AI Algorithm Training on Diagnostic Images

Diagnostic imaging is a complicated, expensive medical process that requires analysis by skilled healthcare professionals.

But as medical technology advances and diagnostic imaging becomes even more useful, the amount of skilled imaging professionals needed to analyze images is not growing to keep pace. This skills gap was exacerbated by the COVID-19 pandemic.

Fortunately, artificial intelligence (AI) tools are increasingly being used to analyze diagnostic images. The result has been improving medical diagnostics and a greater capacity for analysis. AI algorithms can be used to generate images more quickly and spot problems that might otherwise be overlooked. For example, AI technology is now commonly used to perform diagnostic tasks in the specialty of radiology — including classifying tumors or blocked arteries.

But before an AI algorithm can perform these tasks, it must be properly trained using imaging data. For tasks related to medical image classifications, an algorithm must be trained and tested. This starts with the definition of “ground truth” — which is information taken from direct observations. For image classification, ground truth is established based on image labels or annotations. The entire training process can be lengthy and involved, as it includes a number of legal, technical, and regulatory considerations.

Before imaging data can be released, the AI training process must undergo ethical approval. After the data is released, steps must be taken to ensure patient privacy, data security, and accessibility. Data must also be structured in a machine-readable format and properly labeled before medical diagnostics AI training can begin.

The Benefits of Using AI Diagnostics for Imaging

One of the greatest benefits of AI diagnostics deep learning medical technology is the creation of an AI diagnostics accelerator. Medical images are typically composites made of several different images, and machine learning can optimize the compositional process. 

AI algorithms for Positron Emission Tomography (PET) scans, for instance, can lower the number of individual images needed to make a final composite. This has the added benefits of reducing the amount of creative active tracer needed and time spent in each scan position. These benefits are particularly crucial when it comes to pediatric or long-term care patients.

Reducing the length of imaging sessions also provides for a better patient experience, and this can actually improve outcomes. Research shows that many patients suffer from panic attacks and anxiety associated with imaging procedures. Faster scanning processes can reduce anxiety and help patients maintain their treatment regimens.

AI diagnostics companies are also investigating ways that the technology can improve imaging quality by reducing image distortions. Once the technology becomes available, it will eliminate the need for scans to be repeated if a patient accidentally moves while a scan is being performed.

X-ray algorithm training allows physicians to use a low-dose approach to computer tomography called filtered back projection. This CT technique has been falling out of favor, but AI algorithms significantly improve its results making it a more valuable and safer diagnostic technique than alternative approaches.

Use Cases for AI Algorithm Training on Diagnostic Images

From triage to tumor identification, AI algorithms are extracting more insights than ever from diagnostic images.


In one use case, an AI algorithm identifies when diagnostic images will be difficult for a radiologist to analyze. This triaging diagnostic scan allows medical professionals to flag cases that require deeper evaluation or additional tests.

Cardiovascular Imaging

AI algorithms are also used to improve cardiovascular imaging. Using the technology, a comprehensive imaging exam can avoid invasive diagnostic procedures, angiography, and cardiac catheterization.


In 3-D mammography, AI technology can boost the visibility of lesions, facilitating targeted tissue analysis. When used in combination with ultrasound and MRI technologies, AI-enhanced imaging can significantly increase the accuracy of diagnoses. This enhanced imaging technology can also assist radiologists in diagnosing patients according to their breast density and medical history, streamlining the entire diagnostic workflow.

CT Lung Scans

AI technology is also used to assist in the diagnostic imaging of lungs, increasing the speed and resolution of imaging processes. A Chinese company called Infervision has developed a type of AI-assisted CT scan that is capable of identifying suspicious lesions and it is currently improving the treatment of lung cancer.

Neurological Imaging

Algorithms are also being used to improve neurological imaging, improving the diagnosis of brain-related injuries and diseases. AI-augmented CT, CAT, and MRI imaging technologies are now capable of generating comprehensive maps of a patient’s central nervous system.

How TripleBlind Can Support Better Imaging Technology

AI algorithms hold significant promise for improving diagnostic imaging, but unfortunately there are a number of obstacles related to the development and application of this technology.

In particular, companies looking to develop imaging algorithms have limited access to sufficiently large and useful training data. The sharing of healthcare data is challenging due to regulatory restrictions, the risk of abuse, and potential fines for misuse. As a result, most efforts are relegated to limited, highly biased sample sizes that are narrow in scope. Furthermore, most medical organizations do not have the infrastructure for the safe sharing of sensitive medical data.

With our innovative privacy-enhancing technology, TripleBlind has been helping medical research organizations access larger sets of training data for their AI algorithms. The TripleBlind Solution allows data providers to keep their raw data in-house and share only encrypted data that cannot be decrypted. 

Sensitive data can only be processed using authorized operations. Our technology also protects proprietary algorithms, substantially reducing risk on both sides of the data sharing equation. Our technology can also be used with any kind of data, including diagnostic imaging data.

If you would like to learn more about how TripleBlind facilitates AI algorithm training, please contact us today.