Comparison of automated and manual DWI-ASPECTS in acute ischemic stroke: total and region-specific assessment

Cheng, Xiaoqing; Su, Xiaoyu; Shi, JiaQian; Liu, QuanHui; Zhou, Changsheng; Zheng, Donghai; Xing, Wei; Lü, Haitao; Pan, ChengWei; Li, Xiuli; Yu, Yizhou; Zhang, Long Jiang; Lu, Guangming

doi:10.1007/s00330-020-07493-2

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…As shown in Fig. 3 B, the lesion regions in our study mainly scatter in M5 ( n = 24), IC ( n = 24), L ( n = 18) and M2 ( n = 16), and it has been demonstrated that IC and M5 behave lower agreement to the ground truth and higher rate of missed diagnosis [ 52 ]. Thirdly, to align with the emergency setting, the participant raters in this study are general radiologists rather than neuroradiologists.…”

Section: Discussionmentioning

confidence: 87%

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

Chen

Ren

et al. 2022

Insights Imaging

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Objective This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS). Methods Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters’ reading performance. Results In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4 ± 93.6 min and NIHSS of 5 (IQR 2–10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846–0.907) and 0.729 (CI 0.679–0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters’ average sensitivities and specificities from 0.254 (CI 0.22–0.26) and 0.896 (CI 0.884–0.907), to 0.333 (CI 0.301–0.345) and 0.915 (CI 0.904–0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7 s (p = 0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980. Conclusions With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.

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Section: Discussionmentioning

confidence: 87%

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

Chen

Ren

et al. 2022

Insights Imaging

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“…However, the study lacks external validation and reperfusion effect studies, as well as patients with old cerebral infarction and cerebral hemorrhage, which may interfere with the classification outcomes of the model. DWI-ASPECTS is derived from CT-ASPECTS as a tool to semiquantify early ischemic alterations [41,80,81]. Innovatively, using diffusion-weighted imaging (DWI), Do et al developed recurrent residual convolutional neural network (RRCNN) algorithm for the automatic binary classification of the ASPECTS in acute stroke patients with an AUC of 94.1%, indicating that the performance is better than 3DCNN.…”

Section: Automated Aspects Calculationmentioning

confidence: 99%

“…DWI-ASPECTS is derived from CT-ASPECTS as a tool to semiquantify early ischemic alterations [ 41 , 80 , 81 ]. Innovatively, using diffusion-weighted imaging (DWI), Do et al developed recurrent residual convolutional neural network (RRCNN) algorithm for the automatic binary classification of the ASPECTS in acute stroke patients with an AUC of 94.1%, indicating that the performance is better than 3DCNN.…”

Section: Clinical Applications Of Deep Learning In Aismentioning

confidence: 99%

“…Cheng et al developed a deep learning-based automatic software tool (eDWI-ASPECTS) which was equivalent to the diagnostic efficiency of senior neuroradiologists in the evaluation of 10 individual ASPECTS regions, although there are uncertainties in the scoring rules of DWI-ASPECTS. Compared to CT, the initial description of the score is not yet clear [ 81 ]. In conclusion, the criteria for DWI-ASPECTS evaluation will be necessary for the future.…”

Section: Clinical Applications Of Deep Learning In Aismentioning

confidence: 99%

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Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Cui

Fan

Yang

et al. 2022

BioMed Research International

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Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

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“…Many patients with COVID-19 do relatively well in the first week after onset but then (especially high-risk patients) may experience an acute exacerbation, often with ARDS or other sequelae in the second week. Liu and colleagues [ 31 ] reviewed the clinical histories of 276 patients and selected 24 patients meeting predefined criteria of confirmed COVID-19 pneumonia. They noted clinical exacerbation (defined as moving from the general ward to the intensive care unit) at 8.8 days after disease onset.…”

Section: Time Course Of Covid-19: Acute Exacerbation and Complicationsmentioning

confidence: 99%

COVID-19: Zinc and Angiotensin-Converting Enzyme 2 (ACE2) Deficiencies as Determinants of Risk and Severity of Disease: A Narrative Review

Salgo¹

2021

Infect Dis Ther

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Comparison of automated and manual DWI-ASPECTS in acute ischemic stroke: total and region-specific assessment

Cited by 11 publications

References 20 publications

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

COVID-19: Zinc and Angiotensin-Converting Enzyme 2 (ACE2) Deficiencies as Determinants of Risk and Severity of Disease: A Narrative Review

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