It is not so long ago that image-recognition algorithms could only be used to tackle “simple” tasks, such as differentiating cats from dogs. Today, the potential of Machine Learning (the main subset of AI) is much greater than in the past, and when fully exploited, much more complex problems can be tackled.

A very complex problem is the action of detecting a specific element (such as a tumor) on a biomedical image. For a few years, many research projects have been focusing exactly on implementing Artificial Intelligence for such detection or diagnosis questions.

Biomedical imaging (the action of capturing images for both diagnostic and therapeutic purposes) has therefore entered a new age thanks to Artificial Intelligence. See by yourself: AI in biomedical imaging has become one of the ‘hottest’ topics of research in medical Artificial Intelligence, and is currently the topic with one of the highest number of scientific articles downloads amongst the trove of AI scientific articles that are published.

Let’s discover how AI is beginning to change medical imaging services and the innovations that are on the horizon.

AI techniques used in Biomedical Imaging

Computer Vision

Computer Vision, or machine vision, is becoming increasingly mature and widely used in the health and biomedical industry. Computer Vision is the ability of computers to recognize, analyze, interpret, and understand attributes within a visual. CV can exploit texture, shape, contour, and prior knowledge along with contextual information from images, altogether helping better human understanding of visuals. Having this in mind, it is not surprising that in just a few short years, AI applications in biomedicine have “exploded”. The health industry is overflowing with the use of magnetic resonance imaging (MRI), computer tomography (CT), positron emission tomography (PET), and ultrasound imaging - providing CV a huge variety of rich data to exploit. Hence, making Computer Vision the ultimate ‘tool’ to help advance 21st-century healthcare.

Deep Learning

In a nutshell, deep learning is a machine learning technique that constructs artificial neural networks to mimic the structure and the function of the human brain. This learning technique is a groundbreaking tool for processing large quantities of data since the performance of the machine improves as it analyzes more data. Thanks to it’s complex and multilayered structure capable of learning very complex relationships in the data, deep learning is the natural solution to the challenge of helping biomedical professionals to look into the data more thoroughly.

In recent years, deep learning added a huge boost to the already rapidly developing field of computer vision. It has been increasingly used for analyzing medical images in various fields, and it shows excellent performance in various applications such as segmentation, object detection, and eventually object tracking.

Why AI is beneficial for biomedical imaging

Dealing with large datasets

Modern healthcare and biotechnology operations generate an immense amount of visual data. The data accumulation grows exponentially, as the tools for capturing medical and biological images are getting better. Deep inside this data, there are valuable insights regarding a patient's condition, the development of a disease, or even the molecular structure of cells. However, the scope of the data often surpasses the possibilities of traditional analysis. This is a significant challenge since some information from this imaging data can be overlooked or misinterpreted. Artificial Intelligence can be used to make sense of extremely large volumes of data that humans can simply not cope with.

Improving the accuracy of medical diagnosis

From X-rays to CT scans and MRIs, medical professionals are required to examine complex images to analyze patient health and initiate diagnosis. Medical practitioners can use AI-based solutions to gather patient information, analyze patient’s responses, and narrow down the diagnosis choices. Machine Learning algorithms have the capacity to ‘improve’ and ‘learn’ to recognize patterns of disease features, such as the appearance of breast cancers on mammograms as well as analyzing surrounding textural features. ML algorithms can also spot data relationships ‘invisible’ to the human eye, making them the ultimate ‘tool’ for better medical diagnosis.

Saving time and resources

Using AI for disease diagnosis can help cut costs in care delivery, assist healthcare practitioners, and translate into saving months before discovering a life-threatening condition. AI models can be trained to spot even the slightest abnormalities, which can be often the difference between saving or losing a patient.

Enhancing processes

AI in radiology

Development and adoption of AI technologies in radiology is spreading extremely rapidely. In the United States, it is estimated that the Food and Drug administration approves one algorithm per month in the field of radiology. Let's take an example: an algorithm developed by Google can determine patients’ risk of cardiovascular pathology by looking at a scan of their retinas. The model can pick up on subtle changes related to blood pressure, cholesterol, smoking, or even aging.

AI in drug discovery

In a field like drug discovery, the use of Artificial Intelligence is bringing immense value. Dealing with microscopic images can be challenging, since images produced by any given experiment may vary tremendously from one batch to another. Fluctuations in temperature, reagent batches, and exposure time may all produce misleading changes unrelated to the study or the effect of a candidate drug compound. Also, the number of variables that can contribute to the study is enormous. Keeping track of and separating out the effects is a major challenge in data-driven drug discovery. Luckily, AI can help tackle these challenges, especially in the virtual high-throughput screening phase, leading to a more efficient, cheaper, and faster drug discovery process.

AI for surgery optimization

AI can help improve pre-operative planning of surgeries, where surgeons plan the surgical procedure from existing medical records. At this stage, imaging is essential for the success of the surgery. Among existing imaging modalities, X-ray, CT, ultrasound, and MRI are the most common medical imaging techniques used in practice. By using AI, surgeons are able to prepare even better for surgery. AI can improve anatomic classification, detection, segmentation, and registration between medical images (registration is the process to transform different sets of data into one coordinate system). AI can also be used to give intra-operative guidance, and to assist surgical operations.

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The application of AI in the field of biomedical imaging shows how this technology can have a formidable contribution to human quality of life.

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