A deep learning algorithm for radiographic measurements of the hip versus human CT measurements: An intermodality agreement study.

Abstract

Background: Hip dysplasia (HD) is a prevalent cause of non-traumatic hip pain, which may result in osteoarthritis. Radiological measurements of HD exhibit variability based on reader and imaging modality, why it is important to know the agreement between different measurement methods.

Purpose: To estimate agreement between measurements of lateral center edge angle (LCEA) and acetabular inclination angle (AIA) made, respectively, on Computed Tomography (CT) scans by humans and radiographs analyzed by an algorithm. To estimate impact of pelvic rotation on agreement between CT and radiographic measurements.

Material and methods: CT measurements were retrospectively extracted from 172 radiology reports. Radiographs were analyzed using an algorithm. Bland-Altman analysis assessed agreement between CT and radiographic measurements. Regression analyses estimated impact of pelvic rotation on inter-modality agreement.

Results: Mean measured bias (95% confidence interval [CI]) between CT and radiographs for LCEA of right/left hip was 5.53° (95% CI: 4.81 to 6.24) and 5.13 (95% CI: 4.43 to 5.83), respectively. Corresponding values for right/left AIA were 1.08 (95% CI: 0.49 to 1.67) and -0.03 (95% CI: -0.60 to 0.05). Pelvic rotation affected right LCEA and AIA measurements, with a change in obturator foramen index of, respectively, 0.35 and 0.6 resulting in approximately 2° change in values.

Conclusion: There was a significant difference in agreement of 5° between CT and radiographs for the LCEA bilaterally. The difference for the AIA was between 0 and 1°, probably of little clinical significance. Pelvic rotation slightly affected bias of the right LCEA, suggesting minimal clinical impact of a slightly rotated pelvis.