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Clostridioides difficile infection (CDI) poses a persistent challenge in healthcare, with substantial morbidity and mortality implications. This comprehensive review explores current CDI management, emphasising guidelines from IDSA, SHEA, and ESCMID. Additionally, this study spotlights recent drug developments that have the potential to reshape CDI treatment paradigms. Within the current treatment landscape, fidaxomicin, vancomycin, bezlotoxumab, and faecal microbiota transplantation offer varied options, each with its unique strengths and limitations. Fidaxomicin, effective yet resource-constrained, presents a dilemma, with vancomycin emerging as a pragmatic alternative. Bezlotoxumab, though augmenting antibiotics, grapples with cost and safety concerns. Meanwhile, faecal microbiota transplantation, highly efficacious, confronts evolving safety considerations. The horizon of CDI treatment also features promising therapies such as SER-109 and Rebyota, epitomising the evolving paradigm. As CDI management advances, the critical role of standardised microbiome restoration therapies becomes evident, ensuring long-term safety and diversifying treatment strategies.
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BACKGROUND: Interventional radiology employs minimally invasive image-guided procedures for diagnosing and treating various conditions. Among these procedures, alcohol and thermal ablation techniques have shown high efficacy. However, these procedures present challenges such as increased procedure time, radiation dose, and risk of tissue injury. This scoping review aims to explore how augmented reality (AR) can mitigate these challenges and improve the accuracy, precision, and efficiency of image-guided tumor ablation while improving patient outcomes. METHODS: A scoping review of the literature was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline to identify published literature investigating AR in image-guided tumor ablations. We conducted our electronic searches using PubMed, Scopus, Web of Sciences and CINAHL from inception to April 27th, 2023. The following Boolean terms were used for the search: ("augmented reality" OR "AR" OR "navigation system" OR "head mounted device" OR "HMD") AND ("tumor ablation" OR "radiofrequency tumor ablation" OR "microwave tumor ablation" OR "cryoablation"). We considered articles eligible for our scoping review if they met the following conditions: (1) published in English only, (2) focused on image-guided tumour ablations, (3) incorporated AR techniques in their methodology, (4) employed an aspect of AR in image-guided tumour ablations, and (5) exclusively involved human subjects. Publications were excluded if there was no mention of applying AR, or if the study investigated interventions other than image-guided tumour ablations. RESULTS: Our search results yielded 1,676 articles in our initial search of the databases. Of those, 409 studies were removed as duplicates. 1,243 studies were excluded during the title and abstract screening. 24 studies were assessed for eligibility in the full-text stage. 19 studies were excluded, resulting in a final selection of only five studies that satisfied our inclusion criteria. The studies aimed to assess AR's efficacy in tumor ablations. Two studies compared an optical-based AR system with CT guidance. Two studies used a head-mounted AR device, while one used a dual-camera setup. Various tumor types were examined, including bone, abdominal soft tissue, breast, hepatic, renal, colorectal, and lung lesions. All studies showed positive results, including reduced radiation exposure, shorter procedures, and improved navigation, and targeting assistance. CONCLUSION: AR systems enhance image-guided tumor ablations by improving the accuracy of ablation probe placements and increasing efficiency. They offer real-time guidance, enhanced visualization, and improved navigation, resulting in optimal needle placement. AR reduces radiation exposure and shortens procedure times compared to traditional CT-guided techniques. However, limitations like small sample sizes and technical challenges require further research. Despite this, AR shows potential benefits and larger, diverse studies are needed for validation.
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BACKGROUND AND PURPOSE: Artificial intelligence (AI) algorithms, particularly deep learning, have made significant strides in image recognition and classification, providing remarkable diagnostic accuracy to various diseases. This domain of AI has been the focus of many research papers as it directly relates to the roles and responsibilities of a radiologist. However, discussions on the impact of such technology on the radiography profession are often overlooked. To address this gap in the literature, this paper aims to address the application of AI in radiography and how AI's rapid emergence into healthcare is impacting not only standard radiographic protocols but the role of the radiographic technologist as well. METHODS: A review of the literature on AI and radiography was performed, using databases within PubMed, Google Scholar, and ScienceDirect. Video presentations from YouTube were also utilized to weigh the varying opinions of world leaders at the fore of artificial intelligence. RESULTS: AI can augment routine standard radiographic protocols. It can automatically ensure optimal patient positioning within the gantry as well as automate image processing. As AI technologies continue to emerge in diagnostic imaging, practicing radiologic technologists are urged to achieve threshold computational and technical literacy to operate AI-driven imaging technology. CONCLUSION: There are many applications of AI in radiography including acquisition and image processing. In the near future, it will be important to supply the demand for radiographers skilled in AI-driven technologies.
