For every attendee who takes part in the AI event, whether they attend onsite or online, the European Society of Radiology will plant a tree!
Artificial Intelligence is the hottest topic in radiology right now, and the ESR and ESOR are excited to announce their second event on Artificial Intelligence, in Vienna, Austria, on May 14-15, 2020 entitled:
„Intelligence. Innovation. Imaging.
AI in Oncologic Imaging.
An ESR Premium Event brought to you by ESOR.”
The main focus of the event is to provide advanced knowledge about AI in oncology and its use in diagnostic imaging and clinical applications. Delegates will be introduced to important fundamental concepts of oncology, such as tumour heterogeneity, whilst also learning about the processing of imaging data by AI tools and discovering the main potential and emerging clinical applications of this cutting-edge technology. The topics will be taught by expert radiologists who have professional experience working with AI. In between sessions, SME companies will present themselves and showcase their products. The event will conclude with a final session envisioning the future of AI in oncologic imaging and its increasingly crucial role in cancer research and education. This event will also be live streamed.
Am Belvedere 1
This event will also be live streamed.
- to learn the basics of tumour characteristics
- to understand the quantitative imaging data in oncologic imaging
- to be aware of the role of AI in cancer diagnosis and tumour response
- to know the most common applications of AI in oncologic imaging
- to learn how AI may be implemented in research and education in oncologic imaging
Smart scanning and image interpretation
Artificial intelligence not only facilitates the generation of medical images. With its help, image data can also be interpreted in new ways – and diagnoses can be made more precise and meaningful.
The idea is visionary, but nevertheless obvious: could artificial intelligence (AI) make diagnostic scans more precise and meaningful – and thus ultimately make therapy more individual and reliable?
Today, AI already supports imaging at various levels, such as recording and processing image data. In the future, however, it will also become increasingly important for image interpretation.
As in other areas, such as speech recognition on smartphones, AI in medicine is often based on artificial neural networks. This refers to computer algorithms that imitate the networking and function of the nerve cells in the brain (even if they are by no means a lifelike image of the cerebral cortex). Such algorithms are capable of learning and can, for example, be trained with the data from computer tomography in such a way that they independently recognise anatomical structures.
Paths to individualized computed tomography
The practical benefits of AI are now beyond question. For example, Siemens Healthineers has developed an intelligent 3D camera system that recognises the body contours and position of patients in a CT device and individually calculates the optimal height of the scanner table. This makes it possible to achieve better images with lower radiation exposure.
Image processing is also considerably facilitated by AI-based anatomical pattern recognition. Thanks to the technology, radiologists can view the right kidney or left acetabulum in seconds, for example, in extensive 3D image data sets, display the correct numbering of ribs and vertebral bodies or precisely compare the images with previous scans.
Siemens Healthineers recently introduced two digital companions – powerful AI-enriched systems called the AI-Pathway Companion1 and the AI-Rad Companion2. The latter, being an intelligent services platform for radiologists, may help to reduce the time of interpretation and reporting. It automatically performs measurements and prepares results in the form of valuable clinical images and reports. AI-Rad Companion Chest CT is fully integrated in the image interpretation workflow and helps to handle the daily workload with more ease.
Images become data sets
What is particularly fascinating, however, is that AI algorithms can now also be used by physicians for the actual diagnosis. One example is the automated analysis of skull CTs in order to promptly detect unexpected brain hemorrhages.
Even image information that cannot be seen with the naked eye can be revealed by advanced AI applications. For cancer patients, for example, it is in principle possible to use computerized image data analyses to identify specific patterns that make it possible to better assess the course of the disease or the success of the therapy.
In other words, AI could make medical images even more valuable in the future. This would make them all the more beneficial for individual patient care.
1 The product/feature mentioned herein is under development and not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.
2 AI-Rad Companion is 510(k) pending, and not yet commercially available in the United States and other countries.
Author: Siemens Healthineers