Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects

Karthik, R.; Menaka, R.; Johnson, Annie; Anand, Sundar

doi:10.1016/j.cmpb.2020.105728

Cited by 65 publications

(36 citation statements)

References 80 publications

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“…Given the emerging evidence that earlier stroke treatment via MT improves outcomes, analysis of the impact of AI tools, such as RAPID-CTA, on stroke workflow metrics, such as CTA-to-groin puncture time, would have clinical utility. A number of investigators have investigated ways in which AI can be used in the setting of stroke detection, triage, evaluation, and risk stratification [30][31][32][33][34][35][36][37][38][39][40][41][42][43] with some techniques having prognostic value in predicting outcomes from therapy. 41,42 One of the major goals of AI is active radiology worklist reprioritization, which would automatically alert the radiologist to the presence of an imaging study requiring emergent interpretation.…”

Section: Background and Significancementioning

confidence: 99%

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Adhya

Eisenmenger

et al. 2021

Neuroradiol J

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Purpose Several new techniques have emerged for detecting anterior circulation large vessel occlusion by quantifying relative vessel density including RAPID-CTA, potentially allowing for faster triage and decreased time to mechanical thrombectomy. We present our one-year experience on positive predictive value of RAPID-CTA for the detection of large vessel occlusion in patients presenting with stroke symptoms and its effect on treatment time and clinical outcomes. Materials and methods Three hundred and ten patients presenting with stroke symptoms with relative vessel density <60% on RAPID-CTA were included (average age 70 years, 145 male, 165 female). Examinations were considered positive if there was evidence of large vessel occlusion or high grade stenosis. Computed tomography angiography to groin puncture time was calculated during one-year time intervals before and after RAPID-CTA installation. Ninety-day Modified Rankin Scale scores were obtained for patients in each cohort. Results Of the 310 patients, 270 had large vessel occlusion or high grade stenosis (87% positive predictive value), with 161 having large vessel occlusion. Using 45% relative vessel density threshold, 129/161 large vessel occlusion were detected (80% sensitivity) and 163/172 examinations were positive (95% positive predictive value). Computed tomography angiography to groin puncture time was significantly lower after deployment of RAPID-CTA (93 min vs 68 min, p<0.05). Average 90 day modified Rankin Scale score was lower in the RAPID-CTA group with a higher percentage of patients with functional independence, although the data was not statistically significant. Conclusion RAPID-CTA had high positive predictive value for large vessel occlusion with a 45% relative vessel density threshold, which could facilitate active worklist reprioritization. Time to treatment was significantly lower and clinical outcomes were improved after deployment of RAPID-CTA.

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Section: Background and Significancementioning

confidence: 99%

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Adhya

Eisenmenger

et al. 2021

Neuroradiol J

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“…The CAD-assisted detection systems are usually composed of various image processing techniques to perform pre-processing, segmentation, feature extraction, feature selection, and classification. Several CAD-based approaches are proposed to diagnose brain abnormality in imagery, as represented using different modalities [ 32 , 33 , 34 ]. These semi-automated or fully automated approaches are applied to detect either a single brain abnormality or multiple pathologies in a supervised or unsupervised fashion [ 33 , 34 ], and deploy machine learning or deep learning techniques to enhance accuracy and efficiency [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Several CAD-based approaches are proposed to diagnose brain abnormality in imagery, as represented using different modalities [ 32 , 33 , 34 ]. These semi-automated or fully automated approaches are applied to detect either a single brain abnormality or multiple pathologies in a supervised or unsupervised fashion [ 33 , 34 ], and deploy machine learning or deep learning techniques to enhance accuracy and efficiency [ 32 ]. A general categorisation of various approaches employed by CAD systems to assess TBI is shown in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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“…The review articles in the literature have extensively investigated and discussed the recent advancements in neuroimaging, various techniques employed for detection and lesion segmentation (lesion stage wise by Yue), and the challenges involved in these techniques [23][24][25][26][27]. Although these studies have contributed tremendously, there is still scope for a deeper and more exhaustive study particularly based on modalities and their suitability to the current conditions.…”

Section: Introductionmentioning

confidence: 99%

A Review on Computer Aided Diagnosis of Acute Brain Stroke

Inamdar

Raghavendra

Gudigar

et al. 2021

Sensors

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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.

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Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects

Cited by 65 publications

References 80 publications

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

A Review on Computer Aided Diagnosis of Acute Brain Stroke

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