No longer pie in the sky - Smart systems that could actually think like humans and solve complicated problems will be all around us in the future not far away. Deep Learning, a subset of ML, has already exploded in popularity for its ability to help solve complex problems without human intervention. In this blog, I am going to discuss how deep learning is setting health sector for a complete makeover of every aspect of patients’ life and how Evon can help leverage deep learning algorithms (that imitate neural networks of the human brain) to push a life-saving idea in the healthcare market.

Use Cases of Deep Learning in Healthcare

• Faster and accurate diagnosis/prognosis

A class of deep learning known as convolutional neural network (CNN) paves the way for medical imaging applications. CNN is trained in two phases to make accurate predictions: Forward phase and backward phase. In the former phase, the input is passed completely through the network, whereas the latter involves backpropagation of gradients and updating weights, meaning errors from the output are sent back to the input for correction.

CNN has five different layers:

• Input layer
• Convo layer (Convo + ReLU)
• Pooling layer
• Fully connected(FC) layer
• Softmax/logistic layer
• Output layer

CNN is now being increasingly used for computer vision to classify different objects in an input image and identify important features while analyzing images, such as MRI results or x-rays. In fact, the WBCD (Wisconsin Breast Cancer Diagnosis) dataset is now widely used for creating two classifiers that discriminate benign from malignant breast lumps with high accuracy.

A study published in the Annals of Oncology in 2018 also showed that a deep learning CNN identified skin cancer with 10 percent more accuracy than human diagnosticians.

One in ten patients die because of diagnostic errors, shows a study. Deep learning applications become a saviour here as they enable faster and accurate diagnosis by understanding the patterns of injuries and tumours, which leads to quick treatment and accurately estimate the prognosis.

For the sake of another example, a deep learning algorithm developed by Stanford researchers diagnoses pneumonia better than radiologists. The network is also capable of diagnosing 14 medical conditions. These life-saving solutions can also be built in the form of a deep learning custom mobile app development to help people who have to travel to far-flung places to get healthcare.

• Drug Discovery 

Deep learning holds great potential for early-stage drug discovery and manufacturing. Next-generation sequencing, experimental design, toxicity, molecular representation, binding affinity and precision medicine are some deep learning R&D technologies that can help find alternative medications for multifactorial diseases.

High cost of drug discovery and spending 10-15 years to bring a new drug to the market has long been the nemesis of the pharma industry. Deep learning (that is believed to be the panacea of healthcare in the long run) can significantly reduce the time and cost of drug development. Deep learning models can process gigantic amounts of chemical data that is collected over many years in quick time and produce outcomes for drug development. Besides, deciding the efficacy of a new medicine becomes much easier. Deep learning algorithms can decipher from a patient’s physiological signals such as gait, breathing, mobility, behaviour, sleep, and heart rate to establish how a drug is affecting their body, and thus help make informed decisions. Healthcare professionals are counting big on deep learning to help discover drugs for global epidemics and incurable diseases such as Alzheimer's. 

• Smart Health Records

Various traditional predictive modelling techniques are being used for dealing with potential predictor variables in a patient’s electronic health record (EHR) now. But they often lead to imprecise predictions and raise a false alarm for physicians, nurses, and other providers. The reason being that EHRs may have thousands of free-text notes from doctors, nurses, and other providers and predictive modelling techniques consider a limited number of commonly collected variables. This is where deep learning becomes a much bigger force - Deep learning approaches incorporate the entire EHR, leaving nothing behind, and produce accurate predictions for various health problems.

Deep learning models in EHR also reduce the burden of continuously reviewing and updating EHRs. With much lesser pressure of administrative tasks, doctors can spend more time with patients and greatly improve the quality of patient care. Natural Language Processing and deep learning combined are the way for smart EHRs.

• Genome Biology for Personalized Medicine

Understanding the genes of a patient helps doctors provide personalized medicine. The human genome has over 3 billion base pairs. When we consider mutations, there are more than 50 million dimensions. Also add epigenetics and 20,000 gene expressions and transcriptions. However, making sense of this complex and humongous amount of data is not easy for today’s computer science. Deep learning is believed to make breakthroughs in genome biology by helping doctors understand which medicine will help a patient recuperate faster and what type of a disease a patient is likely to contract. IBM Watson Genomics is a good example to cite here as its effort to integrate cognitive computing with genome-based tumour sequencing is helping doctors make a fast diagnosis.

There are endless healthcare applications of deep learning. You can think of virtual nurses, robots performing surgeries, outbreak prediction and many others. However, to build any deep learning healthcare solution, you need a technology partner that has hands-on experience of using new technologies and right resources available at its disposal.

Evon, an offshore software development company in India, has the proven experience of applying deep learning to implement AI Bots for Poker and Rummy card games on Gamentio, a 3D casino games website and app. With more than a decade experience in helping businesses with latest and innovative technology solutions, Evon can help you too to push your deep learning solution for healthcare in the market. Evon’s business analysts and subject matter experts have an eye for detail and expertise in clearly defining requirements. Tell us what type of healthcare solution you want to build using deep learning neural networks like ANN, CNN, RNN and more, and we will explain to you how Evon can help you push a champion in the healthcare market.

So, what are you waiting for? Get in touch with us here.