Hot on the trail of COVID-19: Working with collaborators from around the world, a team began developing an algorithm that helps to evaluate changes in lung tissue. Read about how scientists reached a significant milestone during the pandemic.
Putting the “AI” in aiding clinical decisions
One of the major challenges in radiology is dealing with the large amounts of highly complex data generated during examinations. Comparatively few specialists can evaluate the data, and it is a time-consuming process. As a result, applications using artificial intelligence (AI) are becoming increasingly important. Even today, task-specific AI solutions can help physicians to detect patterns and recognize systematic phenomena in their investigations.
How does artificial intelligence “know” what to do?
In the case of COVID-19, abnormal regions showing airway disease are localized and delineated in the CT image. Once an abnormal region has been detected in an image, the computer can learn from the examples and apply the extracted concepts to new data. The quality of the annotations and data has a major impact on the quality of the final product.
A tracker in a data jungle
Bogdan Georgescu is an expert in AI technologies. Working with an international team, he is developing prototype solutions that will speed up the analysis of radiological patterns in the lungs that could be caused by COVID-19.
What role does CT imaging play in diagnosing COVID-19?
An international team at a virtual conference table
The world is currently in a state of emergency, with governments everywhere restricting personal contact. However, this does not prevent researchers worldwide from pioneering new methods and developing new ideas. Siemens Healthineers formed an international and interdisciplinary team comprising AI scientists from the U.S., software developers from India, CT product as well as research and development experts from Germany, and medical experts from around the globe – and brought them together around a virtual conference table, all with a common goal.
Tremendous computing power is essential
United against the pandemic
Team member Thomas Re, an experienced radiologist, remembers the situation very well: “As for everyone, it was an extremely intense moment. Having trained in radiology in Milan, Italy, a location particularly hit by COVID-19 infections and fatalities just before the U.S. outbreak, and having heard from friends and colleagues directly affected, I was particularly aware of the gravity of the situation. I saw my work on the AI COVID-19 project as my best opportunity to provide a solution in future that could help in this time of crisis by working to improve COVID-19 diagnostic tools for the clinicians on the font-line of the pandemic. My task, as a radiologist, was to annotate imaging data for use in “training” the AI system to recognize COVID-19 disease patterns in chest CT data. Pulling together resources from around the globe for this project and to be part of such a team was truly amazing. My contribution would not have been possible without the support of other local and international team members; in particular, that of my radiology colleague Eileen Krieg, MD based in New Jersey, engineer Amit Vaze and his India based team as well as engineer Guillaume Chabin and his data management team in Paris.”
Solutions that are based on artificial intelligence can ease the workload of radiologists by providing experts with analyses for further assessment. For radiology, this means greater efficiency.
A prototype in record time
Due to the global pandemic, the team was restricted to virtual collaboration in video conferences and used interactive platforms and teamworking tools. "Everyone always had the same level of knowledge," explains Georgescu. His colleague Shikha Chaganti remembers: “We've all been doing it with a smile on our faces. A wonderful sense of community.” This provided the best foundation for meeting the challenge of developing a working prototype at incredible speed, as radiologist Abishek Balachandran confirms: “It was amazing to collaborate on this project. Given that it happened at a time of unprecedented crisis, we’re proud that even in this difficult period our team was able to scale up.” In just three weeks, the first version of the prototype was ready. It would be used to detect and quantify conspicuous tissue structures in the lung on a CT scan.
Algorithms must be fed with good examples
Putting the prototype to the acid test
In order to research the effects of COVID-19 on the lung, the AI team needed reliable comparative values as a solid basis for continuing their work. "When you launch a project like this, you need reliable data and advice from clinical experts right from the start. Only then can we develop the actual prototype. This requires effective algorithms and a powerful computer infrastructure. And finally, it must be possible to make the new concept available to several partners in a clinical setting so that they can test its practical suitability," explains Georgescu.
Therefore, to build, train, and deploy a machine learning model, the AI scientists and engineers worked hand in hand with dedicated clinical partners. “We have managed to engage and effectively work and collaborate across continents to have an efficient data processing pipeline,” says Georgescu.
His team drew on data from COVID-19 cases and additional studies from around 18 collaborating institutes in the U.S., Canada, and Europe – including Foch Hospital (France), Northwell Health (U.S) and Houston Methodist (U.S.), University Hospital Basel (Switzerland), and Vancouver General Hospital (Canada) – as reference values.
At all these locations, the same measures of lesions, lungs, and lobes were acquired to “feed” the algorithm with relevant data.
The intelligent CT pneumonia analysis2
It’s exciting to know that my work can help us to understand COVID-19 better and in future, potentially fight it more effectively.
From crisis comes opportunity
The process that was set in motion by Georgescu and his team could become a model for many future developments in terms of its approach, pace, and guiding principles. The pandemic is not all that has spread in the last few months: Pioneering spirit, cross-border project work, and intelligent collaborations have also flourished. An algorithm is just one of the many results to have come to fruition – and it has been shown that, in the fight against COVID-19, hard work and creativity are also contagious.
Automated quantification is just the beginning
The prototype was developed to automatically identify and quantify hyperdense lung regions in order to detect the affected area and then assess the severity of the inflamed tissue. This automated quantification of abnormalities associated with COVID-19 from noncontrast chest CT scans could help clinicians to evaluate the disease and to assess its severity and progression. However, the accuracy with which COVID-19 can be distinguished from other types of lung disease on CT images still varies.
One more thing
1 This product is under development and not commercially available. Its future availability cannot be ensured.
2 The AI-Rad Companion Chest CT Pulmonary Density feature falls under the FDA Enforcement Policy for Imaging Systems during the Coronavirus Disease 2019 (COVID-19) Public Health Emergency Section IV.C and is therefore not 510(k) cleared. It is not intended to be used to diagnose COVID-19. AI-Rad Companion Chest CT is not commercially available in all countries. Its future availability cannot be guaranteed.
3 Image created with Cinematic Rendering, Courtesy of CHR East Belgium Verviers
- The development of the prototype algorithm and its deployment was a Siemens Healthineers collaborative effort, bringing together frontline healthcare providers, scientists and engineers from Princeton, clinical experts and developers from India, product development teams from CT, Digital Health and Syngo from Germany. The Pulmonary Density feature is new in VA12A without FDA Clearance. According to FDA policy “Enforcement Policy for Imaging Systems During the Coronavirus Disease 2019 (COVID-19) Public Health Emergency issued in April 2020, the manufacturer is allowed to market this feature without FDA-clearance. This policy is intended to remain in effect only for the duration of the public health emergency related to COVID-19 declared by the HHS Secretary in accordance with section 319(a)(2) of the Public Health Services Act (42 U.S.C. 247d(a)(2)). Pulmonary Density results are not indicated for the diagnosis of COVID-19. Only in vitro diagnostic testing is currently the definitive method to diagnose COVID-19.