Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm

Naganuma, Masaki; Tachibana, Akira; Fuchigami, Takuya; Akahori, Sadato; Okumura, Shuichiro; Yi, Kenichiro; Matsuo, Yoshimasa; Ikeno, Koichi; Yonehara, Toshiro

doi:10.1016/j.jstrokecerebrovasdis.2021.105791

Cited by 27 publications

(21 citation statements)

References 28 publications

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“…Since the signs are too faint to be observed, the confidence for lesion determination may vary with the readers’ experience. Various studies have shown that only modest to moderate interrater agreement was achieved for determining NCCT-ASPECTS, and the ICC of radiologists ranged from 0.579–0.936 [ 21 , 23 , 24 , 28 , 48 , 49 ]. In contrast, in our study, the ASPECTS reliability (ICC 0.980) of the radiologists with AI assistance is significantly increased and help to improve the diagnostic confidence and medical quality consistency.…”

Section: Discussionmentioning

confidence: 99%

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: 99%

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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“…The automated APECTS scoring for acute ischemic stroke has been reported using several other AI softwares, such as Syngo.via Frontier ASPECT Score Prototype V2 (Siemens Healthineers, Erlangen, Germany), Brainomix e-ASPECTS ® (Brainomix Ltd., Oxford, UK) and RAPID ASPECTS (iSchemaView, Inc., Menlo Park, CA, USA). Although the reading accuracy of these AI systems was shown to be comparable to that of physicians [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 19 ], these reports were limited to a population of relatively severe and extensive infarction, with the majority of cases including clot retrieval therapy and LVO [ 14 , 15 , 16 ]. In addition, neither physicians nor the AI software had a high reading accuracy for infarcts in the perforating branch region, where relatively mild cases are common [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…As a means of addressing such issues, a head CT image reading software utilizing artificial intelligence (AI) was recently developed, and its reading accuracy of ASPECTS was found to be equivalent to that of stroke specialists in cerebral infarction [ 10 , 11 , 12 , 13 , 14 ]. However, studies have examined the usefulness of the software in determining the indication for endovascular therapy, mainly in cases where the target patient underwent endovascular therapy or in cases of large vessel occlusion (LVO) [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy

Shibata

Sakurai

Keiji

et al. 2022

Neurology International

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Purpose: This study aimed to investigate the accuracy and clinical significance of an artificial intelligence (AI)-based automated Alberta Stroke Program Early Computed Tomography (ASPECT) scoring software of head CT for the indication of intravenous recombinant tissue plasminogen activator (rt-PA) therapy. Methods: This study included two populations of acute ischemic stroke: one comprised patients who had undergone head CT within 48 h of presentation (Population #1, n = 448), while the other included patients within 4.5 h from onset (Population #2, n = 132). The primary endpoint was the concordance rate of ASPECTS of the neurologists and AI software against the benchmark score. The secondary endpoints were to validate the accuracy of the neurologist and AI software in assessing the ability to rule out extensive infarction (ASPECTS of 0–5) in population #2. Results: The reading accuracy of AI software was comparable to that of the board-certified vascular neurologists. The detection rate of cardiogenic cerebral embolism was better than that of atherothrombotic cerebral infarction. By excluding extensive infarction, AI-software showed a higher specificity and equivalent sensitivity compared to those of experts. Conclusions: The AI software for ASPECTS showed convincing agreement with expert evaluation and would be supportive in determining the indications of intravenous rt-PA therapy.

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“…In recent years, automated ASPECTS methods based on machine learning have been proposed to provide objective and fast recommendations of scores based on the NCCT data. Their performances were also reported to be comparable to experienced neuroradiologists (Hoelter et al, 2020;Naganuma et al, 2021). Hoelter et al concluded that among the three most common scoring software on the market (Syngo.via Frontier ASPECT Score Prototype V2, Brainomix e-ASPECTS and RAPID ASPECTS), the highest correlation (r = 0.871, p < .001) was found between expert reading and Brainomix (e-ASPECTS software), followed by Frontier V2, then RAPID (Hoelter et al, 2020).…”

Section: Introductionmentioning

confidence: 88%

Deep learning derived automated ASPECTS on non‐contrast CT scans of acute ischemic stroke patients

Cao

Song

et al. 2022

Human Brain Mapping

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Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers.In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients.First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings.Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of Zehong Cao, Jiaona Xu, and Bin Song contributed equally to this work.

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Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm

Cited by 27 publications

References 28 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

The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy

Deep learning derived automated ASPECTS on non‐contrast CT scans of acute ischemic stroke patients

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