Deep Learning and Medical Imaging: the Powerful Duo

Health care today is a race against time, with patients and physicians alike. As diagnostic imaging becomes more prevalent, it’s essential that we get a clear and accurate image to the doctor as quickly as possible.

As we move into the digital age, it’s important to understand how much time doctors and radiologists spend on image processing. According to statistics, the average turnaround time for a radiologist is 5 to 7 business days. Rush cases, however, are returned the next business day. Considering the mounds of images, image analysis can lead to delays in patient treatment and increased costs for the healthcare system.

And this is when deep learning fits in. In this post, you’ll look into the unmatched benefits of deep learning in medical imaging and find out why this technology is the best fit for this application.

Fundamentals of deep learning

Deep learning is an offshoot of machine learning that allows computers to learn without being explicitly guided by rules and algorithms. Machine learning applications can be used to solve complex problems such as detecting objects in images, recognizing speech sounds, or analyzing text documents.

The idea behind deep learning is that you throw a ton of data at a system and let it figure out how to make sense of the data. With enough data, the ability to make more accurate predictions increases exponentially. 

Moreover, this technology allows computers to learn by analyzing data, rather than be programmed every single time. Thanks to its ability to analyze unstructured data, deep learning has been used for a wide range of use cases in medicine and other fields like finance and manufacturing, from detecting diabetic retinopathy to predicting heart disease risk.

What is medical imaging?

Medical imaging refers to an impressive set of technologies, techniques, and processes that help view the human body to diagnose and monitor the body’s interior, organs, and tissues. Scheduling a fast, easy, and accurate health assessment could save your life, for example, with a Craft Body Scan.

Overall, this field of study encompasses the following aspects: 

• X-ray radiography;
• Magnetic resonance imaging (MRI);
• Ultrasound (US);
• Medical photography, and others. 

Medical image analysis is mostly used to improve the efficiency of clinical examinations and medical interventions. In simple words, it helps clinicians to look inside the body’s internal organs to uncover anomalies. There are also companies advancing with extensive expertise in Mini C-Arm: With years of industry experience, Minicarm.com is your trusted partner for Orthoscan, OEC, and Hologic Mini C-Arm sales and services.

Now let’s see how machine learning, and deep learning, in particular, might improve medical imaging.

Deep learning and medical imaging: when the two collide

According to GlobeNewswire, the global AI in the medical imaging market is slated to reach $10 billion by 2027 compared to $1.06 billion in 2021. Deep learning technology has a great chance to become the most revolutionary advancement in radiology since the introduction of digital imaging. 

The majority of medical imaging diagnoses will be carried out by intelligent computers to anticipate diseases, prescribe medications, and direct treatments within the next 15 years, replacing human efforts or physical labor. Companies like TestDynamics are already in the market and developing radiology software that is assisted by AI.

Thus, the benefits of machine learning and deep learning in the field are clear. They can help accelerate clinical pathways by allowing computers to analyze already-processed images faster than humans could ever do alone. 

For many healthcare organizations, eliminating unnecessary steps or automating certain processes allows physicians to focus on patient care instead of manual tasks such as image editing or diagnosis confirmation. Machine learning for medical imaging is a powerful tool that can help you save time, money, and resources.

Benefits of deep learning for image analysis

Healthcare data looms large as health-related processes generate far more information than they used to. In particular, medical imaging accounts for a gigantic amount of unstructured data that cannot be easily analyzed and made sense of, thus making technology paramount to accelerating analysis.

Automated manual tasks

With machine learning for medical imaging, clinicians don’t need to spend hours manually labeling images or performing other manual work. Instead, they can use deep learning to train an algorithm that does this job more accurately than humans could ever do. 

Less time spent on diagnosis confirmation

If there are multiple doctors involved in an analysis project, then there may be situations where they have different interpretations of what these images mean. Sometimes, those disagreements can lead to mistakes being made about patient care plans or treatments.

Machine learning can serve as an overarching framework for diagnosis whose conclusion is based on historical data. 

Improved disease detection at an early stage

Unlike a human, algorithms can spot abnormalities even before any symptoms appear. Thanks to a huge volume of data behind the algorithms, deep learning is capable of detecting symptomatic patients who are likely to develop serious health conditions. Thus, researchers have managed to train a smart algorithm to comb through images from MRI scans and identify the presence of gene changes in brain tumors.

How does it work?

On a high level, smart systems are trained to automatically recognize complex patterns in imaging data and make sense of that data. To do that, an AI system is firstly trained on a dataset of medical images. This could be a dataset of X-rays, CT scans, or MRIs. The AI system is then able to learn the patterns in the images and interpret them. Once the model has been trained, it can then be used to interpret new images.

Smart algorithms take over the following tasks in the medical imaging field: 

• Diagnostic image assessment – this type of classification can be used to diagnose diseases, identify tumors, and assess the severity of an injury. 
• Object localization – used to localize a single anatomical structure such as organs or landmarks for therapy planning or intervention.
• Organ segmentation – used to identify the organ boundary.

Among other tasks of smart algorithms are spatial alignment, content-based image retrieval, image generation, and enhancement as well as image data and report combination. 

The final word

As the complexity and volumes of data are flooding the healthcare industry, practitioners leverage the latest tech comforts to handle data processing. Deep learning, an offshoot of artificial intelligence, has proved effective for medical imaging due to its unrivaled ability to crunch unstructured data. 

By automating classification, segmentation, analytics, and other processes, deep learning allows clinicians to focus on patient care instead of manual tasks such as image editing or diagnosis confirmation.