Using AI in Terms of Medical Technology : What is Deep Learning
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.