How Data Training Accelerates the Implementation of AI into Medical Industry

COVID-19 has undoubtedly accelerated the application of AI in healthcare, such as virus surveillance, diagnosis and patient risk assessments. AI-powered drones, robots and digital assistants are improving healthcare industry with better accuracy and efficiency. These have enabled doctors to provide more effective and personalized treatment with real-time data monitoring and analysis.

Garbage in, garbage out

As one of the most popular and promising subsets of AI, machine learning gives algorithms the ability to "learn" from training data so as to identify patterns and make decisions with little human intervention. However, as the saying goes, "garbage in, garbage out," making sure correct data fed into ML algorithms is not an easy work.

According to a report "the Digital Universe Driving Data Growth in Healthcare," published by EMC with research and analysis from IDC, hospitals are producing 50 petabytes of data per year. Almost 90% of this data consists of medical imaging i.e. digital images from scans like MRIs or CTs. However, more than 97% of this data goes unanalyzed or unused.

Unstructured raw data needs to be labelled for computer visions so that when the data is fed into an algorithm to train a ML model, the algorithm can recognize and learn from it. As DJ Patil and Hilary Mason write in Data Driven, "cleaning and labeling the data is often the most taxing part of data science, and is frequently 80% of the work."

Many enterprises wish to apply AI to their business practices. They have a glut of data, such as vast amounts of images from cameras and document texts. The challenge, however, is how to process and label those data in order to make it useful and productive. Many organizations are struggling to get AI and ML projects into production due to data labeling limitations and real-time validation deficiency.

A robust data labeling platform with real-time monitoring and high efficiency

An entire ecosystem of tech startups has emerged to contribute to the data labelling process. Among them, ByteBridge.io, a data labeling platform, solves the data labeling challenge with robust tools for real-time workflow management and automating the data labeling operations. Aiming at increasing flexibility, quality and efficiency for the data labeling industry, it specializes in high volumes, high variance, complex data, and provides full-stack solution for AI companies.

"On the dashboard, users can seamlessly manage all projects with powerful tools in real-time to meet their unique requirements. The automated platform ensures data quality, reduces the challenge of workforce management and lowers the costs with transparent standardized pricings," said Brian Cheong, CEO and founder of ByteBridge.io.

The quality of labeled dataset determines the success of AI projects, making it vital to look for a reliable platform that can help developers to overcome the data labeling challenges. The demands of data labelling will continue to be on the rise with the development of AI programs.

Human beings benefit from the implementation of AI systems into medical industry: from diagnosis to treatment, from drug experiment to generalization. These are all exciting areas for AI developers. But before that, providing high-quality training data lays the cornerstone of making those progress.

How Data Labeling Contributes to the War against Covid-19

 Healthcare industry is under enormous pressure, especially in the midst of Covid-19 period. The unexpected global pandemic has presented overwhelming challenges on human beings. Scientist, medical experts, doctors and nurses across the globe have undertaken their responsibility to fight against the disease. However, with a shortage of healthcare labor force, we still cannot deny how limited the current medical capacity is.

On December 30 of 2019, Healthmap, an artificial intelligence (AI) data-driven system that scans data sources for disease outbreak signs, detected an unusual activity about a new type of pneumonia burst in China. One day later, BlueDot, an AI outbreak risk software, raised a similar alarm after scanning thousands of Chinese news reports through its machine learning algorithms.

There’s no doubt that Covid-19 has been a catalyst for strengthening the increasing connection and cooperation between AI and healthcare industry.

Medical image diagnosis for future healthcare

AI and ML can be powerful methods for everything in healthcare: medicine research, diagnosis, disease prevention and control, patient treatment, even administrative and personnel management. AI/ML-enabled systems improve their capabilities and effectiveness by automating the most repetitive and homogenous activities. It is currently moving out of the labs and into real-world applications in the health sector.

When it comes to medical images, ML’s applications can cover the entire cycle from image creation and reconstruction to diagnosis and outcome prediction. AI-backed Machines use the computer vision to detect patterns that human eye can’t catch and correlate them with similar medical image data to identify possible diseases and prepare reports after analysis. X-ray, computed tomography (CT) scan, magnetic resonance imaging (MRI) and other image-based test reports can be easily screened to predict various illness in an automated, accurate, and fast way.

Some healthcare companies are now using ML technology to detect organ anomalies, such as identifying tumors from an MRI scan of the brain, along with millions of labeled medical images to show the affected area and to train ML algorithms to detect such diseases. For example, AI semantic segmentation can be used in liver and brain diagnosis; polygon annotation can be used in dentistry; bounding box in kidney stone; annotation detection in cancer cells, and etc. Medical image annotations provide results of greater accuracy in the early detection, diagnostics and treatment of disease as well as understanding the normal. The medical imaging diagnosis is seen as a powerful method for future applications in the health sector.

Bottlenecks of medical image labeling

High-quality training data is the key to building ML models and help to improve medical image-based diagnosis. However, a great challenge in this field is the lack of high quality data and annotation. Specifically, medical imaging annotations have to be performed by clinical specialists, which is costly and time-consuming.

As DJ Patil and Hilary Mason write in Data Driven, “Cleaning the data is often the most taxing part of data science, and is frequently 80% of the work.” The lack of precise and high quality data presents an overwhelming challenge for machine learning industry, limiting their ability to provide the “right data” to answer specific questions. Currently, most medical research organizations have limited access to data samples from a certain geographic areas.

The hardest part of building AI products is not the AI or algorithms but data preparation and labeling. For example, retinal images are used to develop automated diagnostic systems for conditions, such as diabetic retinopathy, age-related macular degeneration. In order to do that millions of medical images need to be labeled by various conditions structurally. This is laborious as it requires identification of very small structures and usually takes hours for experts to annotate them carefully.

Turning points

Aware of those challenges, ByteBridge.io moves a big step forward through its automated data collection and labeling platform. It allows researchers to have access to high-quality labeled datasets related to health care and public health.

ByteBridge’s innovative data training platform empowers healthcare researchers and ML medical companies to use data cost-effectively and improve healthcare outcomes. From data collection, to data labeling, to machine learning applications, ByteBridge.io provides professional data annotation service on medical images with the highest quality and maximum accuracy.

Different with traditional data labeling companies, in ByteBridge’s dashboard, researchers can create the data project by themselves, upload raw data, download processed results as well as check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and more control over the project status.

Compared to existing Western companies for data annotation outsourcing, Bytebridge.io charges 90% lower. It offers 50% cheaper price than its competitors in China and India. More than that, ByteBridge’s data processing speed is more than 10 times faster than the current data annotation company.

“I believe that we can achieve great innovation in this field based on our product development capabilities and underlying blockchain-based technology. ByteBridge.io is aimed at accelerating the development of ML industry and seamlessly transforming it into other essential areas such as healthcare,” said Brian Cheong, CEO of ByteBridge.io.

Imagine one day, patients can simply go through a fast AI scan as diagnosis; smart wearable devices, such as Apple Watch, can analyze physical data, note abnormality and generate an alarm before you are about to have a heart attack or a stroke; medical detection and prediction can be fully automated and supervised with little human intervention. Such scenes can definitely be realized in the coming future, thanks to ML and AI technology.

Machine Learning has achieved unprecedented success in computer vision and other industries so far. And now it is drastically revolutionizing healthcare area with indispensable support from automated data labeling service.