| Autor: |
Anne Mathilde Andersen, Benjamin S. B. Rasmussen, Ole Graumann, Søren Overgaard, Michael Lundemann, Martin Haagen Haubro, Claus Varnum, Janne Rasmussen, Janni Jensen |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
BioMedInformatics, Vol 3, Iss 3, Pp 714-723 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-7426 |
| DOI: |
10.3390/biomedinformatics3030046 |
| Popis: |
Minimal joint space width (mJSW) is a radiographic measurement used in the diagnosis of hip osteoarthritis. A large variance when measuring mJSW highlights the need for a supporting diagnostic tool. This study aimed to estimate the reliability of a deep learning algorithm designed to measure the mJSW in pelvic radiographs and to estimate agreement between the algorithm and orthopedic surgeons, radiologists, and a reporting radiographer. The algorithm was highly consistent when measuring mJSW with a mean difference at 0.00. Human readers, however, were subject to variance with a repeatability coefficient of up to 1.31. Statistically, although not clinically significant, differences were found between the algorithm’s and all readers’ measurements with mean measured differences ranging from −0.78 to −0.36 mm. In conclusion, the algorithm was highly reliable, and the mean measured difference between the human readers combined and the algorithm was low, i.e., −0.5 mm bilaterally. Given the consistency of the algorithm, it may be a useful tool for monitoring hip osteoarthritis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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