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Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as...

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Detalles Bibliográficos
Autores principales: Chen, Kenneth, Stotter, Christoph, Klestil, Thomas, Nehrer, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498096/
https://www.ncbi.nlm.nih.gov/pubmed/36140636
http://dx.doi.org/10.3390/diagnostics12092235
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author Chen, Kenneth
Stotter, Christoph
Klestil, Thomas
Nehrer, Stefan
author_facet Chen, Kenneth
Stotter, Christoph
Klestil, Thomas
Nehrer, Stefan
author_sort Chen, Kenneth
collection PubMed
description Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.
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spelling pubmed-94980962022-09-23 Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review Chen, Kenneth Stotter, Christoph Klestil, Thomas Nehrer, Stefan Diagnostics (Basel) Review Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for. MDPI 2022-09-16 /pmc/articles/PMC9498096/ /pubmed/36140636 http://dx.doi.org/10.3390/diagnostics12092235 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Chen, Kenneth
Stotter, Christoph
Klestil, Thomas
Nehrer, Stefan
Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
title Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
title_full Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
title_fullStr Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
title_full_unstemmed Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
title_short Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
title_sort artificial intelligence in orthopedic radiography analysis: a narrative review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498096/
https://www.ncbi.nlm.nih.gov/pubmed/36140636
http://dx.doi.org/10.3390/diagnostics12092235
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