AI technology leads to a paradigm shift in body composition analysis
Launched ‘Deep Catch’, a CT-based AI whole body composition segmentation and analysis solution
▶ Accuracy of 97%... Can be used in a wide range of research fields such as cancer, obesity, liver disease, metabolic syndrome, sarcopenia, and osteoporosis
▶ "We will lead the market with innovative products that increase the efficiency and accuracy of body composition testing and diversify the use of medical data"
MEDICAL IP's AI medical image segmentation technology is expected to radically change the body composition analysis paradigm. MEDICAL IP has released 'DeepCatch', a CT-based AI whole-body body composition analysis software.
DeepCatch is a product that automatically analyzes body composition from whole-body CT images. It divides body components such as skin, bone, muscle, visceral fat, subcutaneous fat, and organs.
Users can obtain visual and numerical information on body composition, and a body composition analysis report is automatically generated.
In particular, the accuracy reaches 97%, which can supplement the existing body composition test method. In addition, medical staff were able to obtain more accurate base data when conducting research related to body composition.
The company said, "DeepCatch is the focus of the company's core technologies such as AI deep learning, medical image 3D modeling, and segmentation technology."
DeepCatch enables hospitals to use a patient's CT for body composition analysis to enhance the value of medical data. From the patient's point of view, the exact body composition can be confirmed at the same time as the CT scan. Therefore, DeepCatch is a beneficial solution for both hospitals and patients.