Medi Whale and its product, Dr. Noon, was introduced to media by the Korea Economic Daily. The article is based on interview of Choi, the CEO of Medi Whale. Choi said that their program Dr. Noon can “diagnose disease risk of cardiovascular, macular, and optic nerve with 95% reliability by analyzing the fundus image”.
The article introduces that Choi started the company with an ophthalmology doctor of Yonsei college of medicine. As it says, Medi Whale has collected 0.1million fundus images for AI training and made a technology agreement with Yonsei college of Medicine.
Dr. Noon is useful for diabetic patients, according to the article. Diabetic retinopathy, which is one of the most popular complication of diabetics, can lead patient to blindness if the patient do not go through regular eye examination. Medi whale is on its way starting the clinical trials next month for medical use approval.
Medi Whale is also working on a AI diagnosis supporting program for cardiovascular disease examination. Choi said that by fundus image examination, previous cardiovascular disease examination CT can be replaced with cost decrease from USD 500~600 to USD 10.
Link to the full article as below.
Along with the study of evaluating diabetic retinopathy by applying deep learning to retinal fundus image, predicting cardiovascular risk factor also became focused in medical field.
As identified retinal fundus images can display distinctive features of optic disc or blood vessels, correlation between heart diseases and retinal disease, it allowed to predict the cardiovascular risk factors via deep learning.
Other parameters like age, gender, blood pressure, body mass index, glucose, and cholesterol levels can critically impact different phenotype of retinal images and suggest additional signals of the risk.
Every additional signals can be rapidly derived from various retinal images via spending cheap price (Very Efficient).
Ophthalmologists used the methods of highlighting different anatomical location of retinal by markers to identify and predict the risk factors. Blood vessels were highlighted to predict risk factors such as age, smoking, and SBP.
To predict HbA1c, perivascular surroundings were highlighted, and for gender prediction, optic disc was primarily highlighted.
For other predictions, such as diastolic blood pressure and BMI, the circular border of the image was highlighted and suggested that the signals will be distributed more throughout the image.
Medi Whale Co. introduced their research in EuroPCR, the biggest cardiology workshop in Paris.
This annually holding workshop is mainly focused on invasive cardiac surgery. In this year, 10,987 professionals were participated and shared their individual field research.
From this workshop, Medi-Whale represented the research result of AI which can assist physician’s diagnosis and evaluation in terms of invasive cardiac surgery. This AI can conduct the part which current physicians can easily miss or ignore from prescription.
Medi-Whale will use displayed research data and plan to process the deeper research. Meanwhile, EuroPCR 2018 was ended through distinctive research presentation, seminars, and exhibition. Next EuroPCR will open on May 21st, 2019.
Before when deep learning was not displayed in ophthalmology, diabetic retinopathy was needed to be evaluated and prescribed through human effort.
IDx-DR is using the algorithm which applied two development operating set (high specificity & high sensitivity) to identify diabetic retinopathy and to evaluate patient’s common status.
Referable and non-referable diabetic retinopathies were evaluated by two clinical validation sets (EYEPACS-1 & MESSIDOR-2) and ophthalmologists identified if these sets worked properly.
Multiple grading processes of convolutional neural networks along with data sets are required to get high sensitivity and specificity of RDR.
An overall sensitivity was lower than expectation, but FDA allowed to save more patients who were suffered by diseases.
The reference standard which used for this study was derived from ophthalmologist graders, and this will not find subtle traits which most ophthalmologists would not identify.
01:54:25The most common trend in medical AI society is using deep learning technology which can analyze medical image data.
Deep learning is the process of training neural network which allows an algorithm to program itself by learning from a large set of models that perform the desired behavior.
It is an important source in our company as we are developing products which can detect cardiovascular risk factors through retinal fundus image analysis.
Professional physicians can save time and effort of identifying relationship between one disease the other and predict outcome after when they diagnose patients through deep learning.
Convolutional neural network (CNN) & Recurrent neural network (RNN) are the most well-developed form of artificial neural networks which can analyze specific image or varying data.
The concept of deep learning is derived from the idea of brain signal processes between one neuron to other. Every neurons becomes a connected form of neural network and save large data.
The connected form from artificial neural network is embodied as a weight. Learning processes occur when weights can strategically vary out and form a model which can explain specific data.
After when inserted data pass through activation function and yield results, these results are compared with the answer which needs to be properly displayed.
If error between derived results and official answer exist, those will confirm and change weights to produce more accurate result.
The most significant point of deep learning is the fact that human does not have to indicate any features beforehand as deep learning can learn every feature of data by itself.
In terms of image data classification, AlexNet specifically developed more on optical recognition by CNN from deep learning and displayed the least percentage of error (16.4%).
In medical field, deep learning displays more efficient interpretation result when it compares to professional physician’s interpretation as it is mostly based on CNN.
The most important factors when researchers determine the efficiency of deep learning application are test result accuracy, convenience to patients, and medical cost reduction.
The best representative example of deep learning application can be “Mammography” from Zebra Medical Vision which focused on evaluating breast cancer by interpreting X-Ray picture.
When researchers observed and compared the result of Mammography, AUC displayed the high accuracy of 0.922 and specificity/sensitivity were similar to professional radiologist’s evaluation.
This research proved the fact that the medical field could be replaced with AI based machine rather than remaining professional physicians.
The 2nd Technology Investment Partnering (Tech-investment Road Show) took place at Avison Bio Medical Research Center (ABMRC) in 30th, April. It was hosted by the Ministry of Health and Welfare and was conducted by Korea Health Industry Development Institute.
This ‘visiting investment fair’ visits occupied companies in bio-health field and host a partnering with the investment company, vitalizing the investment environment. This season it was joint hosted with ‘Severance Technology Session’ of Yonsei Medical Center.
Five occupied companies including ‘Medi Whale’, ‘BNH Research’, and ‘Shine Bio’, and eight investment institutions including ‘Korea Investment Partners’, and ‘Miraeasset’ participated. The occupied companies participated consultations with investors, from which they are expected to build an investment agreement.
Medi Whale, an occupied company, took the microphone among companies to introduce their business. Its CEO, Kevin Taegeun Choi, said “It was a pleasing opportunity to have a chance to introduce our business to investors.” The presentation received good responses from the investors which led them to the following meetings.