Next-generation machine learning model to measure the Norberg angle on canine hip radiographs increases accuracy and time to completion.

Abstract

Objective: To apply machine learning (ML) to measure the Norberg angle (NA) on canine ventrodorsal hip-extended pelvic radiographs.

Methods: In this observational study, an NA-AI model was trained on real and synthetic radiographs. Additional radiographs were used for validation and testing. Each NA was predicted using a hybrid architecture derived from 2 ML vision models. The NAs were measured by 4 authors, and the model all were compared to each other. The time taken to correct the NAs predicted by the model was compared to unassisted human measurements.

Results: The NA-AI model was trained on 733 real and 1,474 synthetic radiographs; 105 real radiographs were used for validation and 128 for testing. The mean absolute error between each human measurement ranged from 3° to 10° ± SD = 3° to 10° with an intraclass correlation between humans of 0.38 to 0.92. The mean absolute error between the NA-AI model prediction and the human measurements was 5° to 6° ± SD = 5° (intraclass correlation, 0.39 to 0.94). Bland-Altman plots showed good agreement between human and AI measurements when the NAs were greater than 80°. The time taken to check the accuracy of the NA measurement compared to unassisted measurements was reduced by 45% to 80%.

Conclusions: The NA-AI model proved more accurate than the original model except when the hip dysplasia was severe, and its assistance decreased the time needed to analyze radiographs.

Clinical relevance: The assistance of the NA-AI model reduces the time taken for radiographic hip analysis for clinical applications. However, it is less reliable in cases involving severe osteoarthritic change, requiring manual review for such cases.