Artificial Intelligence and Stroke Imaging

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

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.

Intelligent diagnosis of lung nodule images based on machine learning in the context of lung teaching

The vast majority of intelligent diagnosis models have widespread problems, which seriously affect the medical staff judgment of patients' injuries. So depending on the situation, you need to use different algorithms, The study suggests a model for intelligent diagnosis of lung nodule images based on machine learning, and a support vector machine-based machine learning algorithm is selected. In order to improve the diagnostic accuracy of intelligent diagnosis of lung nodule images as well as the diagnostic model of lung nodule images. The objectives are broken down into algorithm determination and model construction, and the proposed optimized model is solved using machine learning techniques in order to achieve the original algorithm selected for intelligent diagnosis of lung nodule photos. The validation findings demonstrated that dimensionality reduction of the features produced 17 × 1120 and 17 × 2980 non-node matrices with 1216 nodes and 3407 non-nodes in 17 features. The support vector machine classification method has more benefits in terms of accuracy, sensitivity, and specificity when compared to other classification methods. Since there were some anomalies among both benign and malignant tumors and no discernible difference between them, the distribution of median values revealed that the data was symmetrical in terms of texture and gray scale. Non-small nodules can be identified from benign nodules, but more training is needed to separate them from the other 2 types. Pulmonary nodules are a common disease. MN are distinct from the other 2 types, non-small nodules and benign small nodules, which require further training to differentiate. This has great practical value in teaching practice. Therefore, building a machine learning-based intelligent diagnostic model for pulmonary nodules is of significant importance in helping to solve medical imaging diagnostic problems.

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

Neuroimaging in Pediatric Stroke

Pediatric stroke is unfortunately not a rare condition. It is associated with severe disability and mortality because of the complexity of potential clinical manifestations, and the resulting delay in seeking care and in diagnosis. Neuroimaging plays an important role in the multidisciplinary response for pediatric stroke patients. The rapid development of adult endovascular thrombectomy has created a new momentum in health professionals caring for pediatric stroke patients. Neuroimaging is critical to make decisions of identifying appropriate candidates for thrombectomy. This review article will review current neuroimaging techniques, imaging work-up strategies and special considerations in pediatric stroke. For resources limited areas, recommendation of substitute imaging approaches will be provided. Finally, promising new techniques and hypothesis-driven research protocols will be discussed.

Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis

BACKGROUND

Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance.

METHODS

In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans.

RESULTS

In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives.

CONCLUSIONS

Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. REGISTRATION-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.

Quantification of hematoma and perihematomal edema volumes in intracerebral hemorrhage study: Design considerations in an artificial intelligence validation (QUANTUM) study

BACKGROUND

Hematoma and perihematomal edema volumes are important radiographic markers in spontaneous intracerebral hemorrhage. Accurate, reliable, and efficient quantification of these volumes will be paramount to their utility as measures of treatment effect in future clinical studies. Both manual and semi-automated quantification methods of hematoma and perihematomal edema volumetry are time-consuming and susceptible to inter-rater variability. Efforts are now underway to develop a fully automated algorithm that can replace them. A (QUANTUM) study to establish inter-quantification method measurement equivalency, which deviates from the traditional use of measures of agreement and a comparison hypothesis testing paradigm to indirectly infer quantification method measurement equivalence, is described in this article. The Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage study aims to determine whether a fully automated quantification method and a semi-automated quantification method for quantification of hematoma and perihematomal edema volumes are equivalent to the hematoma and perihematomal edema volumes of the manual quantification method.

METHODS/DESIGN

Hematoma and perihematomal edema volumes of supratentorial intracerebral hemorrhage on 252 computed tomography scans will be prospectively quantified in random order by six raters using the fully automated, semi-automated, and manual quantification methods. Primary outcome measures for hematoma and perihematomal edema volumes will be quantified via computed tomography scan on admission (<24 h from symptom onset) and on day 3 (72 ± 12 h from symptom onset), respectively. Equivalence hypothesis testing will be conducted to determine if the hematoma and perihematomal edema volume measurements of the fully automated and semi-automated quantification methods are within 7.5% of the hematoma and perihematomal edema volume measurements of the manual quantification reference method.

DISCUSSION

By allowing direct equivalence hypothesis testing, the Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage study offers advantages over radiology validation studies which utilize measures of agreement to indirectly infer measurement equivalence and studies which mistakenly try to infer measurement equivalence based on the failure of a comparison two-sided null hypothesis test to reach the significance level for rejection. The equivalence hypothesis testing paradigm applied to artificial intelligence application validation is relatively uncharted and warrants further investigation. The challenges encountered in the design of this study may influence future studies seeking to translate artificial intelligence medical technology into clinical practice.

Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.

Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.

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

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.