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Purpose <p>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.</p> Methods <p>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.</p> Results <p>Fifty-five patients were evaluated with a mean age of 13.6 ± 2.1&#xa0;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 (<i>P</i> &lt; 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%; <i>P</i> = 0.015).</p> Conclusion <p>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.</p>

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Smartphone-based surface topography app accurately detects clinically significant scoliosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月2日

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  • Matthew S. Rohde,
  • Marleni Albarran,
  • Anthony A. Catanzano Jr.,
  • Elizabeth J. Sachs,
  • Hiba Naz,
  • Amishi Jobanputra,
  • Jacob Ribet,
  • Kali Tileston,
  • John S. Vorhies

摘要

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.