一 First live-demonstration showcase to be held at RSNA 2017 Lunit booth, North Hall, B #8164
一 Top-notch AI technology with 97% standalone accuracy in nodule detection, 99% for consolidation and pneumothorax
一 Available free to the public; upload medical images and get results in a few seconds
Lunit, an AI-powered medical image analysis software company, is returning to RSNA 2017 in its second year with a new and advanced, cloud-based artificial intelligence solution for real-time image analysis一 Lunit INSIGHT. This will be the first live-demonstration of the software to the public at 2017 Radiology Society of North America Annual Meeting (RSNA), beginning November 26 through December 1 at booth #8164, North Hall, McCormick Place in Chicago.
Lunit is one of the top international medical AI companies, named in CB Insights “AI 100” startups list as one of the leaders transforming healthcare industry. Lunit has proven high-end technology, recognized at international competitions such as ImageNet (5th place, 2015), TUPAC 2016 (1st place), and Camelyon 2017 (1st place), surpassing top companies like Google, IBM, and Microsoft.
Top-notch medical AI technology at the tip of your fingers
Lunit’s AIs are trained by a huge collection of de-identified clinical data from Lunit’s partner hospitals, 18 in total number of partnerships. The total number of images that has been directly used in its research has reached over 1 million well-curated high-quality case images. With the given image data, the AI algorithms are then specifically trained to detect target diseases or radiologic findings, including lung cancer, tuberculosis, pneumonia, pneumothorax, and breast cancer for chest x-ray and mammograms.
Based on this top-notch AI technology, Lunit is to introduce <Lunit INSIGHT>, a cloud-based imaging AI platform that is currently available to the public at https://insight.lunit.io/. The platform delivers Lunit’s state-of-the-art AI models; the first one to be unveiled is the chest x-ray solution. Lunit’s chest x-ray solution detects major chest abnormalities, lung nodule/mass, consolidation, and pneumothorax, with an unprecedented high level of accuracy 一 97% standalone accuracy in nodule detection, 99% for consolidation and pneumothorax.
According to the National Lung Screening Trial (NLST), one of the largest clinical trials conducted on lung cancer screening, 26.5% of lung cancer cases were shown to be missed by chest x-ray. Worldwide, more than 1 billion chest x-ray exams are performed every year. Decreasing the proportion of missed cases even by 10% would translate into significant clinical benefit.
What’s foremost remarkable is how Lunit’s AI solutions have been proven to significantly increase the diagnostic performance of its users up to 20%, from non-radiology physicians to radiology experts. Lunit’s solutions are designed to augment the diagnostic performance level of its users as the “second reader,” not replace them.
“Lunit’s vision is to develop advanced software for medical data analysis and interpretation that goes beyond the level of human vision,” said Anthony S. Paek, CEO of Lunit. “In presenting Lunit INSIGHT, we hope to contribute in opening a new era of medical practice, by helping and empowering healthcare professionals to make more accurate, consistent, and efficient clinical decisions for the patients.”
Users can upload their medical images via online, at Lunit INSIGHT webpage. AI analysis results appear in just a few seconds, including not only the level of abnormality, but also the visualization of the AI’s attention map. Lunit’s solutions will also be presented integrated into the systems of various companies including Nuance, EnvoyAI, and Infinitt Healthcare.
“Featured” in RSNA; development in process for mammography solution
Lunit was chosen as “featured” exhibitor of RSNA in a consecutive two year since its initial presentation last year. This year, Lunit’s exhibition booth is part of the “machine learning showcase,” along with Google Cloud, NVIDIA, and other top exhibitors. On Tuesday, 28 November, Brandon B. Suh, Chief Medical Officer, will give a presentation, “Lunit INSIGHT: Toward Beyond-Human-Level AI for Medical Imaging Modalities,” at Machine Learning Theater. A press conference and demonstration at Lunit booth will be followed shortly after the presentation.
In order to launch meaningful AI solutions that has high clinical impact, proper clinical validation is an important part of the process. “Large-scale multi-center reader studies are set to be conducted in early 2018,” said Suh, Chief Medical Officer of Lunit. “These are the studies with multiple leading hospitals in Korea and the US for Lunit’s chest x-ray and mammography solutions; publication of the results are targeted for late 2018.” FDA approval for Lunit’s chest x-ray and mammography solutions are expected to be achieved by end of 2018.
Other than the chest x-ray solution, Lunit’s mammography solution to detect suspicious breast cancer lesions is in its final stages of development. Lunit INSIGHT for Mammography is expected to be publically released by the first quarter of 2018. Lunit is also doing research in developing solutions for digital breast tomosynthesis, chest CT, and coronary CT angiography.
Nov. 25, 2017, midnight
Lunit, recognized as one of the 100 most promising private AI companies in the world by CB Insights, will showcase a product line based on winning algorithm of MICCAI Tumor Proliferation Assessment Challenge 2016 at the Henry B. Gonzales Convention Center in San Antonio, TX, on March 4 through March 10, 2017.
Lunit's new product seeks to provide valuable information, both diagnostic as well as predictive, for pathologists and researchers alike. Lunit will exhibit the first public demonstration for breast and prostate cancer at booth #222.
Lunit's goal is to develop AI-powered state-of-the-art diagnostic software that helps pathologists diagnose better and faster, as well as discover novel histopathologic biomarkers with high clinical impact.
Lunit is looking for worldwide partnership with healthcare providers, scanner manufacturers, and platform vendors. To schedule a demonstration and/or meeting at the upcoming USCAP 2017, please reach out via email to email@example.com.
Feb. 28, 2017, 2:40 a.m.
At the Radiological Society of North America’s (RSNA) 2016 annual meeting at McCormick Place Convention Center in Chicago, Illinois, on November 27 through December 2, Lunit will showcase Data-driven Imaging Biomarker (DIB), a novel AI-powered visual perception technology derived from cutting-edge deep learning technology applied with large-scale medical data.
In contrast to conventional Computer-Aided Detection (CAD) that is heavily dependent on guidance (lesion annotation) of radiologists, DIB lets the machine define important diagnostic features by itself solely from large-scale data. This can be possible by employing feature-learning capability of deep convolutional neural network.
At the event, Lunit will exhibit the first public demonstration of DIB for chest radiography and mammography at booth #4074 in South – Hall A.
DIB for chest radiography serves to act as a “second reader” in detection and differentiation of abnormal lesions in chest x-rays, including pneumonia, emphysema, diffuse interstitial lung disease, tuberculosis, lung cancer, and pulmonary metastasis, with high diagnostic performance. DIB is expected to help both general practitioners and radiologists interpret CRs more efficiently and accurately.
DIB for Mammography is designed to improve breast cancer detection rates in mammography, mainly focused on breast cancer detection in dense breast, an aspect of breast cancer screening widely regarded to be most challenging. Although it is in the early stages of development, Lunit’s DIB for mammography aims to 1) detect and localize lesions, 2) suggest their BI-RADS categories, and 3) predict their malignancy probabilities.
DIB will also be covered in the following presentations:
“Data-driven Imaging Biomarker: Uncovering Diagnostic Features from Large-scale Medical Images Using Deep Learning” –IN023-EC0-SUB: Informatics Sunday Poster Discussions, Sunday, November 27, 1:00-1:30 PM
“Performance Assessment of Data-driven Imaging Biomarker for Screening Pulmonary Tuberculosis on Chest Radiographs” --IN210-SD-MOB: Informatics Monday Poster Discussions, Monday, November 28, 12:45-1:15 PM
“Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening” --SSE02-06: Breast Imaging (Quantitative Imaging and CAD), Monday, November 28, 3:00-4:00 PM
Deep Convolutional Neural Network Approaches in Making a Diagnosis with Chest Radiographs: Initial Experience --SSJ05-06: Chest (Functional), Tuesday, November 29 Tuesday 3:00-4:00 PM
Lunit is looking forward to meet worldwide healthcare providers who are interested in DIB solutions.
To schedule a demonstration of DIB and/or meeting at the upcoming RSNA 2016, please reach out to us via firstname.lastname@example.org
Nov. 22, 2016, 7:33 a.m.
On Oct. 17, Tumor Proliferation Assessment Challenge was held as a satellite event of MICCAI 2016 and Lunit won first place in all of the three tasks in the challenge. Unlike the previous challenges, participants were asked to predict the tumor proliferation score that can be integrated into current prognostic grading systems, being more relevant to actual clinical practice.
Lunit’s researchers at the challenge venue: Sunggyun Park, Kyunghyun Paeng and Sangheum Hwang (from left)
Led by Kyunghyun Paeng, co-founder and research scientist at Lunit, the team designed a system based on the latest advances in artificial intelligence to help pathologists understand the histological slides more accurately and efficiently.
Top 5 challenge results are shown (click for more information). Semi-automatic methods are omitted as they are out of the challenge scope. The entries with asterisks(*) are teams that used separate additional data for training., not provided in the challenge.
Not only is tumor proliferation, highlighted by number of dividing cells, or mitotic cell count, important in the diagnostic process of histological slides especially in breast cancer, but it is also an important biomarker that is highly predictive of prognosis, or how long a patient will survive. This has very important clinical implications, as the type of treatment selected may be dependent on such biomarkers.
Accurate and consistent assessment of tumor proliferation on whole slide imaging level will bring much value to digital pathology. Lunit's successful endeavors mark a step forward toward application of state-of-the-art software analytics in digital pathology to bring unprecedented clinical benefits to patients.
Detailed code and description of methodologies used for this challenge will be released in the near future.
Oct. 27, 2016, 1:33 p.m.