Thirona obtains AI software patent for treatment of cystic fibrosis in children

The AI company will use the license to integrate a quantitative method for the analysis of chest CT scans.
By Sara Mageit
07:10 am
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Artificial intelligence, Thirona, Cystic Fibrosis

Dutch AI medical image analysis company, Thirona has obtained a license on a patent for the treatment of Cystic Fibrosis (CF).

The patent allows Thirona to develop an AI algorithm that will analyse CT scans to identify abnormalities in the lungs, ensuring that they are identified from an early age and that a personalised treatment plan can follow.

WHY IT MATTERS

Worldwide, there are approximately 70,000 known cases of CF, making it a rare genetic disorder.

Research on the disease, conducted by the Erasmus University Medical Centre Rotterdam in the Netherlands and the Telethon Kids Institute in Australia, previously resulted in PRAGMA-CF: a quantitative method for the analysis of chest CT scans of children with CF.

The current PRAGMA-CF method allows clinicians to manually assess the extent of lung disease in children with CF. However, the use of PRAGMA-CF requires extensive training and is time-consuming.

Thirona, has now obtained an exclusive license to a patent to integrate the method into its certified LungQ software, as well as co-exclusive rights to use the underlying datasets of PRAGMA-CF.

By leveraging artificial intelligence, clinicians will have an automated method to detect and quantify the extent of CF lung disease, helping to improve care and clinical decision-making.

THE LARGER CONTEXT

Earlier this year, Thirona partnered up with German digital health company, Smart Reporting, to launch the SmartCAD COVID-19 software. The platform combines Thirona's algorithm with Smart Reporting's COVID-19 reporting template to enable direct analysis of chest CT images and transfer the results into a structured report.

ON THE RECORD

Dr Eva van Rikxoort, managing director of Thirona, said: “Artificial intelligence algorithms make medical image analysis easier, more accurate and more effective. But it is hard work. Developing an algorithm that works as well as – and sometimes even better than – a clinician, takes time.

"This is especially the case when developing a solution/algorithm to be used on the analysis of CT scans of children. They are still growing, and their bodies and organs therefore keep changing, with or without a condition. All these changes with age must be calculated into the algorithm; a timeconsuming process, but one that is worthwhile.

"The algorithm we are developing will be able to quickly and accurately detect bronchiectasis and mucous quantifications in very young patients with CF, making personalised treatment easier. We believe this will have a substantial impact for patients and are proud to be part of the process.”

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