CT software DeepCatch

for body composition analysisautomated

Deep learning algorithms enable you to analyze the body composition within a few minutes (3 mins for the whole-body, 2 mins for the abdomen). The distribution of adipose tissues and muscles in 3D offers you valuable medical data for numerous applications. Key Features Automated body compositions segmentation Contrast / Non-contrast, 2D/3D models Overall accuracy: Approx. 97% Automated L3 level, abdominal waist detection User define options You can check accurate body composition and distribution from CT images. Enhanced imaging and non-enhanced imaging are available. When we design artificial intelligence, medical staff participate directly to improve its accuracy through anatomical verifications. Applicable Area INDICATIONS CT analysis study can be used in all diseases related to body composition such as metabolic disease, sarcopenia, and osteoporosis via quantitative analysis of muscle, abdominal fat. visceral fat and bone density. HIGH-RISK GROUP CLASSIFICATION High-risk group classification: based on body composition, the risk of metabolic disease and sarcopenia can be calculated atthe timeofCT scan. providing opportunity for early détection and reducing redundant CT scans for patients. For hospitals. it can help with diversifying ways CT data is utilized and enabling them to treat diseases. Drug development/clinical Can be used as a validation tool for dinical research and drug development in sarcopenia, osteoporosis and metabolic diseases including under stated illness.