I founded our company with a vision of "improve Quality of Life for humanities". I did not just mention human health or longevity since cosmetics industry, pet industry and more are both big pieces of our work. We are playing in healthcare space with our expertise in Data Science combining Bioinformatics and Artificial Intelligence/Machine Learning/Deep Learning.

1. Cardiovascular stenosis segmentation and stent/bypass surgery recommendation using Angiogram
- We gather angiogram from AMC (Asan Medical Center, the largest hospital in Korea. AMC cardiologist team is known as world top class mostly due to Asan chairman Park Seung-jung who is a cardiologist at the same time). 2D Xray angiograms are fed into our "deep network" system. Its output is "whether it has detection" in other words classification, "where it is" aka detection, "how severe it is" in other words segmentation, whether it is 50% or 80% blocked. Syntax score (scoring system of cardiologist and radiologist, below 23: s...
I founded our company with a vision of "improve Quality of Life for humanities". I did not just mention human health or longevity since cosmetics industry, pet industry and more are both big pieces of our work. We are playing in healthcare space with our expertise in Data Science combining Bioinformatics and Artificial Intelligence/Machine Learning/Deep Learning.

1. Cardiovascular stenosis segmentation and stent/bypass surgery recommendation using Angiogram
- We gather angiogram from AMC (Asan Medical Center, the largest hospital in Korea. AMC cardiologist team is known as world top class mostly due to Asan chairman Park Seung-jung who is a cardiologist at the same time). 2D Xray angiograms are fed into our "deep network" system. Its output is "whether it has detection" in other words classification, "where it is" aka detection, "how severe it is" in other words segmentation, whether it is 50% or 80% blocked. Syntax score (scoring system of cardiologist and radiologist, below 23: stent, above 23: bypass) is used in telling stent or bypass. We are currently using Deep Learning, CNN, UNet, PSPNet, etc.

2. Diagnosis pipeline of Pediatric rare genetic disease (epilepsy) from sequencing data
- We work with Pediatric department in SNUH (Seoul National University hospital, #4 largest in Korea). Pediatric department here is known to have the most sequencing data of kids with epilepsy. We are supposed to build pipeline which detects variants in Sequencing data and compares sequencing data and VCF (Variant Call Format, we entered 360,000,000 variants into our library).

3. pet CDSS (Clinical Decision Support System, in other words it is IBM Watson system)
- We are partnering with the largest EMR (Electronic Medical Records) company in Korean animal hosptals. We are working with veterinarians in gathering symptoms, image data, test data and etc. I'm planning to include Genomics data as one of input parameters.

4. 10 grading system of the wrinkles at the corners of eyes
- We work with skin standardization institute in Korea. Human specialists have a tendency of bias or lack of reproducibility in telling grades of wrinkles. We are using Deep Learning, specifically CNN, VGG, etc.
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Sanggoo Kang
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Sanggoo Kang CEO Well-rounded engineer and business, bio, ICT and Data Science guru