Publications by authors named "A Isshiki"
Article Synopsis
- The study focuses on improving the detection and evaluation of liver steatosis (fatty liver) using ultrasound images and advanced machine learning techniques like CNNs.
- The research defined three fatty liver grades (normal, mild, moderate-to-severe) based on MRI measurements and analyzed 30 cases with various ultrasound-derived textures.
- Results showed that using these techniques, liver steatosis could be classified with over 60% accuracy based on the parametric images created from echo-envelope statistics, indicating potential for reliable non-invasive diagnosis.
We aimed to develop machine learning-based predictive models for identifying inappropriate implantable cardioverter-defibrillator (ICD) therapy. Our study included 182 consecutive cases (average age 62.2 ± 4.
View Article and Find Full Text PDFObjective: Factors such as age, vital signs, renal function, Killip class, cardiac arrest, elevated cardiac biomarker levels, and ST deviation predict survival in patients with acute myocardial infarction (AMI). However, the existing risk assessment tools lack comprehensive consideration of catheter-related factors, and short-term prognostic predictors are unknown. This study aimed to clarify in-hospital prognostic predictors in hospitalized patients with AMI.
View Article and Find Full Text PDFBackground: Coronary healed plaques (HPs) reportedly have high vulnerability or show advanced atherosclerosis and a risk of rapid plaque progression. However, the prognosis of stable angina pectoris (SAP) patients with HPs undergoing percutaneous coronary intervention (PCI) remains under-investigated.
Methods and results: We analyzed 417 consecutive lesions from SAP patients undergoing pre- and post-intervention optical coherence tomography (OCT) for which HPs were defined as having a layered appearance.