Table 3 Evaluative studies of the computer vision-based analysis system for scoliosis screening
Author | Patients (n) | Computer vision | Comparison/evaluation | Results | Year |
|---|---|---|---|---|---|
Yıldırım et al. [17] | 42 | Hand-held 3D scanner with tablet | Conventional ultrasound scoliosis diagnosis system/point-to-point matching, correlational analysis | Root mean square, correlation (rmax = 0.92, rmin = 0.47 in standing posture) | 2021 |
Lai et al. [37] | 19 | 3D ultrasound imaging system | Commercial 3D ultrasound imaging system/absolute dataset difference | Absolute difference between the two data sets (2.9° ± 1.8°) | 2021 |
Zhang et al. [34] | 367 | Built-in smartphone camera | Plain X-ray images/deep learning-based vertebral landmark detection and difference analysis | Average L2 error (2.8 pixels), Recall (0.99) | 2021 |
Cho et al. [36] | 629 | U-net segmentation, binary mask | Plain X-ray images/Cobb angle measurement difference | Matching score (0.821), mean absolute error of 8.055° for Cobb angle | 2020 |
Mishra et al. [33] | 22 | 3D vision with surface topography | Plain X-ray images/topographical differences | Standard deviation (± 3.4) | 2020 |