Ischemic stroke subtyping method combining convolutional neural network and radiomics

Chen, Yang; He, Yaohua; Jiang, Zhuoyun; Xie, Yuanzhong; Nie, Shengdong

doi:10.3233/xst-221284

Cited by 5 publications

(2 citation statements)

References 23 publications

(28 reference statements)

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“…In their results, the best AUC was 0.9018 and the best Acc was 0.8929. They concluded that radiomics can effectively predict the subtype of ischemic stroke and help doctors to correctly manage these patients [56]. Jiang J et al also studied a clot-based model in 403 AIS patients to identify cardioembolic (CE) stroke before MTB.…”

Section: Etiology Predictionmentioning

confidence: 99%

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

Porto-Álvarez,

Martínez,

Martínez Fernández

et al. 2023

Applied Sciences

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Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. The field of radiological imaging has experienced growth in recent years, which could be boosted by the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology and has shown promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years, and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorize how radiomics may change the paradigm for these patients in the coming years.

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Section: Etiology Predictionmentioning

confidence: 99%

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

Porto-Álvarez,

Martínez,

Martínez Fernández

et al. 2023

Applied Sciences

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“…However, the rapidly developing field of radiomics is providing new pathways for diagnosis and treatment decision-making. For instance, a recent study illustrated that high-dimension radiomics features derived from CT angiography enhances the accuracy of AIS diagnosis and subtype classification, which is crucial for timely intervention ( 30 ). Additionally, another study has shown that radiomic models based on apparent diffusion coefficient (ADC) map are highly effective in identifying the ischemic penumbra, which is important in assessing the normal and ischemic penumbra areas and influencing treatment decisions ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

Kong,

Zhang

2024

Front. Neurol.

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BackgroundPre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients.MethodsWe systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients.ResultsSixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84–0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74–0.86) for predicting reperfusion in AIS.ConclusionRadiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.

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Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management

Chaki,

Woźniak

2024

IEEE Access

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Brain stroke is a complicated disease that is one of the foremost reasons of long-term debility and mortality. Because of breakthroughs in Deep Learning (DL) and Artificial Intelligence (AI) which enable the automated detection and diagnosis of brain stroke as well as intelligently assisting post-brain stroke patients for rehabilitation, is more favorable than a manual diagnosis. Many publications on automated brain stroke detection, diagnosis, and robotic management using DL and AI approaches are now being published. This review provides a study of the detection, diagnosis of brain stroke and robotic management techniques of post-brain stroke rehabilitation from six different perspectives, namely, brain stroke datasets and modalities of brain stroke data collection, pre-processing approaches, DL-based detection and diagnosis of brain stroke, Al-based intelligent post brain stroke rehabilitation assistant, and performance measures. It also examines the conclusions and the consequences of the findings. There are also three ongoing research challenges in the fields of brain stroke detection and diagnosis, as well as post-brain stroke robotic treatment. For this investigation, 130 key papers from the Scopus, PubMed and Web of Science archives were chosen after a comprehensive screening method. This study gives a comprehensive overview of brain stroke detection and post-brain stroke robotic management strategies that may be useful to the scientist's community working in the field of automatic brain stroke detection and robotic rehabilitation management.

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Ischemic stroke subtyping method combining convolutional neural network and radiomics

Cited by 5 publications

References 23 publications

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management

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