scholarly journals The diagnostic accuracy of cross-sectional imaging for detecting acute scaphoid fractures in children: a systematic review

2018 ◽  
pp. 20170883 ◽  
Author(s):  
Amaka C Offiah ◽  
Derek Burke
2021 ◽  
Vol 2 (11) ◽  
pp. 997-1003
Author(s):  
Benjamin J. F. Dean ◽  

Aims Current National Institute for Health and Clinical Excellence (NICE) guidance advises that MRI direct from the emergency department (ED) should be considered for suspected scaphoid fractures. This study reports the current management of suspected scaphoid fractures in the UK and assesses adherence with NICE guidance. Methods This national cross-sectional study was carried out at 87 NHS centres in the UK involving 122 EDs and 184 minor injuries units (MIUs). The primary outcome was availability of MRI imaging direct from the ED. We also report the specifics of patient management pathways for suspected scaphoid fractures in EDs, MIUs, and orthopaedic services. Overall, 62 of 87 centres (71%) had a guideline for the management of suspected scaphoid fractures. Results A total of 11 of 87 centres (13%) had MRI directly available from the ED. Overall, 14 centres (17%) used cross-sectional imaging direct from the ED: MRI in 11 (13%), CT in three (3%), and a mixture of MRI/CT in one (1%). Four centres (6%) used cross-sectional imaging direct from the MIU: MRI in three (4%) and CT in two (2%). Of 87 centres’ orthopaedic specialist services, 74 (85%) obtained repeat radiographs, while the most common form of definitive imaging used was MRI in 55 (63%), CT in 16 (19%), mixture of MRI/CT in three (3%), and radiographs in 11 (13%). Conclusion Only a small minority of centres currently offer MRI directly from the ED for patients with a suspected scaphoid fracture. Further research is needed to investigate the facilitators and barriers to the implementation of NICE guidance. Cite this article: Bone Jt Open 2021;2(11):997–1003.


Author(s):  
Daniel Almeida Ferreira Barbosa ◽  
Lucca Reis Mesquita ◽  
Marcela Maria Costa Borges ◽  
Diego Santiago de Mendonça ◽  
Francisco Samuel Rodrigues de Carvalho ◽  
...  

BJR|Open ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 20210005
Author(s):  
Louis Dwyer-Hemmings ◽  
Cassandra Fairhead

Objectives: To synthesise existing evidence for the diagnostic accuracy of chest radiographs to detect lung malignancy in symptomatic patients presenting to primary care. Methods: A systematic review was performed and reported in accordance with the PRISMA framework, using a protocol prospectively registered with the PROSPERO database (CRD42020212450). Nine databases were searched for relevant studies. Data were extracted and chest radiograph sensitivity and specificity calculated where possible. Risk of bias was assessed using a validated tool. Random effects meta-analysis was performed. Results: Ten studies were included. Sensitivity meta-analysis was performed in five studies which were not the high risk of bias, with summary sensitivity of 81% (95% CI: 74–87%). Specificity could be calculated in five studies, with summary specificity of 68% (95% CI: 49–87%). Conclusions: The sensitivity of chest radiographs for detecting lung malignancy in primary care is relatively low. Physicians and policymakers must consider strategies to attenuate the possibility of false reassurance with a negative chest radiograph for this significant pathology. Options include widening access to cross-sectional imaging in primary care; however, any intervention would need to take into account the medical and financial costs of possible over-investigation. Prospective trials with long-term follow-up are required to further evaluate the risks and benefits of this strategy. Advances in knowledge: The chest radiograph has a sensitivity of 81% and specificity of 68% for lung malignancy in a symptomatic primary-care population. A negative chest radiograph does not exclude lung cancer, and physicians should maintain a low threshold to consider specialist referral or cross-sectional imaging.


2021 ◽  
pp. 20210332
Author(s):  
Conor Joseph Hardacre ◽  
Joseph A Robertshaw ◽  
Shaney L Barratt ◽  
Hannah L Adams ◽  
Robert V MacKenzie Ross ◽  
...  

Objectives: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH). Methods: Searches of Medline, Embase and Web of Science were undertaken on July 1st 2020. Original publications studying AI applied to cross-sectional imaging for the diagnosis of acquired PAH in adults were identified through two-staged double-blinded review. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies and Checklist for Artificial Intelligence in Medicine frameworks. Narrative synthesis was undertaken following Synthesis Without Meta-Analysis guidelines. This review received no funding and was registered in the International Prospective Register of Systematic Reviews (ID:CRD42020196295). Results: Searches returned 476 citations. Three retrospective observational studies, published between 2016 and 2020, were selected for data-extraction. Two methods applied to cardiac-MRI demonstrated high diagnostic accuracy, with the best model achieving AUC=0.90 (95% CI: 0.85–0.93), 89% sensitivity and 81% specificity. Stronger results were achieved using cardiac-MRI for classification of idiopathic PAH, achieving AUC=0.97 (95% CI: 0.89–1.0), 96% sensitivity and 87% specificity. One study reporting CT-based AI demonstrated lower accuracy, with 64.6% sensitivity and 97.0% specificity. Conclusions: Automated methods for identifying PAH on cardiac-MRI are emerging with high diagnostic accuracy. AI applied to cross-sectional imaging may provide non-invasive support to reduce diagnostic delay in PAH. This would be helped by stronger solutions in other modalities. Advances in knowledge: There is a significant shortage of research in this important area. Early detection of PAH would be supported by further research advances on the promising emerging technologies identified.


2018 ◽  
Vol 66 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Asanka R. Wijetunga ◽  
Venessa H. Tsang ◽  
Bruno Giuffre

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