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.

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.