Lunit

Lunit is the first-ever, Real-time Imaging AI Analytics on the Web. Lunit is an AI-powered medical image analysis software company. Founded in 2013, Lunit develops advanced medical image analytics and novel imaging biomarkers via cutting-edge deep learning technology, in order to empower healthcare practitioners to make more accurate, consistent, and efficient clinical decisions.

Lunit INSIGHT is web-based medical image diagnostic software, developed using cutting-edge deep learning technology. DIB technology is an imaging biomarker derived from large-scale medical image data. Letting the machine define important diagnostic features by itself without guidance from previously established medical criteria (defined by humans) is key to Lunit deep learning technology. Invented in 1900, chest radiography is mainly used to evaluate the lungs and heart, help diagnose and monitor treatment response for a variety of diseases such as pneumonia, tuberculosis and lung cancer.

Albeit an old technology, it remains to take an integral part of clinical decisions made by physicians worldwide. Lunit research aims to use technology to understand lesions on chest x-rays in depth and devise better models of lesion morphology in order to improve the overall diagnostic performance of chest radiography interpretation. It seeks to provide earlier detection of breast cancer, and is also used to assess extent of disease and treatment response. Despite technological advances to enhance mammography, there many false-negative cases especially in subjects with dense breast. A significant rate of false-positive cases associated with mammography is also problematic as there are many women are subjected to unnecessary painful invasive procedures due to the erroneous result on the mammography, not to mention the psychological distress they have to experience.

Lunit research aims to apply advanced algorithms to analyze mammography images in fine detail and develop improved detection models for malignant features in order to significantly decrease false negative and false positive results. Pathology is the pillar of the final diagnosis process in medicine. Visualized in its most basic unit, the cell, within its community, the tissue, diseases are assessed in conclusion on their identification and state. Much improvement in the diagnostic process by application of analytics is feasible; moreover, with the recent advent of digital pathology, the applicability of analytics has been greatly enhanced. The purpose of Lunit research in digital pathology is to objectively define the myriad morphological features in tissue samples and innovate the accuracy, efficiency, and consistency of histopathological diagnosis.