Deep Learning-Generated Radiographic Hip Dysplasia Parameters: Relationship to Postoperative Patient-Reported Outcome Measures

Background: Hip dysplasia (HD) causes accelerated osteoarthrosis of the acetabulum and is diagnosed through radiographic evaluation. An artificial intelligence (AI) program capable of measuring the necessary anatomical landmarks relevant to HD could reduce resource utilization, increase standardized HD screenings, and form HD outcome models. The study’s aim was to evaluate the relationship between AI measurements of dysplastic hips on initial presentation and changes in patient-reported outcome measures following surgical intervention for HD. Methods: One hundred nine patients with HD and planned surgical intervention obtained preoperative anterior-posterior pelvic radiographs which were measured by the HIPPO AI for lateral center edge angle, Tönnis angle, Sharp angle, Caput-Collum-Diaphyseal angle, femoral coverage, femoral extrusion, and pelvic obliquity. Patients completed a preoperative survey containing the 12-Item Short Form, EuroQol Visual Analog Scale (EQVAS), International Hip Outcome Tool (iHOT-12), Harris Hip Score, and Visual Analog Pain Scales. Patients were recommended to follow up at four months and one year to complete the same survey. Changes in outcome measures were evaluated with paired t-tests for each follow-up interval. Partial Spearman Rank-order correlations were performed between radiographic measures and changes in outcome measures at each follow-up interval controlling for age, BMI, and follow-up time. Results: Patients had significant improvement in all outcome measures at four months (N=46,p-values<0.05) and one year (N=49,p-values<0.001), except one-year EQVAS (p-value=0.090). Significant positive correlation of moderate strength existed between the Sharp angle and iHOT-12 at four months postoperatively (r =0.472,p-value=0.044). No other significant correlations were found at either follow-up interval between HIPPO measures and outcome measures. Conclusion: Correlations between deep learning radiographic measurements of dysplastic hips and improvements in postoperative outcomes as evaluated by outcome measures lacked any significant relationships in this study. Physicians treating HD patients can augment care with AI tools but outcomes are likely more multi-factorial and require multi-disciplinary patient care.