OBJECTIVE: Lower limb peripheral arterial disease in the symptomatic stage has a significant effect on patients ́ functional disability. Before an intervention, an imaging diagnostic examination is necessary to determine the extent of the disability. This study evaluates cost-effectiveness of duplex ultrasonography (DUS), digital subtraction angiography (DSA), computed tomography angiography (CTA) and magnetic resonance angiography (MRA) in the diagnostics of symptomatic patients with lower limb peripheral arterial disease indicated for endovascular or surgical intervention. METHODS: Discrete event simulation was used to capture lifetime costs and effects. Costs were calculated from the perspective of the health care payer, and the effects were calculated as quality-adjusted life year's (QALY's). The cost-effectiveness analysis was performed to pairwise compare CTA, MRA and DSA with DUS as the baseline diagnostic modality. A scenario analysis and probabilistic sensitivity analysis were carried out to evaluate the robustness of the results. RESULTS: In the basic case, the DUS diagnostic was the least expensive modality, at a cost of EUR 10,778, compared with EUR 10,804 for CTA, EUR 11,184 for MRA, and EUR 11,460 for DSA. The effects of DUS were estimated at 5.542 QALYs compared with 5.554 QALYs for both CTA and MRA, and 5.562 QALYs for DSA. The final incremental cost-effectiveness ratio (ICER) value of all evaluated modalities was below the cost-effectiveness threshold whereas CTA has the lowest ICER of EUR 2,167 per QALY. However, the results were associated with a large degree of uncertainty, because iterations were spread across all cost-effectiveness quadrants in the probabilistic sensitivity analysis. CONCLUSION: For imaging diagnosis of symptomatic patients with lower limb peripheral arterial disease, CTA examination appears to be the most cost-effective strategy with the best ICER value. Baseline diagnostics of the DUS modality has the lowest costs, but also the lowest effects. DSA achieves the highest QALYs, but it is associated with the highest costs.

• MeSH
• analýza nákladů a výnosů * MeSH
• CT angiografie ekonomika   statistika a číselné údaje   MeSH
• diagnostické zobrazování ekonomika   statistika a číselné údaje   MeSH
• digitální subtrakční angiografie * ekonomika   MeSH
• dolní končetina * diagnostické zobrazování   MeSH
• duplexní dopplerovská ultrasonografie ekonomika   MeSH
• kvalitativně upravené roky života * MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční angiografie ekonomika   MeSH
• onemocnění periferních arterií * diagnostické zobrazování   ekonomika   MeSH
• senioři MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: In recent years, there has been an increasing effort to take advantage of the potential use of low magnetic induction devices with less than 1 T, referred to as Low-Field MRI (LF MRI). LF MRI systems were used, especially in the early days of magnetic resonance technology. Over time, magnetic induction values of 1.5 and 3 T have become the standard for clinical devices, mainly because LF MRI systems were suffering from significantly lower quality of the images, e.g., signal-noise ratio. In recent years, due to advances in image processing with artificial intelligence, there has been an increasing effort to take advantage of the potential use of LF MRI with induction of less than 1 T. This overview article focuses on the analysis of the evidence concerning the diagnostic efficacy of modern LF MRI systems and the clinical comparison of LF MRI with 1.5 T systems in imaging the nervous system, musculoskeletal system, and organs of the chest, abdomen, and pelvis. METHODOLOGY: A systematic literature review of MEDLINE, PubMed, Scopus, Web of Science, and CENTRAL databases for the period 2018-2023 was performed according to the recommended PRISMA protocol. Data were analysed to identify studies comparing the accuracy, reliability and diagnostic performance of LF MRI technology compared to available 1.5 T MRI. RESULTS: A total of 1275 publications were retrieved from the selected databases. Only two articles meeting all predefined inclusion criteria were selected for detailed assessment. CONCLUSIONS: A limited number of robust studies on the accuracy and diagnostic performance of LF MRI compared with 1.5 T MRI was available. The current evidence is not sufficient to draw any definitive insights. More scientific research is needed to make informed conclusions regarding the effectiveness of LF MRI technology.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Breast augmentation is one of the most frequently performed cosmetic procedures worldwide, but it carries certain risks including breast implant rupture. Timely and accurate diagnostics of ruptures are crucial, as undiagnosed ruptures can lead to serious health complications. Imaging methods, such as magnetic resonance imaging (MRI), are recommended for the diagnosis of breast implants due to their high accuracy. However, current diagnostics rely heavily on the subjective interpretation and experience of the physician. This study investigates the potential of neural networks (NN) to address this limitation and improve the accuracy of rupture detection in silicone breast implants. We applied a deep learning-based neural network system trained on MRI images of breast implants to detect ruptures. The dataset included annotated MRI scans of symptomatic and asymptomatic patients with confirmed implant integrity or rupture. Several models were trained using ResNet-18, ResNet-50, and Xception networks, with various hyperparameter settings and augmentation techniques applied to enhance model performance and generalizability. The performance of the NN model was evaluated using confusion matrices and standard metrics such as true positive rate (TPR) and true negative rate (TNR). A semi-automated algorithm for the detection of intracapsular ruptures of breast implants on MRI was successfully developed. The algorithm correctly detected ruptures in 95.4% of cases and accurately identified cases without rupture in 86.7% of instances. Our findings highlight the potential of neural networks as a supportive tool in diagnosing breast implant ruptures. By semi-automating rupture detection, NNs can reduce diagnostic errors, expedite image evaluation, and optimize resource use in medical practice. The study underscores the importance of combining artificial intelligence with expert evaluation to enhance patient care and reduce costs in medical diagnostics.

Vysokoškolská učebnice, která se zaměřuje na teorii ošetřovatelství.

• MeSH
• teorie ošetřovatelství MeSH

• Konspekt
• Patologie. Klinická medicína
• Učební osnovy. Vyučovací předměty. Učebnice
• NLK Obory
• ošetřovatelství
• NLK Publikační typ
• učebnice vysokých škol