Current Approaches to Artificial Intelligence in Medical Imaging
by Kris Dickie, VP of Research and Development
There has been a tremendous amount of discussion and research around the development of artificial intelligence (AI) and machine learning (ML), a subset of AI. With respect to medicine and medical imaging, AI has been around for years, helping to provide more automated approaches by which humans interact with data and images. Depending on the application, AI is currently used to sort data, apply transformations that help with interpretation and diagnoses, and even to perform actual diagnoses and detection. Clarius has built AI into its products from the very beginning to provide the most optimized and high quality image, as well as to reduce the number of operations required to attain a high-quality image.
Read PDF version
282 KB / PDF
Machine learning is a subset of AI and builds on the foundational work of developing neural networks within a computing engine. AI relies on predefined logic that follows an “if this then that” model, and complexity is limited only by the number of pathways that engineers and scientists can create within a given problem definition. With ML, the AI is trained on data sets which provide enough variability to account for as many scenarios as possible, essentially creating an exponential number of pathways compared to a traditional AI approach. The machine thus learns from the variability provided. For ML to be applied to real-world medical imaging, ideally tens of thousands of images need to be captured and analysed for the machine to build a model that is accurate enough to provide a useful AI tool.
Clarius does not currently use machine learning or deep learning techniques in its products, however it has been investing in machine learning projects and is collecting and analyzing data to help launch the next generation of automation and detection inside a handheld ultrasound.
Calculating Heart Rate Using AI
To measure heart rate in most ultrasound systems, it is extremely common for users to enable M-Mode to visualize one beam over time and then make a time measurement within a spectrum. This calculated time can then be converted to a heart rate measurement. While this method is not overly complicated, it does require the user to perform the following steps:
- Enable M-Mode
- Place a gate at the correct point on the image
- Pause the scan
- Enable measurement tools
- Place two cursors on the spectrum
To help speed up the workflow when just a basic heart-rate calculation is required, Clarius created an AI tool to automatically calculate the heart rate from the greyscale image without using M-Mode. By analyzing motion within a set of regions within the image, the standard deviation for each region can be calculated. When the standard deviation is filtered over time, and if a periodic pattern emerges, the use of Fourier transforms can be applied to calculate the rhythm, or peak frequency, of the image, and thus correlate the calculation to a heart rate measurement. By acquiring data over a number of seconds before displaying information, averaging is applied to account for the lower frame rates of greyscale imaging versus M-Mode, such as 30 frames per second versus 200 M-Mode lines per second.
Currently, Clarius’ Heart Rate AI works on adult cardiac images, with obstetric cardiac calculations currently being developed.
Heart Rate Monitor
Not scanning → Power Saver on
Scanning → Power Saver off
Managing Power with AI
Clarius Scanners are powered by a rechargeable lithium-ion battery that provides up to one hour of continuous scanning time. Although every scanner comes with two batteries, optimizing battery life is still a priority. Clarius incorporates a powerful AI algorithm to manage the power supply to the scanner. For example, when users put the scanner down on a table in an imaging state, the AI analyzes multiple scanner inputs, such as motion sensors and the image itself, to determine if a lower frame rate state should be engaged. If the user then starts imaging a patient, the AI will engage a high frame rate state once again. If the scanner is left for too long in the lower frame rate state, it will pause imaging, at which point the user will need to manually engage imaging through the freeze button within the Clarius App.