A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

Comparative Analysis of CT and MRI in Emergency Assessment of Stroke: A Review

Objective: To compare the diagnostic accuracy of computed tomography (CT) and magnetic resonance imaging (MRI)in emergency assessment of stroke in brain imaging from the review of literature. Method: Relevant databases (PubMed, google scholar etc.) were searched and literature were reviewed from 1995 to 2019. Literature from non-Scopus and unauthorized authorizations was excluded.Result: It was observed that for MRI DWI (Diffusion-weighted imaging) is preferred and in CT, axial sections are opted. In earlier studies, it was seen that neither CT nor MRI came out to besuperior. This may be due to the previous technology used. Some studies also, suggested that Diffusion-weighted imaging is highly accurate in diagnosis of stroke and also superior to CT. Another study suggested that SWI is a new approach in visualizing the hemorrhage in acute stroke. On one hand, evidence revealed that MRI is as good as CT. While on the other hand, literature concluded that CT angiography is good for intracranial and extracranial vasculature. Some studies suggested that CT is more reliable and is readily available for stroke. Conclusion: Present study concludes that both diagnostic imaging modalities i.e., CT and MRI have their advantages in diagnosis of ischemic and hemorrhagic stroke. Also chances of stroke increases with increase in age. Other factors influencing the stroke diagnosis and treatment are type of stroke, diagnostic imaging modality available, and cost-effectiveness of diagnostic exams performed

A bioimpedance-based monitor for real-time detection and identification of secondary brain injury

Secondary brain injury impacts patient prognosis and can lead to long-term morbidity and mortality in cases of trauma. Continuous monitoring of secondary injury in acute clinical settings is primarily limited to intracranial pressure (ICP); however, ICP is unable to identify essential underlying etiologies of injury needed to guide treatment (e.g. immediate surgical intervention vs medical management). Here we show that a novel intracranial bioimpedance monitor (BIM) can detect onset of secondary injury, differentiate focal (e.g. hemorrhage) from global (e.g. edema) events, identify underlying etiology and provide localization of an intracranial mass effect. We found in an in vivo porcine model that the BIM detected changes in intracranial volume down to 0.38 mL, differentiated high impedance (e.g. ischemic) from low impedance (e.g. hemorrhagic) injuries (p \u3c 0.001), separated focal from global events (p \u3c 0.001) and provided coarse ‘imaging’ through localization of the mass effect. This work presents for the first time the full design, development, characterization and successful implementation of an intracranial bioimpedance monitor. This BIM technology could be further translated to clinical pathologies including but not limited to traumatic brain injury, intracerebral hemorrhage, stroke, hydrocephalus and post-surgical monitoring

Biomarkers of Acute Brain Injury in the Emergency Department

The diagnosis of acute brain injury in the acute care setting is based on neurological examination and neuroimaging tools such as computed tomography (CT) scanning and magnetic resonance imaging (MRI). However, there are limitations to both CT and MRI scanning. The lack of objective, noninvasive and readily accessible clinical tools to detect injury has left clinicians with uncertainty about how to best identify and treat these conditions. It is also very difficult for patients and their families who struggle to better understand the deficits they deal with on a daily basis. There have been many studies exploring many promising biomarkers during the last decade. Despite the large number of published studies there is still a lack of any Food and Drug Administration (FDA)-approved biomarkers for brain injury in adults and children. Given all of these researches, there is now an important need to validate and introduce them into the clinical setting. This chapter reviews commonly studied biomarkers for acute brain injury in humans, with an emphasis on traumatic brain injury and stroke

Diseases of the Brain, Head and Neck, Spine 2020–2023

This open access book offers an essential overview of brain, head and neck, and spine imaging. Over the last few years, there have been considerable advances in this area, driven by both clinical and technological developments. Written by leading international experts and teachers, the chapters are disease-oriented and cover all relevant imaging modalities, with a focus on magnetic resonance imaging and computed tomography. The book also includes a synopsis of pediatric imaging. IDKD books are rewritten (not merely updated) every four years, which means they offer a comprehensive review of the state-of-the-art in imaging. The book is clearly structured and features learning objectives, abstracts, subheadings, tables and take-home points, supported by design elements to help readers navigate the text. It will particularly appeal to general radiologists, radiology residents, and interventional radiologists who want to update their diagnostic expertise, as well as clinicians from other specialties who are interested in imaging for their patient care

Cerebral Vein and Dural Sinus Thrombosis

Cerebral venous thrombosis (CVT) is an uncommon disorder in the general population. At least 1 risk factor can be identified in 85% of patients with CVT. Because of the high frequency of thrombophilia among patients with CVT, screening for hypercoagulable conditions should be performed. Two pathophysiological mechanisms contribute to their highly variable clinical presentation. Four major syndromes have been described: isolated intracranial hypertension, focal neurological abnormalities, seizures, and encephalopathy. Cavernous sinus thrombosis represents the single CVT which produces a characteristic clinical syndrome. Head Computed Tomography is the most frequently performed imaging study, but Magnetic Resonance Imaging of the head combined with Magnetic Resonance venography are the most sensitive studies. Acute phase therapy for CVT focuses on anticoagulation, management of seizures, increased intracranial pressure, and prevention of cerebral herniation. The majority of patients have a complete or partial recovery, however they have an increased incidence of venous thromboembolism. Clinical and imaging follow-ups 3–6 months after diagnosis are recommended to assess for recanalization

An artificial intelligence (AI)-based approach to clinical trial recruitment: The impact of Viz RECRUIT on enrollment in the EMBOLISE trial

Background EMBOLISE (NCT 04402632) is an ongoing randomized controlled trial investigating the safety and efficacy of middle meningeal artery embolization for the treatment of subacute or chronic subdural hematoma (SDH). Viz RECRUIT SDH is an artificial intelligence (AI)-based software platform that can automatically detect SDH in noncontrast computed tomography (NCHCT) images and report the volume, maximum thickness, and midline shift. We hypothesized that the mobile recruitment platform would aid enrollment and coordinate communication and image sharing among the entire research team. Materials and methods Patient enrollment in EMBOLISE prior to and after implementation of Viz RECRUIT SDH at a large comprehensive stroke center was compared along with the performance of the software platform. The EMBOLISE trial was activated on May 5, 2021, and Viz RECRUIT SDH was activated on October 6, 2021. The pre-AI cohort consisted of all patients from EMBOLISE to AI activation (153 days), and the post-AI cohort consisted of all patients from AI activation until August 18, 2022 (316 days). All alerts for suspected SDH candidates were manually reviewed to determine the positive predictive value (PPV) of the algorithm. Results Prior to AI-software implementation, there were 5 patients enrolled (0.99 patients/month) and one screen failure. After the implementation of the software, enrollment increased by 36% to 1.35 patients/month (14 total enrolled), and there were no screen failures. Over the entire post-AI period, a total of 6244 NCHCTs were processed by the system with 207 total SDH detections (3% prevalence). 35% of all alerts for suspected SDH were viewed within 10 min, and 50% were viewed within an hour. The PPV of the algorithm was 81.4 (CI [75.3, 86.7]). Conclusion The implementation of an AI-based software for the automatic screening of SDH patients increased the enrollment rate in the EMBOLISE trial, and the software performed well in a real-world, clinical trial setting