Leveraging artificial intelligence in ischemic stroke imaging

Automatic assessment of DWI-ASPECTS for acute ischemic stroke based on deep learning

BACKGROUND

Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-quantitative method for early ischemic changes in acute ischemic stroke.

PURPOSE

However, ASPECTS is still affected by expert experience and inconsistent results between readers in clinical. This study aims to propose an automatic ASPECTS scoring model based on diffusion-weighted imaging (DWI) mode to help clinicians make accurate treatment plans.

METHODS

Eighty-two patients with stroke were included in the study. First, we designed a new deep learning network for segmenting ASPECTS scoring brain regions. The network is improved based on U-net, which integrates multiple modules. Second, we proposed using hybrid classifiers to classify brain regions. For brain regions with larger areas, we used brain grayscale comparison algorithm to train machine learning classifiers, while using hybrid feature training for brain regions with smaller areas.

RESULTS

The average DICE coefficient of the segmented hindbrain area can reach 0.864. With the proposed hybrid classifier, our method performs significantly on both region-level ASPECTS and dichotomous ASPECTS. The sensitivity and accuracy on the test set are 95.51% and 93.43%, respectively. For dichotomous ASPECTS, the intraclass correlation coefficient (ICC) between our automated ASPECTS score and the expert reading was 0.87.

CONCLUSIONS

This study proposed an automated model for ASPECTS scoring of patients with acute ischemic stroke based on DWI images. Experimental results show that the method of segmentation first and then classification is feasible. Our method has the potential to assist physicians in the Alberta Stroke Program with early CT scoring and clinical stroke diagnosis.

The prognostic value of ASPECTS in specific regions following mechanical thrombectomy in patients with acute ischemic stroke from large-vessel occlusion

Objective

The aim of this study is to investigate the relationship between the volume of specific regional infarction and the prognosis of patients who undergo mechanical thrombectomy (MT) for acute large vessel occlusion.

Methods

In this study, we collected the clinical and imaging features of patients with unilateral acute anterior circulation ischemic stroke from January 2021 to June 2023 in the Second Affiliated Hospital of Nanchang University. All patients underwent CT perfusion and non-contrast CT scan before MT. The ASPECTS was assessed based on imaging data, and artificial intelligence was used to obtain the percentage of infarction in each of the 10 regions of ASPECTS. According to the modified Rankin Scale, the patients were divided into the good prognosis group and poor prognosis group at the 90-day follow-up. Various indicators in the two groups were compared. Multivariable logistic regression was used to assess the risk factors for poor prognosis. The relationship between core infarction volume and the probability of poor prognosis was plotted to analyze the trend of poor prognosis with changes in the proportion of infarction volume. Finally, a receiver operating characteristic curve was constructed to analyze the predictive ability on poor prognosis.

Results

A total of 91 patients were included, with 58 patients having a good prognosis (mRS ≤ 2) and 33 patients having a poor prognosis (mRS ≥ 3). Multivariate analysis showed that NIHSS score and core infarction involving the internal capsule and M6 region were independent risk factors for poor prognosis. According to the linear correlation, a higher ratio of core infarction volume in the internal capsule or M6 region was linked to an increased risk of a poor prognosis. However, the non-linear analysis revealed that the prognostic impact of core infarction volume was significant when the ratio was greater than 69.7%. The ROC curve indicated that the combination of NIHSS score, infarct location, and the ratio of infarct volume has an AUC of 0.87, with a sensitivity of 84.8% and a specificity of 84.5%.

Conclusion

It is important to examine the location and volume of the infarct in the internal capsule and M6 when deciding whether to do a MT.

A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke

Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.

Artificial intelligence applied in acute ischemic stroke: from child to elderly

This review will summarize artificial intelligence developments in acute ischemic stroke in recent years and forecasts for the future. Stroke is a major healthcare concern due to its effects on the patient's quality of life and its dependence on the timing of the identification as well as the treatment. In recent years, attention increased on the use of artificial intelligence (AI) systems to help categorize, prognosis, and to channel these patients toward the right therapeutic procedure. Machine learning (ML) and in particular deep learning (DL) systems using convoluted neural networks (CNN) are becoming increasingly popular. Various studies over the years evaluated the use of these methods of analysis and prediction in the assessment of stroke patients, and at the same time, several applications and software have been developed to support the neuroradiologists and the stroke team to improve patient outcomes.

Exercise Intervention for Alzheimer's Disease: Unraveling Neurobiological Mechanisms and Assessing Effects

Alzheimer's disease (AD) is a progressive neurodegenerative disease and a major cause of age-related dementia, characterized by cognitive dysfunction and memory impairment. The underlying causes include the accumulation of beta-amyloid protein (Aβ) in the brain, abnormal phosphorylation, and aggregation of tau protein within nerve cells, as well as neuronal damage and death. Currently, there is no cure for AD with drug therapy. Non-pharmacological interventions such as exercise have been widely used to treat AD, but the specific molecular and biological mechanisms are not well understood. In this narrative review, we integrate the biology of AD and summarize the knowledge of the molecular, neural, and physiological mechanisms underlying exercise-induced improvements in AD progression. We discuss various exercise interventions used in AD and show that exercise directly or indirectly affects the brain by regulating crosstalk mechanisms between peripheral organs and the brain, including "bone-brain crosstalk", "muscle-brain crosstalk", and "gut-brain crosstalk". We also summarize the potential role of artificial intelligence and neuroimaging technologies in exercise interventions for AD. We emphasize that moderate-intensity, regular, long-term exercise may improve the progression of Alzheimer's disease through various molecular and biological pathways, with multimodal exercise providing greater benefits. Through in-depth exploration of the molecular and biological mechanisms and effects of exercise interventions in improving AD progression, this review aims to contribute to the existing knowledge base and provide insights into new therapeutic strategies for managing AD.

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

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.

Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke

Purpose

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was designed for semi-quantitative assessment of early ischemic changes on non-contrast computed tomography (NCCT) for acute ischemic stroke (AIS). We evaluated two automated ASPECTS software in comparison with reference standard.

Methods

NCCT of 276 AIS patients were retrospectively reviewed (March 2018–June 2020). A three-radiologist consensus for ASPECTS was used as reference standard. Imaging data from both baseline and follow-up were evaluated for reference standard. Automated ASPECTS were calculated from baseline NCCT with 1-mm and 5-mm slice thickness, respectively. Agreement between automated ASPECTS and reference standard was assessed using intra-class correlation coefficient (ICC). Correlation of automated ASPECTS with baseline stroke severity (NIHSS) and follow-up ASPECTS were evaluated using Spearman correlation analysis.

Results

In score-based analysis, automated ASPECTS calculated from 5-mm slice thickness images agreed well with reference standard (software A: ICC = 0.77; software B: ICC = 0.65). Bland–Altman analysis revealed that the mean differences between automated ASPECTS and reference standard were ≤ 0.6. In region-based analysis, automated ASPECTS derived from 5-mm slice thickness images by software A showed higher sensitivity (0.60 vs. 0.54), lower specificity (0.91 vs. 0.94), and higher AUC (0.76 vs. 0.74) than those using 1-mm slice thickness images (p < 0.05). Automated ASPECTS derived from 5-mm slice thickness images by software B showed higher sensitivity (0.56 vs. 0.51), higher specificity (0.87 vs. 0.81), higher accuracy (0.80 vs. 0.73), and higher AUC (0.71 vs. 0.66) than those using 1-mm slice thickness images (p < 0.05). Automated ASPECTS were significantly associated with baseline NIHSS and follow-up ASPECTS.

Conclusion

Automated ASPECTS showed good reliability and 5 mm was the optimal slice thickness.

Cavitation-assisted sonothrombolysis by asymmetrical nanostars for accelerated thrombolysis

Sonothrombolysis with recombinant tissue plasminogen activator (rtPA) and microbubbles has been widely studied to enhance thrombolytic potential. Here, we report different sonothrombolysis strategy in nanoparticles using microbubbles cavitation. We found that different particles in shape exhibited different reactivity toward the cavitation, leading to a distinct sonothrombolytic potential. Two different gold nanoparticles in shape were functionalized with the rtPA: rtPA-functionalized gold nanospheres (NPt) and gold nanostars (NSt). NPt could not accelerate the thrombolytic potential with a sole acoustic stimulus. Importantly, NSt enhanced the potential with acoustic stimulus and microbubble-mediated cavitation, while NPt were not reactive to cavitation. Coadministration of NSt and microbubbles resulted in a dramatic reduction of the infarcts in a photothrombotic model and recovery in the cerebral blood flow. Given the synergistic effect and in vivo feasibility of this strategy, cavitation-assisted sonothrombolysis by asymmetrical NSt might be useful for treating acute ischemic stroke.

Emerging Applications of Radiomics in Neurological Disorders: A Review

Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.