Medical-Grade Science
Backed by Research
Discover the rigorous clinical validation and innovative technology that powers Fecal Scanner Plus.
Trusted by Researchers
Validated at Leading Medical Institutions
About AICU Global
Transforming healthcare through clinically validated AI-powered self-monitoring solutions
Our Mission
AICU is a medical AI company developing innovative healthcare solutions that enable self-monitoring, diagnosis, and prediction of diseases using clinically validated medical data and vision AI deep learning technology. We enable users to easily and affordably check their health status in everyday life, prevent disease and detect risks early through continuous health monitoring, and deliver personalized health management for better well-being—especially in an aging society and the era of single-person households.
Our Story
AICU was founded to address the critical gap in accessible, continuous health monitoring for chronic disease management. Traditional medical testing requires expensive hospital visits and lab tests, leaving patients unable to monitor their condition between appointments. We recognized that emerging AI and smartphone technology could enable medical-grade monitoring from home, making preventive care accessible to everyone. Our vision is to transform reactive healthcare into proactive health management through clinically validated, AI-powered self-monitoring solutions.
Company Facts
Founded
2021
Headquarters
New Jersey, USA
R&D Center: Daegu, South Korea
Team Size
12 employees
Focus Areas
AI/Machine Learning for Healthcare, Inflammatory Bowel Disease Research, Medical Device Software, Digital Health
Funding
Seed-funded by Center for Creative Economy and Innovation Gyeongnam
Clinical Validation
2 Patents Granted/Pending
Published in The American Journal of Gastroenterology
AI Trained on 10,000+ Medical Images
4+ Years of Research
Medical Advisory Board
Guided by leading experts in gastroenterology and medical research
Dr. Eunsu Kim, M.D.
Professor of Gastroenterology
- • Kyungpook National University Hospital
- • Chair of Microbiome Research Committee
- • Over 50 SCI publications on IBD
Dr. Jaechan Park, M.D.
Neurosurgeon
- • Director of Biomedical Research Institute
- • Kyungpook National University
- • 121 SCI publications in cerebrovascular research
Dr. Kyunghoon Kang, M.D.
Professor of Neurology
- • Kyungpook National University
- • Over 30 SCI publications on neurodegenerative diseases
Protected Innovation
Our proprietary technology is protected by patents covering advanced AI methods for stool image analysis and disease prediction.
Patent Number
US 18/220,022
THE SYSTEM AND METHOD FOR STOOL IMAGE ANALYSIS
Filing Date
July 25, 2023
Inventors
Eun Soo KIM, Sung Moon JEONG, Dong Won WOO
This patent covers our proprietary method for analyzing fecal samples using machine learning algorithms to detect UC
Patent Number
KR 1020220085663
The system and method for stool image analysis
Filing Date
July 12, 2022
Inventors
Eun Soo KIM, Sung Moon JEONG, Dong Won WOO
The present invention relates to a stool image analysis system and method, and more particularly, to a stool image analysis system and method for deriving a user's colonic condition by analyzing the user's stool image using a pre-trained deep learning model.
Peer-Reviewed Research
Our technology is validated through rigorous peer-reviewed research published in leading medical journals.
The American Journal of GASTROENTEROLOGY
Volume 120
Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis
Authors
Jung Won Lee, MD, Dongwon Woo, MS, Kyeong Ok Kim, MD, Eun Soo Kim, MD, PhD, Sung Kook Kim, MD, PhD, Hyun Seok Lee, MD, PhD, Ben Kang, MD, Yoo Jin Lee, MD, Jeongseok Kim, MD, Byung Ik Jang, MD, PhD, Eun Young Kim, MD, PhD, Hyeong Ho Jo, MD, Yun Jin Chung, MD, Hanjun Ryu, MD, Soo-Kyung Park, MD, Dong-Il Park, MD, Hosang Yu, MS and Sungmoon Jeong, PhD
Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.
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