Deep learning in healthcare

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Deep learning in healthcare

The rapid advancement of AI (Artificial Intelligence) has introduced the possibility of using aggregated healthcare data to produce powerful models that can automate examination and diagnosis.

To discover meaningful patterns in large, high-dimensional datasets, artificial neural networks and deep learning techniques can be used. Although going back as far as to the 1980s, deep learning has come to prominence in recent years driven in large part by the power of graphics processing units (GPUs) and the increasing availability of large, carefully annotated dataset.

While AI is perhaps the more widely used technology term, deep learning in healthcare is a branch of machine learning that offers transformative potential and introduces an even richer solutions layer to intricate healthcare challenges.

Using deep learning techniques provides the ability to analyze data at exceptional speeds without compromising on accuracy. In deep learning the aim is to make artificial neural networks learn from large amounts of data. It is referred to as “deep learning" as the neural networks have various layers that enable learning.

In Amatis, we are perfectly aware of the potential of applying deep learning techniques and methods to the area of healthcare. In line with our current product portfolio, we have already successfully applied some deep learning methods in electrocardiogram (ECG) analysis.

Our products and services are certified with the ISO 9001:2015-Quality management systems and the ISO 13485:2016-Medical Device Quality Systems Standards and we are committed to providing top quality products and services at all times.

The first and the major prerequisite to use deep learning is a massive amount of training dataset as the quality and evaluation of deep learning-based classifier relies heavily on the quality and amount of the data. Limited availability of analytic data is the biggest challenge for the success of deep learning in medical diagnosis.

The development of a massive training dataset is itself a laborious time-consuming task that requires extensive time from medical experts. Therefore, more qualified experts are needed to create quality data at a massive scale, especially for rare diseases. Moreover, a balanced dataset is necessary for deep learning algorithms to learn the underground representations appropriately. In healthcare majority of the available dataset is unfortunately unbalanced leading to class imbalance.

Sharing of medical data is severely complex and difficult compared to other datasets. All medical data stored and processed by Amatis is handled in strict accordance with the security controls of the ISO27001:2013 Information Security Management System Standard. We attach the utmost importance to protecting the privacy and confidentiality of medical data as well as customer data.

As Amatis, we want to make our contribution to the humankind by producing software and hardware that utilizes deep learning techniques to diagnose diseases and treat certain diseases. Our track record of producing high-quality software solutions is the first step in this direction. Our qualified software developers and experts will continue their work towards designing and developing cutting edge healthcare products that utilize deep learning and AI.

 

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