▶ Submission of artificial intelligence-based body composition technology that divides organs and body composition in abdominal CT

▶ Introduced ‘Deep Catch’, which can predict and manage body composition-related diseases such as metabolic diseases and sarcopenia

MEDICAL IP participated in ESGAR held from June 13th to 16th in Valencia, Spain.

ESGAR is leading the latest technology trends in gastrointestinal and abdominal radiology in Europe. Through various lectures, discussions, exhibitions, and scientific sessions, it is an academic society that deals with medical imaging-based technology, diagnosis, intervention, management of abdominal diseases, and more.

At ESGAR, MEDICAL IP focused on technologies that automatically segment and analyze body composition of the whole body, including organs, in abdominal CT.

A company official said, “The results of quantitative analysis of body components such as bones, muscles, visceral fat, and subcutaneous fat as well as small intestine, large intestine, liver, gallbladder, kidney, stomach, and pancreas in abdominal images can be used as biomarkers for various diseases. Because of this, these technologies have high scalability. With this technology at the forefront, MEDICAL IP was able to find various points of discussion with various medical staff who participated in ESGAR.”

Since various organs are concentrated in the abdomen and there are many soft tissues, there are high hurdles for artificial intelligence learning using medical images, so most AI diagnostic assistant companies use chest images.

On the other hand, MEDICAL IP has developed 'Deep Catch', an artificial intelligence solution that can numerically analyze organs and body composition in not only chest but also abdominal images based on technology that precisely 3-dimensionally divides and quantifies all human body information in medical images. Deepcatch has achieved remarkable results by publishing 22 international SCI journals within 3 years of its launch.

As such, MEDICAL IP stands out in the abdominal field and published three research abstracts during this ESGAR period as follows.

- Development and validation of deep neural network with neutralization techniques for opportunistic screening in CT image: Osteoporosis, sarcopenia, cardiovascular disease, fatty liver

- A preliminary study on the prediction of hepatocellular carcinoma in patients with chronic hepatitis B using heterogeneous data

- Quantitative analysis of liver proton density fat fraction with deep learning on low-dose computed tomography

Through this, we introduced disease prediction and prevention technology using medical images.

CEO Park Sang-joon said, “Early detection and preemptive management are very important for geriatric diseases such as body composition-related metabolic diseases, sarcopenia, and osteoporosis, and ‘Deep Catch’ is an optimized product for this.” With this ESGAR as an opportunity, we will contribute to improving the quality of human life and enter the global market.” said.