Evaluation of a deep learning software for automated measurements on full-leg standing radiographs
Automated measurements on anteroposterior full-leg standing radiographs offer a reliable alternative to manual assessments. The use of AI in musculoskeletal radiology has the potential to support physicians in their daily practice without compromising patient care standards.
Authors
- Louis Lassalle (ORCID: https://orcid.org/0000-0003-3632-8254)
- Vincent Marty
- Jeanne Ventre (ORCID: https://orcid.org/0000-0002-4291-0710)
- Marion Durteste (ORCID: https://orcid.org/0000-0001-6989-3589)
- Nor-Eddine Regnard (ORCID: https://orcid.org/0000-0001-6093-1315)
- Lauryane Clovis
- Ali Guermazi (ORCID: https://orcid.org/0000-0002-9374-8266)
- Nicolas Nitche
- Alexis Ducarouge (ORCID: https://orcid.org/0000-0003-0268-6010)
- Zekun Zhang (ORCID: https://orcid.org/0000-0003-3893-3036)
- Jean-Denis Laredo
Institutions
- Boston University (US)
- Université Paris Cité (FR)
- Institute Mutualiste Montsouris (FR)
- Imaging, Brain, and Neuropsychiatry (FR)
- Laboratoire de Biologie, Bioingéniérie et Bioimagerie ostéoarticulaire (FR)
- Générale de Santé (FR)
- Biomatériaux et Bioingénierie (FR)
Publication Details
- Journal
- Knee Surgery and Related Research
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1186/s43019-024-00246-1
- Citations
- 6
- Primary Topic
- Hip disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 3.44