Deep learning is a branch or subset of machine learning which uses artificial neural networks as its foundation. Neural networks are inspired by how the brain processes information and can be used to simulate how the brain learns. Deep learning algorithms have achieved state-of-the-art results in many areas, including computer vision, natural language processing, and recommender systems.
In recent years, deep learning in healthcare has begun to impact the medical industry. Deep learning algorithms are being used for a variety of tasks in healthcare, including diagnostic radiology, pathology, drug development, and predictions of patient outcomes. In this blog post, we will discuss some of the ways deep learning is changing healthcare.
Deep Learning for Diagnostic Radiology
Diagnostic radiology is one area where healthcare professionals make use of deep learning in healthcare. Deep learning is used for the automated interpretation of medical images. Algorithms can be trained to detect abnormalities in images, such as tumors in MRI scans or signs of diabetic retinopathy in fundus photographs.
Several start-ups are working on commercial products that use deep learning for the automated interpretation of medical images. Some deep learning systems have been trained on over one million X-ray, and CT scans dataset. These systems have shown to be more accurate than radiologists at detecting pneumonia and identifying fractures.
Deep Learning for Pathology
Deep learning in healthcare is also being used for digital pathology. In digital pathology, whole slide images (WSI) are scanned at high resolution and digitized. The digitized images can then be uploaded to a computer where they can be viewed and analyzed with image analysis software.
Deep Learning for Drug Development
Deep learning is also being used for drug development. Algorithms can be trained to predict the efficacy of potential drugs by analyzing data from previous clinical trials. In 2018, Regeneron Pharmaceuticals announced that it had used deep learning to develop a new treatment for Alzheimer’s disease. The treatment showed positive results in early clinical trials and will move on to Phase III clinical trials.
Deep Learning for Personalized Medicine
Another deep learning application in healthcare can be seen in how it is changing healthcare is personalized medicine. Deep learning can predict which patients are likely to respond positively to a certain treatment based on their characteristics (e.g., age, gender, genetic makeup). This information can then be used to tailor treatments specifically for each patient, leading to improved outcomes.
In one study, a deep learning algorithm was used to predict which patients with melanoma would respond positively to immunotherapy. This treatment harnesses the body's immune system to fight cancer—based on genetic markers in the tumor cells. The algorithm could accurately predict which patients would respond positively to immunotherapy with an accuracy of 85%.
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