Minimum joint space width (mJSW) is explored as a metric able to indicate osteoarthritis progression in the hip. 300 annotated radiographs were used to train a deep learning (DL) algorithm to measure mJSW in native hips on antero-posterior (AP) pelvis radiographs. Trained annotators measured the mJSW in 375 additional images in order to establish a baseline with which to measure the DL algorithm's success. The mean absolute error between the human and DL algorithm’s measurement was 0.87 +/- 1.05 mm. In 70%, 84%, and 90% of the 375 cases compared against, the algorithm’s measurement was within 1 mm, 1.5 mm, and 2 mm, respectively. A value, however, could only be interpreted 92.3% of the time. Radiographic findings and a patient's reported osteoarthritis symptoms don’t always align. Future algorithms should consider data sets detailing patient-reported outcomes.