AKIRA: Deep learning tool for image standardization, implant detection and arthritis grading to establish a radiographic registry in patients with anterior cruciate ligament injuries
Authors
Cody C. Wyles (ORCID: https://orcid.org/0000-0002-8629-7567)
Kellen L. Mulford (ORCID: https://orcid.org/0000-0003-1019-5035)
Bruce A. Levy (ORCID: https://orcid.org/0000-0002-7694-1814)
Aaron John Krych (ORCID: https://orcid.org/0000-0003-3248-8007)
Yining Lu (ORCID: https://orcid.org/0000-0002-3967-645X)
Christopher L. Camp (ORCID: https://orcid.org/0000-0003-3058-7327)
AKIRA: Deep learning tool for image standardization, implant detection and arthritis grading to establish a radiographic registry in patients with anterior cruciate ligament injuries
Cody C. Wyles, Kellen L. Mulford, Bruce A. Levy, Aaron John Krych et al.
Citations
6 citations
Journal
Knee Surgery Sports Traumatology Arthroscopy
Topics
Total Knee Arthroplasty Outcomes
Field-Weighted Citation Impact (FWCI)
12.04
article
AKIRA: Deep learning tool for image standardization, implant detection and arthritis grading to establish a radiographic registry in patients with anterior cruciate ligament injuries
Cody C. Wyles, Kellen L. Mulford, Bruce A. Levy, Aaron John Krych, Yining Lu, Christopher L. Camp, Ayoosh Pareek, Ellie Kaji, Linjun Yang, Megan L. Grove
article en
6 citations
Abstract
No abstract available for this paper.
Publication Tag
Knee Surgery Sports Traumatology ArthroscopyVol. 33(11)
Institutions
Hospital for Special Surgery (US), Mayo Clinic (US), Orlando Health (US), Mayo Clinic in Florida (US), Orthopedic Institute (US)