Background: This study was aimed at determining the extent to which after-hours diagnostic imaging is appropriate within the case hospital's Emergency Department. This was amid growing concerns of the inappropriateness of some medical investigations within the Australian health-care system.
Methods: After-hours referral data and patient notes were used in reviewing the clinical case. Diagnostic imaging was deemed appropriate if reflective of clinical guidelines, and if not reflective, whether the investigation changed the patient's ongoing management.
Results: Results indicated that 96.37% of after-hours diagnostic imaging adhered to clinical guidelines and was appropriately requested, with 95.85% changing the ongoing management of the patient. The most sought after diagnostic imaging procedures were Chest X-Ray (30.83%), and CT Brain (16.58%), with 99.16% and 98.44 appropriateness respectively. Chest pain (14.49%) and motor vehicle accidents (8.12%) were the leading reason for ordering after-hours imaging.
Conclusion: This study provided an Emergency Department example as it relates to after-hours diagnostic imaging appropriateness. This study found that most after-hours referrals were appropriate.
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http://dx.doi.org/10.1016/j.amsu.2016.11.019 | DOI Listing |
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View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
View Article and Find Full Text PDFJ Imaging Inform Med
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Monash Imaging, Monash Health, 246 Clayton Rd, Clayton, VIC, 3168, Australia.
We extend existing techniques by using generative adversarial network (GAN) models to reduce the appearance of cast shadows in radiographs across various age groups. We retrospectively collected 11,500 adult and paediatric wrist radiographs, evenly divided between those with and without casts. The test subset consisted of 750 radiographs with cast and 750 without cast.
View Article and Find Full Text PDFUrol Oncol
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Research Department, Urovallarta Medical Center, Puerto Vallarta, Mexico.
Background: Multiparametric MRI (Mp-MRI) is a key tool to screen for Prostate Cancer (Pca) and Clinically Significant Prostate Cancer (CsPca). It primarily includes T2-Weighted imaging (T2w), diffusion-weighted imaging (DWI), and Dynamic Contrast-Enhanced imaging (DCE). Despite its improvements in CsPca screening, concerns about the cost-effectiveness of DCE persist due to its associated side effects, increased cost, longer acquisition time, and limitations in patients with poor kidney function.
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