Royal Philips has signed a global collaboration agreement with US-based HeartFlow to enhance access to diagnostic and planning tools for assessment and treatment of patients with suspected coronary artery disease (CAD).

Under the deal, Philips will promote the combination of its advanced catheters with HeartFlow FFRct Analysis technology to image and analyse interior measurements of coronary arteries.

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The FFRct Analysis is a non-invasive technology developed to deliver insights into the extent and impact of CAD on the heart’s blood flow.

Using deep learning, the technology creates a personalised, digital three-dimensional (3D) model of coronary arteries that could be analysed by computer algorithms to replicate blood flow in the model and investigate the effect of blockages in coronary blood flow.

Philips catheters covered in the agreement include coronary fractional flow reserve (FFR), instant wave-free ratio (iFR), and intravascular ultrasound (IVUS) solutions.

“We can advance our goal of helping healthcare providers decide, guide, treat and confirm the right therapy at the point of care.”

Philips Image-Guided Therapy Devices business leader Christopher Barys said: “To address the burden that CAD is placing on healthcare systems and to transform the patient experience, hospitals are focused on adopting new technology that can improve the diagnosis and care of these patients while reducing unnecessary invasive procedures.

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“HeartFlow shares our mission of improving clinical outcomes and, through collaboration, we can advance our goal of helping healthcare providers decide, guide, treat and confirm the right therapy at the point of care.”

The commercial collaboration will initially focus on the US market, with plans to expand into other international markets in the future.

Philips and HeartFlow are currently working on additional collaborations in diagnostic imaging and advanced informatics.

The firms have signed an exclusive agreement for co-developing a cath lab X-ray image-derived FFR or iFR solution to simultaneously improve workflow, diagnosis and treatment of CAD