boneage

COMING SOON

AZboneage provides advanced automated calculation of the bone age of pediatric patients, based on the reference Greulich & Pyle methodology. Seamlessly integrated into clinical workflows, the product also performs the statistical comparison between the bone and chronological age of the young patients.

AZbone-age provides advanced automated calculation of the bone age of pediatric patients, based on the reference Greulich & Pyle methodology. Seamlessly integrated into clinical workflows, the product also performs the statistical comparison between the bone and chronological age of the young patients.

Pathologies Detected

Anatomical Areas Covered
bone age
HAND

Bakes by clinical research

🥼 Scientific evidence
5/18/2023
Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children
🥼 Scientific Evidence
5/1/2024
Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs

Benefices

Superior Diagnostic Performance

Enhanced diagnostic accuracy exceeds the native capabilities of imaging professionals. The integration aids in achieving better patient outcomes, and boosts confidence in diagnostic calls.
67%
sensitivity per case
73%
Reading time decreased

Better Operational Throughput

The predictive process of fracture detection is streamlined. In a busy clinical setting, this improvement enables the processing of  more cases within the workflow, reducing turnaround times on fracture reports and potentially reducing patient wait times.

Effective Case Handling

Fracture detection reliability is improved through an advanced medical-grade algorithm. This allows for the early identification of patients in need of urgent treatment, and reduces the risk of complications from undiagnosed fractures.

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 Michaely
Owner and Chief Radiologist MVZ Radiologie Karlsruhe

The implementation of AZmed's Rayvolve AI software for fracture detection at our institution has particulary helped our junior clinicians and practicioners in the emergency department, especially out of hours with additonal 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 Basu
MSK radiologist at Wrightington Hospital

Rayvolve 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, MD,
MSK radiologist at UH

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