azmed Rayvolve Trauma

1. https://radiology.healthairegister.com/products/azmed-rayvolve/
2. Gasmi I, Calinghen A, Parienti JJ, Belloy F, Fohlen A, Pelage JP. Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children. Pediatr Radiol. 2023 Jul;53(8):1675-1684. doi: 10.1007/s00247-023-05621-w. Epub 2023 Mar 6. PMID: 36877239.
   https://doi.org/10.1007/s00247-023-05621-w
3. Fu T, Viswanathan V, Attia A, Zerbib-Attal E, Kosaraju V, Barger R, Vidal J, Bittencourt LK, Faraji N. Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs. Acad Radiol. 2024 May;31(5):1989-1999. doi: 10.1016/j.acra.2023.10.042. Epub 2023 Nov 22. PMID: 37993303.
   https://doi.org/10.1016/j.acra.2023.10.042
4. External validation of a commercially available deep learning algorithm
   for fracture detection in children
   Michel Dupuisa, Leo Delbos, Raphael Veilb, Catherine Adamsbauma
   Diagnostic and Interventional Imaging, Volume 103, Issue 3, March 2022, Pages 151-159
   https://doi.org/10.1016/j.diii.2021.10.007
5. Reichert, G.; Bellamine, A.; Fontaine, M.; Naipeanu, B.; Altar, A.; Mejean, E.; Javaud, N.; Siauve, N. How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room? J. Imaging 2021, 7, 105.
   https://doi.org/10.3390/jimaging7070105