ARTORG Media Releases

31 October 2019

Liver tumors removed safely, noninvasively and efficiently

Many liver tumors have long been difficult or impossible to remove. Since 2015, however, it has been possible to treat these tumors by combining noninvasive surgical techniques, radiological imaging and a navigation system. For the first time, a new study by University of Bern and Inselspital, Bern University Hospital, has impressively demonstrated the success of this technique.

The surgeon and the interventional radiologist at the Inselspital check the results of the ablation.
The interventional team at the Inselspital, Bern University Hospital, uses image-guided technology to plan and validate the exact positioning of the probe into the liver tumor as well as to perform the ablation procedure. (Photo: Adrian Moser for Insel Gruppe AG)

Every year, approximately 1,250 people in Switzerland develop liver cell cancer (hepatocellular carcinoma). Among other things, this type of cancer forms small tumors that are difficult to detect and access and could therefore not be removed using conventional surgical techniques. For some years now, surgeons and interventional radiologists have had a microwave ablation method at their disposal that guides a treatment probe from the abdominal wall directly to the tumor. Without reliable planning and instrument navigation, however, this method requires excessive care because it can injure major blood vessels or even the lungs. Furthermore, the complete removal of tumors cannot be sufficiently ensured.

Innovative procedure from Bern

In 2013, the Department of Visceral Surgery and Medicine and the Department of Diagnostic, Interventional and Pediatric Radiology at Inselspital together with the ARTORG Center for Biomedical Engineering Research at the University of Bern developed a navigation system for the treatment of tumors. Thanks to the integration of magnetic resonance imaging and computed tomography, the new navigation system makes it possible to plan the access path to the tumor and guide the probe precisely to the target area by means of real-time navigation. Because the liver is constantly moving due to breathing, real-time measurement is crucial. The technology developed to market maturity by CAScination in Bern has now been introduced worldwide.

Noninvasive computer-assisted navigation to the tumor

Image-guided ablation (tumor removal) is today performed at Inselspital two to three times a week to treat patients with liver cancer as well as metastases of colon cancer and other types of cancer. Prior to the procedure, patients are safely embedded in a vacuum mattress to fix their position exactly. The system visualises the respective location of the tumors precisely in the organ before insertion and after placement of the probe, as well as for monitoring success. Navigation points on the abdominal wall and a precise target system for the treatment probe enable computer-assisted, exact implementation of the planned treatment path, as well as microwave ablation of the tumor. After the operation, the treatment team immediately sees whether the tumor tissue has been completely ablated and can make improvements if necessary.

Safe, tissue-conserving and efficient

As the first user and co-developer, Inselspital has now evaluated in a retrospective analysis the method’s safety, therapeutic and procedural efficiency. To this end, the study team analyzed 174 ablations in cases of liver cell cancer between 2015 and 2017, which were carried out with the support of image-guided navigation. Needle placement and ablation coverage immediately after the procedure were checked each time in the navigation system.

The results of this study were published in Liver International in October 2019. Lead author PD Dr. med. Anja Lachenmayer summarizes: “Overall, our analysis showed that we were able to efficiently remove 96.3 percent of the tumors. On average, the probe deviated only 3.2 millimeters from the ideal treatment site. The risk of complications was very low at 5.9 percent (0.9% of which were high-grade complications).” Due to the minimally invasive procedure, patients could normally leave the hospital after only two days. The head of the study emphasizes: “We are able to prove that image-guided microwave ablation is a safe, tissue-conserving and efficient treatment for removing liver tumors. With the new system, we can even detect tumors not visible with conventional imaging and also treat areas that have remained inaccessible until now.” The latter is crucial for many patients because in more than half of liver cell cancer cases, tumors are untreatable because of difficult anatomic locations and could not be treated without navigation support.

Focus on Bernese technology at International ECALSS Meeting 2019

With nearly 600 liver tumors treated (in 391 interventions, 590 tumors removed), Inselspital has some of the world’s greatest experience with the ablation navigation system that was developed in Bern. Visceral surgeon Anja Lachenmayer and interventional radiologist Martin Maurer presented their experiences with the navigation system to international specialists in surgery, radiology, biomedical engineering and in the industry at the annual meeting of the European Computer Assisted Liver Surgery Society ECALSS (October 17-19, www.ecalss.org).

19 September 2019

 

Advanced AI boosts clinical analysis of eye images

 

A fast and reliable machine learning tool, developed by the ARTORG Center, University of Bern, and the Department of Ophthalmology, Inselspital brings Artificial Intelligence (AI) closer to clinical use in Ophthalmology. The novel method published in Nature Scientific Reports on September 19, 2019 presents a tool that reliably extracts meaning from extensive image data. Based on a convolutional neural network (CNN) the tool is able to provide results within seconds, thus supporting the doctor with comprehensive image analysis during a consultation with the patient.

Automated detection and localisation of key biomarkers in OCT eye scans. The AI system capable of doing so with human-level performance, computes these in a fraction of a second. Such capabilities open the door to widespread use of clinical digital aids to help ophthalmologists provide better care for their patients. (ARTORG Center)

Modern medical imaging devices allow ophthalmologists to monitor chronic eye conditions in detail. Ophthalmologists mostly choose Optical Coherence Tomography (OCT), an imaging tool that generates 3D images of the eye at extremely high resolution. But without AI support the large amount of images and information exceeds the capacity of an individual expert. The challenge of this study was, to provide AI-tools, capable of analyzing a large amount of data at very high speed to facilitate the use of all available information from image analysis during patient consultations.

The research team from Artificial Intelligence in Medical Imaging (AIMI) laboratory at the ARTORG Center for Biomedical Engineering Research, University of Bern, and the Department of Ophthalmology at Inselspital, Bern University Hospital now presents a machine learning method capable of identifying a wide range of biomarkers from OCT-scans of the retina virtually providing clinically relevant data support instantaneously.

Artificial Intelligence spots biomarkers for each disease type

“In our approach, the AI classifies patient OCT scans on the basis of disease-typical biomarkers”, explains Prof. Dr. Raphael Sznitman, group Head of the ARTORG ́s AIMI lab. Biomarkers are landmarks and features in OCT scans that can indicate a disease or can be used to show worsening or improvement after treatment. “What sets our results apart is that our AI algorithm provides a rich biomarker characterization, able to classify scans on the basis of well understood and known indications from the clinical community. Here, we manage to identify these biomarkers autonomously, without the cost and effort of having a trained human eye specialist previously mark the structures, the technology needs to focus on.”

3D imaging monitors sight-threatening macular diseases

The most frequent eye diseases worldwide are linked to degenerative eye conditions that deteriorate the macula (part of the rear part of the eye or retina), ultimately leading to loss of sight. Prof. Dr. med. Sebastian Wolf, Chairman and Head of the Department of Ophthalmology at Inselspital, Bern University Hospital, as a clinician uses OCT-scans for the therapy of chronic retinal conditions, such as age-related macular degeneration (AMD) or diabetic macular edema (DME). “As patient numbers are growing, we need to develop automated AI tools in the clinical setting to assist doctors in analyzing the abundant data of OCT scans. Having accurate, comprehensive information from the analysis of a patient’s OTC at hand during the consultation, is key to improve management of such diseases in the future. The tool presented in this paper is an important step in achieving the goal of better care for the patient.”

Machine learning makes the abundance of images exploitable

To assist eye doctors in clinical routine and research, computer programs can automatically extract, summarize and present the most important information from the growing number of routinely generated OCT scans. “This automated analysis can provide a cost effective and reliable tool for doctors to having to go through every image manually”, says Thomas Kurmann PhD student at ARTORG AIMI lab. “Our results so far are showing, that our Artificial Intelligence can consistently classify the most common disease types automatically with great precision, and identify a wide range of biomarkers typically found in pathological eye scans.”

Part of a large collaborative effort between University of Bern, the Inselspital and RetinAI Medical AG, the findings have the potential to be transformative for ophthalmologists. The next steps for this technology will be to understand how it can be incorporated into the clinic to improve the management of chronic eye conditions.



17 September 2019

Prestigious grant to use Artificial Intelligence for improved glucose control

The ARTORG Center for Biomedical Engineering Research of the University of Bern is the recipient of a grant from JDRF, the leading global funder for type 1 diabetes research. Thanks to the grant, a team led by Stavroula Mougiakakou will investigate a large, real-world dataset to develop advanced algorithms for automated insulin delivery that are capable of predicting dangerously low or high blood sugar levels. The goal is to optimize and personalize insulin treatment.

Qingnan Sun, PhD student, with the model that is able to provide personalized advice on insulin treatment. (Adrian Moser)

People with diabetes have a need to control their blood sugar levels to a normal range at all times. Today, scientifically validated automated insulin delivery (AID) systems are available as tools to enhance self-management. These systems empower people with diabetes to more successfully control their condition to prevent hypoglycemic (low-glucose) and hyperglycemic (high-glucose) events. However, these tools still have some shortcomings, because the algorithms used in these systems do not react adequately to variables influencing the blood sugar fluctuations in individuals, such as food intake or physical activity.

Bernese research group succeeds with advanced algorithms

The laboratory Artificial Intelligence in Health and Nutrition of the ARTORG Center proposes to use big data and deep and reinforcement learning technologies (machine learning tools) to improve the prediction accuracy of AID algorithms. The Artificial Intelligence (AI) algorithms will be trained to foresee dangerously low or high blood sugar levels in real life situations. “If we can predict future blood glucose levels, we can provide early warnings and thus improve each patient’s safety”, explains Prof. Dr. Stavroula Mougiakakou, who leads the laboratory.

Prof. Mougiakakou ́s team is one of only eight laboratories to receive the prestigious research grants awarded through a request for applications (RFA) by the US-based diabetes research foundation JDRF. The grant of about 144,000 USD most importantly provides access to big data, containing diabetes-specific patients’ information from thousands of glucose monitors and insulin pumps. The de-identified data were collected by Tidepool — a nonprofit organization committed to making diabetes data more accessible, actionable and meaningful for people with diabetes, clinicians, and researchers — through the Tidepool Big Data Donation Project.

“We are honored and proud that JDRF recognizes the potential of and our expertise in applications of AI in diabetes,” says Stavroula Mougiakakou, principal investigator of the project. “This grantgives us a unique opportunity to access big diabetes-related data and use it synergetically with advanced AI algorithms to uncover patterns and trends that bring us closer to more precise and personalized insulin treatment.” Prof. Mougiakakou has introduced the use of AI in insulin treatment optimization in the late 1990s.

Machine learning to gain diabetes insights from big data

The data JDRF and Tidepool will provide access to has been de-identified and combined in meaningful ways for the use of clinicians and researchers. “This data is a big step forward for ourresearch”, says Qingnan Sun, PhD student at the ARTORG laboratory working on the JDRF funded project. “The data access will help us to refine the algorithms that are used in AID systems, making it possible to warn a person at least half an hour before they develop hypo- or hyperglycemia.”

Personalizing blood sugar predictions

“First the AI algorithms will analyze glucose data to detect for each person, how age, bodily fitness, insulin treatment, number of years with the disease, as well as daily routines influence his or her glucose control,” explains Prof. Mougiakakou. “Subsequently the model uses these findings to predict hypo- or hyperglycemic events early enough so that the person with diabetes can react and prevent their onset. It is important to mention that the model will continue to learn individual’spattern and habits while in use.”

Media release University of Bern, 17 September 2019