Building scientific evidence together to demonstrate the power of our AI suites
International multicenter validation of an expanded AI diagnostic system for 18 pathologies in thoracic and musculoskeletal radiography
Review the multicenter Rayvolve AI validation across 18 thoracic and musculoskeletal X-ray pathologies using 21,581 X-rays from 52 centers in 20 countries.
Read morePerformance of a complete AI radiographic suite across 258,373 X-rays from 26 countries: a worldwide evaluation
This study reports a large-scale, international, multi-center, retrospective evaluation of a complete radiographic AI suite across various clinical settings.
Read moreEnhancing pediatric fracture detection: multicenter evaluation of a deep learning AI model and its impact on radiologist performance
Pediatric fracture detection AI is evaluated in this multicenter deep learning study, measuring its impact on radiologist performance, diagnostic accuracy, and workflow in routine X-ray imaging.
Read moreEvaluation of the performance of an artificial intelligence (AI) algorithm in detecting thoracic pathologies on chest radiographs
The purpose of the study was to assess the performance of readers in diagnosing thoracic anomalies on standard chest radiographs (CXRs) with and without a AI tool and to evaluate the standalone performance of Rayvolve in detecting thoracic pathologies on CXRs.
Read moreAssessing the potential of a deep learning tool to improve fracture detection by radiologists and emergency physicians on extremity radiographs
To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and assess the performance of radiologists and emergency physicians in identifying fractures of the extremities with and without the DL aid.
Read moreComparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children
To evaluate the performance of an AI algorithm based on deep neural networks toward detecting traumatic appendicular fractures in a pediatric population. To compare sensitivity, specificity, positive predictive value and negative predictive value of different readers and the AI algorithm.
Read moreHow can a deep learning algorithm improve fracture detection on X-rays in the emergency room?
AI fracture detection enhances diagnostic accuracy, helping radiologists detect fractures faster with deep learning models for improved patient care.
Read moreExternal validation of a commercially available deep learning algorithm for fracture detection in children
The purpose of this study was to conduct an external validation of a fracture assessment AI algorithm (Rayvolve®) using digital radiographs from a real-life cohort of children presenting routinely to the emergency room
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