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Optical coherence tomography (OCT) is the highest resolution in-vivo modality to study atherosclerosis, the disease causing most heart attacks and strokes. However, broad utilization of OCT is limited by poorly-demarcated tissue interfaces, frequent artefacts, and massive datasets, with consequent poor measurement reproducibility.

Octiocor has developed the ‘Auto-OCT’ software platform, a novel, fully automated artificial-intelligence neural network-based image analysis system to identify and measure atherosclerosis from OCT images. Auto-OCT reproducibly corrects artefacts, identifies disease areas, measures high-risk atherosclerotic plaque features equivalent to the current standard provided by histology and clinicians, and stratifies coronary artery disease patients for precision therapies.

As AutoOCT does not change how OCT or percutaneous intervention (PCI) is performed, and its implementation, analysis and reporting is automatic and free of operator engagement or additional training to interpret images (a major barrier to implementation of other imaging techniques), the company envisages rapid uptake of AutoOCT after appropriate regulatory approval.

Once launched, AutoOCT would also provide a more cost-effective, efficient method for determining efficacy of anti-atherosclerotic drugs before costly Phase 3 clinical trials.

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