ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps

Zhu, Hongtao; Yang, Chen Die; Tang, Tianyu; Ma, Gao; Zhou, Jiaju; Zhang, Jiulou; Lu, Shanshan; Wu, Fann; Luo, Limin; Liu, Sheng; Ju, Shenghong; Shi, Haibin

doi:10.1016/j.cmpb.2022.106630

Cited by 5 publications

(8 citation statements)

References 30 publications

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“…Eleven studies used CT perfusion source data and parametric maps as model input for core infarct estimation (12,13,29,31,33,42,44,47,50,53,54), including one study generating a synthesized pseudo-DWI map based on CTP parametric maps (42). Five studies used source images and features derived from non-contrast CT (41,52) and CT angiography (15,45,46).…”

Section: Resultsmentioning

confidence: 99%

“…We conducted a sensitivity analysis by removing one study at each step (Figure 3). The adjusted overall DSC score ranged from 0.47 (95% CI 0.41-0.53) after removing the study by Zhu et al (54) to 0.52 (95% CI 0.41-0.63) after removing the study by McKinley et al (25). Publication bias assessed by graphic funnel plot showed an asymmetrical shape, and not all studies were plotted within the area under the curve of the pseudo-95% CI, indicating the potential publication bias among included studies (Figure 4).…”

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

“…For studies applying conventional ML classifiers, the pooled DSC score was 0.44 (95%CI 0.34-0.54) for models inputting MR data (11,25,38) and 0.37 (95%CI 0.33-0.41) for a single model using CT data as a reference (13). For studies developing DL-based approaches, the pooled DSC was 0.45 (95%CI 0.38-0.53) for models inputting MR data (16,40,43) and 0.63 (95%CI 0.48-0.78) when using CT data (31,50,52,54). Sustained high heterogeneity was observed in the subgroup of DL models combined with CT data (Higgins I 2 93.9%, p < 0.001).…”

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

“…Eleven studies were included in the meta-analysis (11,13,16,25,31,38,40,43,50,52,54). Methodological quality assessment of the included studies is shown in Table 2.…”

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

“…Only one study determined model performance using an external test set (13). Imaging data from one study were collected from four major manufacturers (44), five studies reported using single-vendor data (11,25,38,40,54), and others remained unknown (13,16,31,43,52). Although all the included studies clearly defined the validation methods, the relationship between the number of training images and model performance (i.e., sample size estimation) was not carefully evaluated.…”

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

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Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

et al. 2022

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Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Eleven studies meeting the inclusion criteria were included in the quantitative analysis. Study characteristics, model methodology, and predictive performance of the included studies were extracted. A meta-analysis was conducted on the dice similarity coefficient (DSC) score by using a random-effects model to assess the overall predictive performance. Study heterogeneity was assessed by Cochrane Q and Higgins I2 tests. The pooled DSC score of the included ML models was 0.50 (95% CI 0.39–0.61), with high heterogeneity observed across studies (I2 96.5%, p < 0.001). Sensitivity analyses using the one-study removed method showed the adjusted overall DSC score ranged from 0.47 to 0.52. Subgroup analyses indicated that the DL-based models outperformed the conventional ML classifiers with the best performance observed in DL algorithms combined with CT data. Despite the presence of heterogeneity, current ML-based approaches for final infarct prediction showed moderate but promising performance. Before well integrated into clinical stroke workflow, future investigations are suggested to train ML models on large-scale, multi-vendor data, validate on external cohorts and adopt formalized reporting standards for improving model accuracy and robustness.

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Section: Resultsmentioning

confidence: 99%

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

“…Eleven studies were included in the meta-analysis (11,13,16,25,31,38,40,43,50,52,54). Methodological quality assessment of the included studies is shown in Table 2.…”

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

Section: Performance For Core Infarcted Tissue Prediction: a Meta-ana...mentioning

confidence: 99%

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Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

et al. 2022

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Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

Raju,

Neelapu,

Laskar

et al. 2024

Multimed Tools Appl

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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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ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps

Cited by 5 publications

References 30 publications

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

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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