Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy

Keller G, Rachunek K, Springer F, Kraus M (2023)


Publication Type: Journal article

Publication year: 2023

Journal

DOI: 10.1007/s11547-023-01720-8

Abstract

Purpose: Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians in initial image interpretation. Materials and Methods: A pre-trained DL algorithm was specifically fine-tuned on static and dynamic dorsopalmar wrist radiography (training data set n = 201) for the automated assessment of the SL distance. Afterwards the DL algorithm was evaluated (evaluation data set n = 364 patients with n = 1604 radiographs) and correlated with results of an experienced human reader and with arthroscopic findings. Results: The evaluation data set comprised arthroscopically diagnosed SL insufficiency according to Geissler’s stages 0–4 (56.5%, 2.5%, 5.5%, 7.5%, 28.0%). Diagnostic accuracy of the DL algorithm on dorsopalmar radiography regarding SL integrity was close to that of the human reader (e.g. differentiation of Geissler’s stages ≤ 2 versus > 2 with a sensitivity of 74% and a specificity of 78% compared to 77% and 80%) with a correlation coefficient of 0.81 (P < 0.01). Conclusion: A DL algorithm like this might become a valuable tool supporting clinicians’ initial decision making on radiography regarding SL integrity and consequential triage for further patient management.

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APA:

Keller, G., Rachunek, K., Springer, F., & Kraus, M. (2023). Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy. La Radiologia Medica. https://doi.org/10.1007/s11547-023-01720-8

MLA:

Keller, Gabriel, et al. "Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy." La Radiologia Medica (2023).

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