This retrospective study utilizing MRI demonstrates that the medial head of the gastrocnemius is the most commonly injured muscle of the calf, closely followed by the soleus, the latter finding rarely reported in the sonographic literature. Dual injuries of the calf muscle complex occur much more commonly than previously reported and may be of prognostic significance.

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Meningiomas are the most common intracranial primary neoplasm in adults. Over recent years, interest in this clinically diverse group of tumors has intensified, bringing new questions and challenges to the fore, particularly in the fields of epidemiology, radiology, pathology, genetics, and treatment. Interest in modern meningioma research has been stimulated by the high tumor prevalence and the advances in technology. The incidence of meningiomas is climbing, and may indicate increased exposure to environmental risk factors or more sensitive diagnostic modalities. Technological advances have dramatically improved radiologic imaging and radiotherapy treatments, and further refinements are under investigation. Furthermore, the current era of tumor genetics and molecular biology is challenging translational researchers to discover new, targeted, therapeutic agents. This review is an update on the recent advances in the understanding of meningiomas and their management, and highlights pertinent research questions to be addressed in the future.

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BackgroundOperating rooms contribute between 20% to 70% of hospital waste. This study aimed to evaluate the waste burden of neurointerventional procedures performed in a radiology department, identify areas for waste reduction, and motivate new greening initiatives.MethodsWe performed a waste audit of 17 neurointerventional procedures at a tertiary-referral center over a 3-month period. Waste was categorized into five streams: general waste, clinical waste, recyclable plastic, recyclable paper, and sharps. Our radiology department started recycling soft plastics from 13 December 2019. Hence, an additional recyclable soft plastic waste stream was added from this time point. The weight of each waste stream was measured using a digital weighing scale.ResultsWe measured the waste from seven cerebral digital subtraction angiograms (DSA), six mechanical thrombectomies (MT), two aneurysm-coiling procedures, one coiling with tumour embolization, and one dural arteriovenous fistula embolization procedure. In total, the 17 procedures generated 135.3 kg of waste: 85.5 kg (63.2%) clinical waste, 28.0 kg (20.7%) general waste, 14.7 kg (10.9%) recyclable paper, 3.5 kg (2.6%) recyclable plastic, 2.2 kg (1.6%) recyclable soft plastic, and 1.4 kg (1.0%) of sharps. An average of 8 kg of waste was generated per case. Coiling cases produced the greatest waste burden (13.1 kg), followed by embolization (10.3 kg), MT (8.8 kg), and DSA procedures (5.1 kg).ConclusionNeurointerventional procedures generate a substantial amount of waste, an average of 8 kg per case. Targeted initiatives such as engaging with suppliers to revise procedure packs and reduce packaging, digitizing paper instructions, opening devices only when necessary, implementing additional recycling programs, and appropriate waste segregation have the potential to reduce the environmental impact of our specialty.

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.

No aspect of neurointerventional practice has been associated with as longstanding contention and debate as to its effectiveness as has vertebroplasty (VP). Four blinded randomized controlled trials published since 2009 have demonstrated conflicting results regarding a conferred benefit in pain reduction and functional improvement for patients who undergo VP for osteoporotic vertebral compression fractures. Significant heterogeneity exists between each of these trials, which has resulted in difficulty for interventionalists and surgeons to translate the trial findings into routine clinical practice. In addition, patients and their families are ever more enlightened and enabled via the internet and social media to review both medical literature and websites. Without the proper background and context, their decisions may be lacking appropriate and necessary scientific discussion. This review article summarizes the randomized controlled trial data to date, with particular focus on the aforementioned four blinded studies. We will also evaluate the profound impact of the decrease in vertebral augmentation utilization on short- and long-term patient morbidity and mortality using available national and administrative datasets from both within the USA and internationally. We also consider future trial design to help evaluate this procedure and determine its role in modern neurointerventional practice.

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