An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1

Gupta, Rajiv; Krishnam, Sanjith Prahas; Schaefer, Pamela W.; Lev, Michael H.; González, R. Gilberto

doi:10.1016/j.nic.2020.07.005

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…Fortunately, this limitation has now been resolved in many centers with the use of multidetector scanners. In the event of inadequate flow of the contrast agent through the cerebral vasculature, accurate perfusion maps will not be generated. Low cardiac output, suboptimal contrast bolus, cardiac arrhythmia, severe intracranial vascular narrowing, and multiple intracranial emboli are some of the conditions associated with this technical error 49,99,100 . Specific deconvolution methods may be able to correct these errors 26 …”

Section: Pitfalls Of Brain Perfusion Imaging In Stroke Mimicsmentioning

confidence: 99%

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Clinical application of brain perfusion imaging in detecting stroke mimics: A review

Khalili

Wang

Garg

et al. 2022

Journal of Neuroimaging

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Stroke mimics constitute a significant proportion of patients with suspected acute ischemic stroke. These conditions may resemble acute ischemic stroke and demonstrate abnormalities on perfusion imaging sequences. The most common stroke mimics include seizure/epilepsy, migraine with aura, brain tumors, functional disorders, infectious encephalopathies, Wernicke's encephalopathy, and metabolic abnormalities. Brain perfusion imaging techniques, particularly computed tomography perfusion and magnetic resonance perfusion, are being widely used in routine clinical practice for treatment selection in patients presenting with large vessel occlusion. At the same time, the utilization of these imaging modalities enables the opportunity to better diagnose patients with stroke mimics in a time‐sensitive setting, leading to appropriate management, decision‐making, and resource allocation. In this review, we describe patterns of perfusion abnormalities that could discriminate patients with stroke mimics from those with acute ischemic stroke and provide specific case examples to illustrate these perfusion abnormalities. In addition, we discuss the challenges associated with interpretation of perfusion images in stroke‐related pathologies. In general, perfusion imaging can provide additional information in some cases—when used in combination with conventional magnetic resonance imaging and computed tomography—and might help in detecting stroke mimics among patients who present with acute onset focal neurological symptoms.

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Section: Pitfalls Of Brain Perfusion Imaging In Stroke Mimicsmentioning

confidence: 99%

“…(C, D) Three-dimensional pseudo continuous arterial spin labeling showed increased cerebral blood flow in the same regions (arrows) corresponding to thiamine deficiency. The patient's symptoms markedly improved after initiation of intravenous thiamine technical error 49,99,100. Specific deconvolution methods may be able to correct these errors 26.…”

mentioning

confidence: 99%

Clinical application of brain perfusion imaging in detecting stroke mimics: A review

Khalili

Wang

Garg

et al. 2022

Journal of Neuroimaging

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“…The increasing burden on radiological departments is not predicted to be backed with an equivalent increase in radiologists and it is therefore highly likely that increased MRI use will lead to longer response times or increased error rates [9,10]. To counterbalance this for stroke diagnosis, artificial intelligence (AI) has been proposed as a technology to enhance the radiology workflow [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…The detection properties of AI can be used in a multitude of workflows including triaging, detection aid, MRI protocol selection, and contrast agent admission decisions. Several studies have reviewed AI for stroke imaging, but these are either applied to CT, are unsystematic, or with a scope too wide to properly elucidate stroke detection in MRI [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

Bojsen,

Elhakim,

Graumann

et al. 2024

Insights Imaging

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Objectives This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies. Methods PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies. Results Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079. Conclusion Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated. Critical relevance statement This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials. Key Points There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI. Graphical Abstract

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Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging

Mannil

Hainc

Grkovski

et al. 2021

Acta Neurochirurgica Supplement

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An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1

Cited by 13 publications

References 32 publications

Clinical application of brain perfusion imaging in detecting stroke mimics: A review

Clinical application of brain perfusion imaging in detecting stroke mimics: A review

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging

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