AZnod: AI Lung Nodule Detection
AZnod reduces the lung cancer screening interpretation burden, providing fully automated detection and characterization of pulmonary nodules that can be challenging to see on low-dose chest CT. For these programs, it delivers a single prioritized report, ensuring fast, consistent review of every study.
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Pathologies detected
Anatomical coverage: Lungs
Nodule size range: 3 mm to 30 mm
Built on clinical evidence
AZmed's Rayvolve and Rayscan AI suites are rigorously validated with world-leading institutions across a broad range of imaging modalities. Based on peer-reviewed clinical literature and large-scale real-world studies, AZmed's software provides high-quality image interpretation and reliable decision support, driving better patient care and raising the bar for safer, more sustainable radiology.
Optimized workflow and improved quality of care
Earlier, more consistent detection and follow-up of pulmonary nodules on chest CT.
88%
Nodule detection sensitivity
0.91
False positives per scan
<3 min
Analysis time per scan
Built for chest CT workflows
AZnod automatically detects pulmonary nodules on chest CT and reports each nodule's volume along with three key length measurements, supporting consistent characterization and follow-up tracking across studies over time.
Testimonials
We use Rayvolve for the detection of fractures and thoracic pathologies. The solution seamlessly integrates into our local PACS and our workflow. Thanks to its results, our radiologists can significantly speed up their overall reporting while simultaneously increasing its accuracy. We therefore consider it our AI-based second opinion, which boosts our overall quality and performance.
Henrik MichaelyOwner & Chief RadiologistMVZ Radiologie KarlsruheThe implementation of AZmed’s Rayvolve AI software for fracture detection at our institution has particularly helped our junior clinicians and practitioners in the emergency department, especially out of hours — with additional support in image interpretation and diagnosis, which in turn allows for a more efficient and streamlined patient treatment pathway into Orthopaedic fracture clinic.
Dr Subhasis BasuMSK RadiologistWrightington HospitalRayvolve demonstrated high stand-alone accuracy, aided diagnostic accuracy, and decreased interpretation time. When extrapolated over an entire population, one can see quickly how using this tool can really help decrease medical errors and healthcare costs.
Navid Faraji, MDMSK RadiologistUHOther AI radiology solutions
Support every diagnostic
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Book a custom demo. Our team will show you how AI results appear in your viewer, how AZmed integrates with your PACS, and what a typical implementation involves.
