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Naming| 'DeepCatch' to 'catch' body composition through deep learning

[Naming| 'DeepCatch' that catches whole body composition with deep learning]



MEDICALIP has developed 'DeepCatch' that can segment and analyze body composition through CT. It was named after combining artificial intelligence (AI) 'deep' learning and 'catch', which means to accurately capture body composition. DeepCatch's slogan is 'Catch your body compositions'.



DeepCatch was developed using MEDICALIP's unique medical image segmentation technology. In addition to the original technology that accurately divides organs and lesions with AI, you upload a whole-body CT image of the patient and, with one click, 7 structures, including skin, bone, muscle, visceral fat, subcutaneous fat, organs, and cerebrospinal cord, of the human body are perfectly divided. Numerical analysis information is also provided in reports (for general use) and Excel (for experts).

DeepCatch not only provides visual and quantitative information on body but also reaches 97% accuracy of body composition analysis. Medical staff will be able to secure accurate data related to body composition, which was previously difficult to obtain.

The biggest strength is that DeepCatch is a solution that creates 'added value' for medical imaging. Medical staff can conduct CT analysis research using the medical image big data accumulated in hospitals for years.

Patients also get benefits from DeepCatch by using their CT images not only in the relevant department but also to check their muscle mass and fat mass as a visualized quantitative information, so they can receive ancillary medical services as well.

Sang-Jun Park, CEO of MEDICALIP, said, “We developed DeepCatch to change the paradigm of body composition analysis by expanding MEDICAL IP’s AI medical image segmentation technology. It will be an optimal solution to explore how body composition is related to cancer, obesity, and sarcopenia.”

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