Smartphone-based surface topography app accurately detects clinically significant scoliosis.

PURPOSE: The purpose of this study was twofold: (1) to validate the predictive capabilities of the Scoliosis Assessment App using ST technology against X-ray "ground truth" in patients being evaluated for clinically significant scoliosis; and (2) to compare the diagnostic accuracy of the App versus the commonly used scoliometer tool. METHODS: A multicenter, prospective validation study was conducted among patients with known or suspected scoliosis. The App determined an Asymmetry Index to predict the likelihood of clinically significant disease (MCM ≥ 20°) as determined by X-ray. Outcomes included the sensitivity, specificity, and area under the receiver operating characteristic curve (ROC AUC) associated with the Apps prediction of clinically significant disease. RESULTS: Fifty-five patients were evaluated with a mean age of 13.6 ± 2.1 years. The App correctly classified 91% (50/55) of the patients compared to 69% (38/55) for the scoliometer. The sensitivity of the App was 96.4% (89.6-100% CI) versus 50% (28.1-71.9% CI) for the scoliometer (P < 0.05), while the specificity values were 85.2% (71.8-98.9% CI) and 88.9% (74.4-100% CI), respectively. ROC analysis indicated a statistically significant difference in accuracy (AUC) in favor of the App (95% versus 71%; P = 0.015). CONCLUSION: The Scoliosis Assessment App using ST technology offers an accurate, accessible, and non-ionizing method of detecting clinically significant scoliosis, suggesting that the App can be used for detection and monitoring as an alternative to radiography and as a replacement for scoliometer without diminishing the standard of care. Further studies are required to assess variations of sensitivity in a large cohort of patients and clinical utility as an alternative to radiographs.

Duke Scholars

Author Anthony Agostino Catanzano Jr. Orthopaedic Surgery