SMART LAB

Research

Data-driven Clinical outcome Prediction

Total 3건 1 페이지
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Finding integrated imaging biomarker for Cardiac disease
AI-based Cardiac Failure Risk Prediction with Cardiac CT/MRI
  • Cardiac MR Image (CMR) is cross-sectional and objective image phenotype information for the entire heart area, so it is possible to quantitatively analyze left ventricle (LV) remodeling and myocardial fibrosis.
  • CMR contains information on the movement of the heart between the systolic-diastolic phases, and by analyzing it, functional analysis of cardiac images such as the ejection fraction or cardiac output is possible.
  • CMR provides the complex interplay between MV/LV functional (motion, systolic function and strain in CINE sequence), structural (analysis of mitral valve (MV) apparatus and LV abnormal findings and MV copatation depth, tenting area, LVSV, etc.), tissue (LV remodeling causes in myocardium change according to-LGE quantification or T1 value in LGE or T1 map sequence) abnormalities.
  • Thus, it is expected that an imaging biomarker that has been difficult to analyze can be derived through AI-based CMR analysis.
  • By deriving CMR-based imaging biomarkers of patients, it is expected that the effects of LV remodeling and infarction on treatment response or long-term prognosis and direct cardiac function can be newly defined.
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Finding integrated imaging biomarker for Infectious Diseases
AI-based Prognostic Solution for Infectious Diseases
  • During the COVID-19 pandemic, when a new infectious disease spreads, it is important to detect the disease early and implement appropriate control policy to minimize damage.
  • It is expected to be able to quickly diagnose infectious diseases and predict the prognosis of patients with infectious diseases in the early stages of infection by fusion technologies such as artificial intelligence and big data.
  • AI technology can comprehensively analyze/learn a specific pattern of infectious disease images of various patients. This enables the artificial intelligence to efficiently discern the subtle changes in the ROI where infectious diseases are suspected in the image.
  • For initial response to infectious diseases in local governments/hospitals, AI technology to assist epidemiological investigations of infectious diseases can be developed to increase the ability to respond to infectious diseases early.
  • By applying an infectious disease prognosis prediction solution using AI in clinical practice, it improves the efficiency of distribution of medical resources by predicting patients at risk of severe/death, and lowers the disease transmission rate and mortality rate of infectious disease patients by providing customized medical services.
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Deep Learning-based Tumor Response Assessment

To verify its effectiveness after anticancer treatment, criteria for assessing tumor reactions to treatments were presented, and some of standards are being used with constant development and improvement. However, while evaluation criteria have been revised steadily with an empirical and statistical approach, methods of measuring the condition of a tumor is relatively classic and usually depends on people or tools.

The most widely used criteria as of 2020 is RECIST1.1. After ‘WHO Criteria’ was first published in 1981, followed by Response Evaluation Criteria in Solid Tumors (RECIST) by research groups from Europe and North America in 2000. Finally, after some revised, RECIST1.1 was announced in 2009. It is mainly used for the purpose of assessing the effectiveness of surgery, chemotherapy, and radiation therapy. 

The purpose of this study is to evaluate the treatment response of solid cancers more objectively, accurately and quickly than conventional methods by measuring and classifying the tumor’s condition using deep learning based on RECIST1.1. Furthermore, the ultimate goal is to improve existing assessment criteria and present new standards to provide optimal treatment for each patients.