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Why AI, and why now? The Rise of Artificial Intelligence in Medical 3D Printing


Not a day goes by without another story about the impact of Artificial Intelligence (AI) in our daily lives...

Whether it is the latest app to help you keep on top of your diet or maximizing your pension contributions, AI is the latest in a long line of technological advances aimed to make our lives better. In the financial services industry, AI-driven algorithms are making great strides in augmenting routine tasks and removing the need for others completely. Fraud detection is something that affects us all, yet is complex and difficult to detect. Banks and financial exchanges are embracing the ability of AI to sift through large data warehouses and identify fraudulent patterns that would be impractical for humans to do. In pharma, AI is increasingly being used to design better drugs more safely, cutting down development times and costs. What all of these applications have in common is the ability of AI to simplify complex multifactorial decision-making processing. Not least, Artificial Intelligence in medical 3D printing...

Artificial Intelligence and Medical Data

AI is being applied to a wide range of processes in a variety of industries. Whilst they are complex problems, there is an outcome that can be optimized for. These outcomes are usually strongly linked to the rationale for the adoption of AI in the first place. They allow us to identify problems that would have been unaddressable without AI due to the scale of the data or the ambiguity of the outcome. The advances in deep learning have also typically been in the fields of image processing, NLP and voice analysis. This is the result of the impressive success of particular neural network architectures in addressing these problems. Medical data is typically in one of these forms and therefore a prime candidate for the adoption of AI-driven algorithms. A good use-case is that of processing audio notes into actionable text notes by applying voice recognition and NLP.

In the field of medical 3D printing, Artificial Intelligence has been applied to advanced segmentation tasks using image processing neural networks. Segmentation is the process of identifying objects and their boundaries within an image. In the context of medical images and 3D printing, this is the anatomy within an image. When an image is fully segmented, all the pixels in the image have a label that corresponds to a piece of anatomy or part of scanning machinery that is visible within the image.

Artificial Intelligence in the Clinical Setting

In the clinical setting, the adoption of Artificial Intelligence is greeted with a mixture of excitement and concern about the accuracy of results produced by machines. Clearly, there is a need for a higher standard of accuracy in hospitals for AI, as the ramifications of a mistake can drastically affect peoples’ lives and health. The final decision for a clinical diagnosis should always remain with a healthcare professional. AI can help increase the capacity of people to make decisions and improve the quality of those decisions, but ultimately it should be there to augment rather than replace.

Figure 1: Rise in the demand for imaging in the NHS in England taken from NHS Imaging and Radiodiagnostic activity in England 2012/13 Release

One of the main drivers for the adoption of AI in imaging is the inexorable rise in the utilization of imaging to make clinical decisions. As the number of images increases, so does the corresponding demand for analysis of them. AI can help address this need by augmenting the analysis of the imaging. This will facilitate better decision making in healthcare as the burden of routine analysis is removed and time is freed up to address more complex and difficult analysis that requires expert human intervention.

Of course, axial3D's mission is to support healthcare professionals in making more informed decisions, supported by in-depth insights through advanced medical visualizations. If you have a complex case, we might be able to help...

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Source for Figure 1: NHS Imaging and Radiodiagnostic activity in England 2012/13

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