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With the emergence of Artificial Intelligence (AI) 2.0, computers are now equipped with new creative capabilities and are playing an increasingly significant role in design. The use of AI augmentation has the potential to enhance design performance, however, there is limited research on the acceptance of AI-augmented design. The research gap under consideration in this study is addressed by presenting an acceptance model designed for AI-augmented design. This model integrates a range of variables including perceived privacy risk, enjoyment, perceived value, perceived usefulness, perceived ease of use, perceived behavioral control, social influence, and behavioral intention. The proposed model was validated through a questionnaire survey of 249 designers in China.The results reveal that enjoyment, perceived value, perceived ease of use, perceived behavioral control, and social influence have a significant positive impact on users' intention to use AI-augmented design, while perceived privacy risk has a significant negative impact. Perceived value was found to mediate the relationship between enjoyment and behavioral intention, while perceived behavioral control play a mediation role in the relationship between social influence and behavioral intention.In conclusion, this study highlights the variables that influence the acceptance of AI-augmented design and provides valuable insights into the potential benefits and drawbacks of integrating AI technologies in design. The proposed acceptance model serves as a framework for future research in this area and can guide the development of more user-friendly and effective AI-augmented design tools and technologies.

BackgroundBrain edema is a severe complication in patients with large vessel occlusion (LVO) that can reduce the effectiveness of endovascular therapy (EVT). This study aimed to investigate the association of the perfusion profile at baseline computed tomography (CT) perfusion with rapidly progressing brain edema (RPBE) after EVT in patients with acute anterior LVO.MethodsWe retrospectively reviewed consecutive data collected from 149 patients with anterior LVO who underwent EVT at our center. Brain edema was measured by the swelling score (0–6 score), and RPBE was defined as the swelling score increased by more than 2 scores within 24 h after EVT. We investigated the effect of RPBE on poor outcomes [National Institute of Health Stroke Scale (NIHSS) score and modified Rankin scale (mRS) score at discharge, the occurrence of hemorrhagic transformation, and mortality rate in the hospital] using the Mann–Whitney U-test and chi-square test. A multivariate logistic regression model was used to assess the relationship between perfusion imaging parameters and RPBE occurrence.ResultsOverall, 39 patients (26.2%) experienced RPBE after EVT. At discharge, RPBE was associated with higher NIHSS scores (Z = 3.52, 95% CI 2.0–12.0, P < 0.001) and higher mRS scores (Z = 3.67, 95% CI 0.0–1.0, P < 0.001) including the more frequent occurrence of hemorrhagic transformation (χ2 = 22.17, 95% CI 0.29–0.59, P < 0.001) and higher mortality rates in hospital (χ2 = 9.54, 95% CI 0.06–0.36, P = 0.002). Univariate analysis showed that intravenous thrombolysis, baseline ischemic core volume, and baseline mismatch ratio correlated with RPBE (all P < 0.05). After dividing the mismatch ratio into quartiles and performing a chi-square test between quartiles, we found that the occurrence of RPBE in Q4 (mismatch ratio > 11.3) was significantly lower than that in Q1 (mismatch ratio ≤ 3.0) (P < 0.05). The result of multivariate logistic regression analysis showed that compared with baseline mismatch ratio 11.3 (OR:5.26, 95% CI 1.28–20.00, P = 0.021) were independent protective factors for RPBE.ConclusionIn patients with anterior circulation LVO stroke undergoing successful EVT, a large mismatch ratio at baseline is a protective factor for RPBE, which is associated with poor outcomes.

Background: Rapid recognition of acute stroke and large vessel occlusion (LVO) is essential in prehospital triage for timely reperfusion treatment. Objective: This study aimed to develop and validate a new screening tool for both stroke and LVO in an urban Chinese population. Methods: This study included patients with suspected stroke who were transferred to our hospital by emergency medical services between July 2017 and June 2021. The population was randomly partitioned into training (70%) and validation (30%) groups. The Staring-Hypertension-atrIal fibrillation-sPeech-weakneSs (SHIPS) scale, consisting of both clinical and medical history information, was generated based on multivariate logistic models. The predictive ability of the SHIPS scale was evaluated and compared with other scales using receiver operating characteristic (ROC) curve comparison analysis. Results: A total of 400 patients were included in this analysis. In the training group ( n = 280), the SHIPS scale showed a sensitivity of 90.4% and specificity of 60.8% in predicting stroke and a sensitivity of 75% and specificity of 61.5% in predicting LVO. In the validation group ( n = 120), the SHIPS scale was not inferior to Stroke 1-2-0 ( p = 0.301) in predicting stroke and was significantly better than the Cincinnati Stroke Triage Assessment Tool (C-STAT; formerly CPSSS) and the Prehospital Acute Stroke Severity scale (PASS) (all p