Purpose Practical challenges are encountered in percutaneous intravascular procedures when applied to markedly angulated branching vessels. Herein, we introduced a folded-loop guidewire remodeling technique–the guidewire-shaping technique–to overcome difficult catheterization. Materials and Methods First, the tip of a 0.014-inch micro-guidewire was manually shaped like a pigtail loop. Second, the shaped guidewire was introduced into the microcatheter and was preloaded into the hollow metal introducer for suitability with the microcatheter hub. Gentle rotation of the guidewire after release from the microcatheter can create the preshaped pigtail loop configuration. On pulling back, the loop loosened, the configuration was changed to a small U-shaped tip, and the guidewire tip was easily introduced into the target artery. Results Between December 2019 and January 2022, the described technique was used in 64 patients (male/female, 49/15; mean age, 66.8 ± 9.5 years) for selective arterial embolization, after failed attempts with the conventional selection technique. The technique was successful in 63/64 patients (98%). The indications of embolization include transcatheter arterial chemoembolization, gastrointestinal bleeding, hemoptysis, trauma-induced bleeding, and tumor bleeding. Conclusion The folded-loop guidewire remodeling technique facilitates the catheterization of markedly angulated branching arteries; when usual catheterization method fails.
BACKGROUND Many COVID-19 patients rapidly progress into respiratory failure with a broad range of severity. Identification of the high-risk cases is critical for early intervention. OBJECTIVE The aim of this study is to develop deep learning models that can rapidly diagnose high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. METHODS We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed model (ACNN) including an artificial neural network for clinical data and a convolution-neural network for 3D CT imaging data is developed to classify high-risk cases with a severe progression (event) from low-risk COVID-19 patients (event-free). RESULTS By using the mixed ACNN model, we could obtain high classification performance using novel coronavirus pneumonia (NCP) lesion images (93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 AUC) and using lung segmentation images (94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC) for event vs. event-free groups. CONCLUSIONS Our study has successfully differentiated high-risk cases among COVID-19 patients using the imaging and clinical features of COVID-19 patients. The developed model is potentially utilized as a prediction tool for intervening active therapy.
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