Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study.

Background: Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows. Methods: This multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC). Results: For ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06. Conclusions: The AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.

Chinese Tofu-Inspired Biomimetic Conductive and Transparent Fibers for Biomedical Applications.

Conductive fibers are vital for next-generation wearable and implantable electronics. However, the mismatch of mechanical, electrical, and biological properties between existing conductive fibers and human tissues significantly retards their further development. Here, the concept of neuro-like fibers to meet these aforementioned requirements is proposed. A new wet spinning process is established to continuously produce pure gelatin hydrogel fibers. The key is the controllable and rapid gelation of spinning solutions based on the salting-out effect, which is inspired by the Chinese food tofu. The resultant fibers exhibit neuro-like features of soft-while-strong mechanical properties, high ionic conductivity, and superior biological properties including biodegradability, biocompatibility, and edibility, which are crucial for implanted applications but seldom reported. Furthermore, all-weather suitable neuro-like fibers with excellent anti-freezing and water retention properties are developed by introducing glycerol for wearable applications. The optical fiber, transient electronics, and electronic data glove made of neuro-like fibers profoundly demonstrate their potential in biomedical applications.

How multisensory perception promotes purchase intent in the context of clothing e-customisation.

With the continuing development of internet technologies, an increasing number of consumers want to customise the products they buy online. In order to explore the relationship between perception and purchase intent, a conceptual framework was developed that was based on the link between multisensory perception, positive emotions, and purchase intent in fashion e-customisation marketing. We discuss the outcomes derived from consumers' experiences in fashion e-customisation and analyse the relationships between variables. Questionnaires were used to collect data for this quantitative study (n = 398 participants). The data was analysed using factor analysis, correlation analysis, and regression analysis. The findings contribute to the field of clothing e-customisation by identifying the effects of visual perception, haptic imagery, and auditory stimulation on arousal, and purchase intent. Visual perception and haptic imagery exerted a positive influence over dominance. We also identify the effects of arousal and dominance on purchase intent, and assess the mediating effects of these variables on visual perception, haptic mental imagery, and purchase intent. The results highlight how fashion e-customisation marketing strategies can be adopted by managers in order to increase positive emotions and how multisensory perception can potentially be used to influence consumers' purchase behaviour.