Scholarlink

Purpose <p>Radiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scoliosis (AIS), but none have replaced the need for serial spinal radiographs. This study aimed to evaluate the accuracy and criterion validity of a new AI-powered digital health application using 3D surface topography to predict Cobb angles, with the goal of reducing radiation exposure and enabling home-based curve monitoring.</p> Methods <p>In a single-center observational study, 125 patients with confirmed or suspected scoliosis underwent smartphone-based 3D surface-topography scans in standing and forward-bending positions. Poor-quality scans (<i>n</i> = 20) were excluded. Radiographic Cobb angles were used as the gold standard. After random allocation, 79 scans formed the training set and 26 formed the validation set; external data (142 controls, 188 scoliosis patients) were added to strengthen training, and 25 controls were added to the test set. Accuracy was expressed as mean absolute error (MAE) and correlation with radiographs. Criterion validity was assessed by sensitivity, specificity, and AUC at clinically meaningful thresholds (10°, 25°, and 40°).</p> Results <p>Across 51 test scans (AIS + controls), the algorithm showed a strong correlation with radiographs (<i>r</i> = 0.922, 95% CI 0.866–0.955) and an MAE of 5.9° (95% CI 4.5–7.3). In AIS-only curves of 10–50°, the MAE was 6.4° (95% CI 4.4–8.3). At 10°, sensitivity was 0.962, specificity was 0.960, and AUC was 0.978. At 25°, sensitivity was 0.706, specificity was 0.853, and AUC was 0.917. At 40°, sensitivity was 0.667, specificity was 1.000, and AUC was 1.000.</p> Conclusion <p>This AI-driven 3D surface topography demonstrated high validity for non-radiographic Cobb angle prediction, particularly in mild-to-moderate AIS. It supports safer, more frequent, home-based monitoring, though refinements are needed for severe curves and patients with a higher BMI.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

查看全文
  • Stefan Parent,
  • Marjolaine Roy-Beaudry,
  • Justin Dufresne,
  • Rachelle Imbeault,
  • Soraya Barchi,
  • Marie Beauséjour

摘要

Purpose

Radiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scoliosis (AIS), but none have replaced the need for serial spinal radiographs. This study aimed to evaluate the accuracy and criterion validity of a new AI-powered digital health application using 3D surface topography to predict Cobb angles, with the goal of reducing radiation exposure and enabling home-based curve monitoring.

Methods

In a single-center observational study, 125 patients with confirmed or suspected scoliosis underwent smartphone-based 3D surface-topography scans in standing and forward-bending positions. Poor-quality scans (n = 20) were excluded. Radiographic Cobb angles were used as the gold standard. After random allocation, 79 scans formed the training set and 26 formed the validation set; external data (142 controls, 188 scoliosis patients) were added to strengthen training, and 25 controls were added to the test set. Accuracy was expressed as mean absolute error (MAE) and correlation with radiographs. Criterion validity was assessed by sensitivity, specificity, and AUC at clinically meaningful thresholds (10°, 25°, and 40°).

Results

Across 51 test scans (AIS + controls), the algorithm showed a strong correlation with radiographs (r = 0.922, 95% CI 0.866–0.955) and an MAE of 5.9° (95% CI 4.5–7.3). In AIS-only curves of 10–50°, the MAE was 6.4° (95% CI 4.4–8.3). At 10°, sensitivity was 0.962, specificity was 0.960, and AUC was 0.978. At 25°, sensitivity was 0.706, specificity was 0.853, and AUC was 0.917. At 40°, sensitivity was 0.667, specificity was 1.000, and AUC was 1.000.

Conclusion

This AI-driven 3D surface topography demonstrated high validity for non-radiographic Cobb angle prediction, particularly in mild-to-moderate AIS. It supports safer, more frequent, home-based monitoring, though refinements are needed for severe curves and patients with a higher BMI.