Magnetic resonance imaging, knee arthroscopy, and clinical decision making: A descriptive study of five surgeons

2009 ◽  
Vol 25 (4) ◽  
pp. 577-583 ◽  
Author(s):  
Sarah Derrett ◽  
Gayle D. Walley ◽  
Stephen A. Bridgman ◽  
Paula Richards ◽  
Nicola Maffulli

Objectives: A randomized controlled trial (RCT) showed magnetic resonance imaging for patients waiting for knee arthroscopy did not reduce the number of arthroscopies. Our study aimed to identify decisions made by orthopedic surgeons about whether patients on a waiting list should proceed to arthroscopy, and to describe surgeons’ decisions.Methods: Five surgeons were asked to Think Aloud (TA) as they made their decisions for twelve patients from the original RCT. Audiotapes of the decision making were transcribed for analysis.Results: For five patients, surgeons agreed about proceeding with arthroscopy, although reasoning differed. In no cases did surgeons agree about not proceeding to arthroscopy. Agreement was more likely in patients with clinically diagnosed meniscal abnormality, and less likely in patients with osteoarthritis.Conclusions: Surgeons’ decisions were influenced by patient wishes. For some patients, the decision to proceed with arthroscopy was based solely on clinical diagnosis; MRI may not be advantageous in these instances. Surgeons disagreed more often than they agreed about the decision to proceed with arthroscopy, particularly when OA was diagnosed. This has implications for decision making in the current NHS patient choice environment. Patients may choose a treatment provider from a list of available providers at time of original clinical assessment and diagnosis. The treating surgeon does not necessarily re-examine the patient until the day of surgery. Given the variation between surgeons about the merits of proceeding with arthroscopy, surgeons may end up in the invidious position of providing surgery to patients whom they do not believe will benefit from arthroscopy.

1998 ◽  
Vol 7 (3) ◽  
pp. 205-209 ◽  
Author(s):  
Jerry S. Sher ◽  
Joseph P. Iannotti ◽  
Gerald R. Williams ◽  
Richard J. Herzog ◽  
J.Bruce Kneeland ◽  
...  

2015 ◽  
Vol 48 (4) ◽  
pp. 249-259 ◽  
Author(s):  
Daian Miranda Ferreira ◽  
Régis Otaviano França Bezerra ◽  
Cinthia Denise Ortega ◽  
Roberto Blasbalg ◽  
Públio César Cavalcante Viana ◽  
...  

Abstract Magnetic resonance imaging is a method with high contrast resolution widely used in the assessment of pelvic gynecological diseases. However, the potential of such method to diagnose vaginal lesions is still underestimated, probably due to the scarce literature approaching the theme, the poor familiarity of radiologists with vaginal diseases, some of them relatively rare, and to the many peculiarities involved in the assessment of the vagina. Thus, the authors illustrate the role of magnetic resonance imaging in the evaluation of vaginal diseases and the main relevant findings to be considered in the clinical decision making process.


2007 ◽  
Vol 23 (11) ◽  
pp. 1167-1173.e1 ◽  
Author(s):  
Stephen Bridgman ◽  
Paula J. Richards ◽  
Gayle Walley ◽  
Gilbert MacKenzie ◽  
Darren Clement ◽  
...  

2018 ◽  
Vol 89 (6) ◽  
pp. A41.1-A41
Author(s):  
Heidi N Beadnall ◽  
Linda Ly ◽  
Chenyu Wang ◽  
Thibo Billiet ◽  
Annemie Ribbens ◽  
...  

IntroductionQuantitative magnetic resonance imaging (MRI) analysis is currently used in multiple sclerosis (MS) clinical trials. Quantitative MRI (QMRI) data derived using formal analysis techniques is not used in routine MS clinical practice and its effect on clinical decision-making is unknown. The study objective is to explore the influence that QMRI data has on clinical decision-making in real-world MS patients.MethodsClinical MS brain MRI scans (separated by one-year minimum, acquired on the same scanner from the same patient) were evaluated. All patients were on the same disease-modifying therapy (DMT) six months prior to and during the study. QMRI analyses were performed on scan pairs by; imaging analysts using specialised software [semi-automated], and MSmetrix [fully-automated]. Data was presented in two separate reports; local QMRI (semi-automated) and centralised QMRI (MSmetrix). Questionnaires were completed by the same neurologist for each subject using clinical data and standard MRI and QMRI reports.Results31 relapsing-MS patients (77.4% female), with baseline age 42.14 [10.70] years, disease duration 7.68 [4.89] years and EDSS score 1.40 (1.36), were evaluated. Injectable, oral and infusion DMTs were administered in 29.0%, 61.3% and 9.7% of patients respectively. According to questionnaire responses, 83.9% were predicted to have stable disease over the next year based on clinical reports alone and 67.7% with the addition of QMRI report data. DMT change would be considered in 16.1% based on clinical reports and 32.3% with QMRI report inclusion. Earlier clinical ±MRI follow up was considered in 51.6% (MRI only 41.9%;both 9.7%) when QMRI reports were reviewed.ConclusionThis preliminary retrospective study indicates that QMRI report data has the potential to influence clinical decision-making in relapsing-MS patients on DMT regarding disease stability assessment, therapy change contemplation, and consideration of earlier follow-up. This work supports a role for formal QMRI analysis and reporting as a clinical-decision support system in MS.


2021 ◽  
Vol 8 ◽  
Author(s):  
Hugh O'Brien ◽  
John Whitaker ◽  
Baldeep Singh Sidhu ◽  
Justin Gould ◽  
Tanja Kurzendorfer ◽  
...  

Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows.Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar.Methods: A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes, which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA dataset (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts.Results: Note that 84.7% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA-derived data, with no further training, where it achieved an 88.3% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians.Conclusion: Automatic ischemic scar detection can be performed from a routine cardiac CTA, without any scar-specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times, and guide clinical decision-making.


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