-0 10.1007/s00330-021-07892-z 10.1007/s00256-019-03317-7 10.1186/s12873-019-0289-3 10.7759/cureus.6615 10.1136/bmjqs-2018-008370 10.1186/s13244-022-01234-3 10.1007/s11547-020-01197-9 10.1016/j.acra.2007.12.015 10.1148/radiol.2021203886 10.1117/1.JMI.5.1.011018 |
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| Keywords | RADIOLOGY & IMAGING Diagnostic Imaging Emergency Service, Hospital Fractures, Closed Artificial Intelligence |
| Language | English |
| License | This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ. cc-by |
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| Notes | Protocol ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 JV and DJ of the Steering Committee are employees of Gleamer SAS, France. SA is a shareholder of RAIQC, UK. All other authors declare no competing interests. |
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| References | Blazar, Mitchell, Townzen (R7) 2020; 12 Duron, Ducarouge, Gillibert (R14) 2021; 300 Hussain, Cooper, Carson-Stevens (R1) 2019; 19 Guermazi, Tannoury, Kompel (R15) 2022; 302 Hayashi, Kompel, Ventre (R4) 2022; 51 Kuo, Harrison, Curran (R19) 2022; 304 van Leeuwen, Schalekamp, Rutten (R10) 2021; 31 Hillis, Berbaum, Metz (R22) 2008; 15 Shelmerdine, White, Liu (R12) 2022; 13 Snaith, Hardy (R8) 2014; 22 York, Jenkins, Ireland (R9) 2020; 49 Hillis, Schartz (R21) 2018; 5 Bousson, Attané, Benoist (R3) 2023; 30 Guly (R6) 2001; 18 Dratsch, Chen, Rezazade Mehrizi (R18) 2023; 307 Neri, Miele, Coppola (R17) 2020; 125 Donaldson, Reckless, Scholes (R2) 2008; 62 Kelly, Judge, Bollard (R11) 2022; 32 Challen, Denny, Pitt (R16) 2019; 28 Shelmerdine (2024110105502450000_14.9.e086061.12) 2022; 13 Blazar (2024110105502450000_14.9.e086061.7) 2020; 12 2024110105502450000_14.9.e086061.13 2024110105502450000_14.9.e086061.2 Hussain (2024110105502450000_14.9.e086061.1) 2019; 19 2024110105502450000_14.9.e086061.15 Bousson (2024110105502450000_14.9.e086061.3) 2023; 30 2024110105502450000_14.9.e086061.14 2024110105502450000_14.9.e086061.5 2024110105502450000_14.9.e086061.6 2024110105502450000_14.9.e086061.16 2024110105502450000_14.9.e086061.19 2024110105502450000_14.9.e086061.4 Dratsch (2024110105502450000_14.9.e086061.18) 2023; 307 Neri (2024110105502450000_14.9.e086061.17) 2020; 125 2024110105502450000_14.9.e086061.20 2024110105502450000_14.9.e086061.11 2024110105502450000_14.9.e086061.22 2024110105502450000_14.9.e086061.10 2024110105502450000_14.9.e086061.21 Snaith (2024110105502450000_14.9.e086061.8) 2014; 22 York (2024110105502450000_14.9.e086061.9) 2020; 49 |
| References_xml | – volume: 5 start-page: 1 year: 2018 ident: R21 article-title: Multireader sample size program for diagnostic studies: demonstration and methodology publication-title: J Med Imag doi: 10.1117/1.JMI.5.4.045503 – volume: 307 year: 2023 ident: R18 article-title: Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance publication-title: Radiology doi: 10.1148/radiol.222176 – volume: 32 start-page: 7998 year: 2022 ident: R11 article-title: Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE) publication-title: Eur Radiol doi: 10.1007/s00330-022-08784-6 – volume: 30 start-page: 2118 year: 2023 ident: R3 article-title: Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms publication-title: Acad Radiol doi: 10.1016/j.acra.2023.06.016 – volume: 18 start-page: 263 year: 2001 ident: R6 article-title: Diagnostic errors in an accident and emergency department publication-title: Emerg Med J doi: 10.1136/emj.18.4.263 – volume: 22 start-page: 63 year: 2014 ident: R8 article-title: Emergency department image interpretation accuracy: The influence of immediate reporting by radiology publication-title: Int Emerg Nurs doi: 10.1016/j.ienj.2013.04.004 – volume: 304 start-page: 50 year: 2022 ident: R19 article-title: Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis publication-title: Radiology doi: 10.1148/radiol.211785 – volume: 62 start-page: 174 year: 2008 ident: R2 article-title: The epidemiology of fractures in England publication-title: J Epidemiol Community Health doi: 10.1136/jech.2006.056622 – volume: 302 start-page: 627 year: 2022 ident: R15 article-title: Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence publication-title: Radiology doi: 10.1148/radiol.210937 – volume: 51 start-page: 2129 year: 2022 ident: R4 article-title: Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning publication-title: Skeletal Radiol doi: 10.1007/s00256-022-04070-0 – volume: 31 start-page: 3797 year: 2021 ident: R10 article-title: Artificial intelligence in radiology: 100 commercially available products and their scientific evidence publication-title: Eur Radiol doi: 10.1007/s00330-021-07892-z – volume: 49 start-page: 601 year: 2020 ident: R9 article-title: Reporting Discrepancy Resolved by Findings and Time in 2947 Emergency Department Ankle X-rays publication-title: Skeletal Radiol doi: 10.1007/s00256-019-03317-7 – volume: 19 year: 2019 ident: R1 article-title: Diagnostic error in the emergency department: learning from national patient safety incident report analysis publication-title: BMC Emerg Med doi: 10.1186/s12873-019-0289-3 – volume: 12 year: 2020 ident: R7 article-title: Radiology Training in Emergency Medicine Residency as a Predictor of Confidence in an Attending publication-title: Cureus doi: 10.7759/cureus.6615 – volume: 28 start-page: 231 year: 2019 ident: R16 article-title: Artificial intelligence, bias and clinical safety publication-title: BMJ Qual Saf doi: 10.1136/bmjqs-2018-008370 – volume: 13 year: 2022 ident: R12 article-title: Artificial intelligence for radiological paediatric fracture assessment: a systematic review publication-title: Insights Imaging doi: 10.1186/s13244-022-01234-3 – volume: 125 start-page: 505 year: 2020 ident: R17 article-title: Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology publication-title: Radiol Med doi: 10.1007/s11547-020-01197-9 – volume: 15 start-page: 647 year: 2008 ident: R22 article-title: Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis publication-title: Acad Radiol doi: 10.1016/j.acra.2007.12.015 – volume: 300 start-page: 120 year: 2021 ident: R14 article-title: Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study publication-title: Radiology doi: 10.1148/radiol.2021203886 – ident: 2024110105502450000_14.9.e086061.11 doi: 10.1007/s00330-022-08784-6 – volume: 30 start-page: 2118 year: 2023 ident: 2024110105502450000_14.9.e086061.3 article-title: Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms publication-title: Acad Radiol doi: 10.1016/j.acra.2023.06.016 – ident: 2024110105502450000_14.9.e086061.16 doi: 10.1136/bmjqs-2018-008370 – volume: 12 year: 2020 ident: 2024110105502450000_14.9.e086061.7 article-title: Radiology Training in Emergency Medicine Residency as a Predictor of Confidence in an Attending publication-title: Cureus – ident: 2024110105502450000_14.9.e086061.10 doi: 10.1007/s00330-021-07892-z – ident: 2024110105502450000_14.9.e086061.2 doi: 10.1136/jech.2006.056622 – ident: 2024110105502450000_14.9.e086061.15 doi: 10.1148/radiol.210937 – volume: 13 year: 2022 ident: 2024110105502450000_14.9.e086061.12 article-title: Artificial intelligence for radiological paediatric fracture assessment: a systematic review publication-title: Insights Imaging doi: 10.1186/s13244-022-01234-3 – volume: 125 start-page: 505 year: 2020 ident: 2024110105502450000_14.9.e086061.17 article-title: Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology publication-title: Radiol Med doi: 10.1007/s11547-020-01197-9 – volume: 22 start-page: 63 year: 2014 ident: 2024110105502450000_14.9.e086061.8 article-title: Emergency department image interpretation accuracy: The influence of immediate reporting by radiology publication-title: Int Emerg Nurs doi: 10.1016/j.ienj.2013.04.004 – volume: 307 year: 2023 ident: 2024110105502450000_14.9.e086061.18 article-title: Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance publication-title: Radiology doi: 10.1148/radiol.222176 – ident: 2024110105502450000_14.9.e086061.14 doi: 10.1148/radiol.2021203886 – ident: 2024110105502450000_14.9.e086061.22 doi: 10.1016/j.acra.2007.12.015 – ident: 2024110105502450000_14.9.e086061.6 doi: 10.1136/emj.18.4.263 – ident: 2024110105502450000_14.9.e086061.13 – ident: 2024110105502450000_14.9.e086061.19 doi: 10.1148/radiol.211785 – ident: 2024110105502450000_14.9.e086061.4 doi: 10.1007/s00256-022-04070-0 – ident: 2024110105502450000_14.9.e086061.20 – volume: 19 year: 2019 ident: 2024110105502450000_14.9.e086061.1 article-title: Diagnostic error in the emergency department: learning from national patient safety incident report analysis publication-title: BMC Emerg Med doi: 10.1186/s12873-019-0289-3 – volume: 49 start-page: 601 year: 2020 ident: 2024110105502450000_14.9.e086061.9 article-title: Reporting Discrepancy Resolved by Findings and Time in 2947 Emergency Department Ankle X-rays publication-title: Skeletal Radiol doi: 10.1007/s00256-019-03317-7 – ident: 2024110105502450000_14.9.e086061.5 – ident: 2024110105502450000_14.9.e086061.21 doi: 10.1117/1.JMI.5.1.011018 |
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| Snippet | IntroductionMissed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient... Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity.... Introduction Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient... |
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| SubjectTerms | Artificial Intelligence Automation Diagnostic Errors Diagnostic Imaging Emergency medical care Emergency Medicine Emergency Service, Hospital Fractures, Bone - diagnostic imaging Fractures, Closed Humans Medical diagnosis Observational studies Prospective Studies Protocol Radiography - methods RADIOLOGY & IMAGING Research Design Software United Kingdom |
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| Title | Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study |
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