Breakthrough Deep Learning Methods for Clinical Research

Deep learning is a subset of machine learning algorithms that uses multiple layers of artificial neural networks (ANN) to extract latent features from the input data, eliminating the necessity of manual feature engineering.

ANNs are basically the mathematical model of our human brain. Deep learning methods are data-hungry and rely on the availability of a huge data corpus. Lucky for them, data is abundant right now, and it is continually growing at an exponential rate. Our world is filled with electronic devices and gadgets, and no matter what anyone does and where they go, they are constantly generating data.

For instance, many people have become used to wearing fitness bands and watches. These devices are actively tracking movements, like steps, heartbeats and much more. Unarguably the most popular social platform on the planet, Facebook – generates 4 petabytes of data every day. Thus, people are understandably starting to consider data as a new fuel method.

A Data-based Approach

On the one hand, we are able to generate richer and richer data, and on the other, we have deep learning algorithms that depend on such bulk data. This means that the prerequisites for making deep learning applications successful are already available in large quantities and are waiting to be used. This is one of the main reasons why deep learning research and algorithms are gaining more and more popularity in recent years.

Besides abundant data, the easy availability of computing resources has also helped make using deep learning models easier. Processing large volumes of data, training sizable deep learning models, and hosting them for inferences definitely demands powerful resources.

Platforms like Spell have been making this process easier. Spell is the first comprehensive MLOps platform for deep learning that helps create spell models for deep learning methods and allows the seamless operationalization of these models in various domains like NLP, Computer Vision, and Speech recognition.

The Clinical Impact

Clinical research is a branch of medicine that examines the safety and efficacy of drugs, equipment, diagnostic products, and treatment regimens designed for human use. These can be used for illness prevention, treatment, diagnosis, or symptom relief. Some of the successful uses of deep learning so far include target identification and drug repurposing, given the large volumes of structured data available in this area. Deep learning-based analytics have also been used in recruitment and retention activities for clinical trials, such as identifying better candidates.

Deep learning is also being widely used in clinical research because it can produce state-of-the-art systems with enough training data. There are several published examples of deep learning applied to Image analysis, like classifying tissue images as normal or abnormal, drug discovery, finding the perfect size of artificial bones, and so on. Deep learning technologies help to extract more from the data that we have or will generate in the future.

Reducing Costs

Deep learning models can help to reduce the cost of clinical trials by helping to eliminate failures. The existing drug trial system is inefficient and costly. The average cost of bringing a novel medicine to patients is $2.6 billion. In addition, each authorized medicine must cover both its own expenses and the costs of those therapies that failed the trial.

Anyone who is familiar with the current drug development process understands how demanding and challenging it is. It all starts with a clinical strategy and the formulation of a protocol. The number of patients for a site is then estimated. Some companies promise a certain quantity of recruits. Some people get more or less than the amount predicted.

Many studies are canceled due to the wrong estimation of the candidates required. Now, let’s imagine a scenario where one could predict the number of patients that would be required for a study and can also help to determine whether a molecule can be a good candidate to treat a disease or not. Such scenarios have been recently made possible because of the use of deep learning methods. Because of such strategies, medical researchers from around the world were able to create the vaccines for Covid-19 in such a short period of time.

Machine Learning Limits

Although deep learning models are becoming more reliable at improving different aspects of clinical research, there is very little possibility that these will eventually replace the human workforce because no matter how good a machine gets, we will always need confirmation from a human expert before agreeing to the decision provided by the machine. So, deep learning models will ultimately act as a decision support system rather than replacing the actual human expert.

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